Building SimCity

Game Histories

edited by Henry Lowood and Raiford Guins

Debugging Game History: A Critical Lexicon, edited by Henry Lowood and Raiford Guins, 2016

Zones of Control: Perspectives on Wargaming, edited by Pat Harrigan and Matthew Kirschenbaum, 2016

Gaming the Iron Curtain: How Teenagers and Amateurs in Communist Czechoslovakia Claimed the Medium of Computer Games, Jaroslav Švelch, 2018

The Elusive Shift: How Role-Playing Games Forged Their Identity, Jon Peterson, 2020

Homebrew Gaming and the Beginnings of Vernacular Digitality, Melanie Swalwell, 2021

Game Wizards: The Epic Battle for Dungeons & Dragons, Jon Peterson, 2021

Arcade Britannia: A Social History of the British Amusement Arcade, Alan Meades, 2022

Building SimCity: How to Put the World in a Machine, Chaim Gingold, 2024

Building SimCity

How to Put the World in a Machine

Chaim Gingold

foreword by Janet H. Murray

The MIT Press

Cambridge, Massachusetts

London, England

© 2024 Chaim Gingold

All rights reserved. No part of this book may be used to train artificial intelligence systems or reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.

The MIT Press would like to thank the anonymous peer reviewers who provided comments on drafts of this book. The generous work of academic experts is essential for establishing the authority and quality of our publications. We acknowledge with gratitude the contributions of these otherwise uncredited readers.

This book was set in ITC Stone Serif Std and ITC Stone Sans Std by New Best-set Typesetters Ltd.

Library of Congress Cataloging-in-Publication Data

Names: Gingold, Chaim, author.

Title: Building SimCity : how to put the world in a machine / Chaim Gingold ; foreword by Janet H. Murray.

Description: Cambridge, Massachusetts : The MIT Press, 2024. | Series: Game histories | Includes bibliographical references and index.

Identifiers: LCCN 2023015471 (print) | LCCN 2023015472 (ebook) | ISBN 9780262547482 (paperback) | ISBN 9780262377584 (epub) | ISBN 9780262377591 (pdf)

Subjects: LCSH: SimCity (Computer game) | City planning—Simulation games. | City planning—Computer simulation.

Classification: LCC HT165.5 .G56 2024 (print) | LCC HT165.5 (ebook) | DDC 307.1/2160113—dc23/eng/20230530

LC record available at https://lccn.loc.gov/2023015471

LC ebook record available at https://lccn.loc.gov/2023015472

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For Emily, Tali, and Lev

“Then, open up,” she said to the god, who had in sport taken the form of a human child, and he opened his mouth.

Then she saw in his mouth the whole universe, with the far corners of the sky, and the wind, and lightning, and the orb of the earth with its mountains and oceans, and the moon and stars, and space itself; and she saw her own village and herself. She became frightened and confused, thinking, Is this a dream or an illusion fabricated by God? Or is it a delusion in my own mind? Or is it a portent of the powers of this little boy, my son? For God’s power of delusion inspires in me such false beliefs as “I exist,” “This is my husband,” “This is my son.” When she had come to understand true reality in this way, God spread his magic illusion in the form of maternal love. Instantly Yashodha lost her memory of what had occurred. She took her son on her lap and was as she had been before, but her heart was flooded with even greater love for God, whom she regarded as her son.

—Myth of Krishna and Yashodha¹

We are almost certainly living in a computer simulation.

—Philosopher²

Software is eating the world.

—Venture capitalist³

The secret of the universal machine is that it can imitate. . . . The universal machine is remarkably human.

—Scientist⁴

And are not we as well, if you examine us physically, mechanistically, statistically, and meticulously, nothing but the minuscule capering of electron clouds?

—Science fiction author⁵

Series Foreword

What might histories of games tell us not only about the games themselves but also about the people who play and design them? We think that the most interesting answers to this question will have two characteristics. First, the authors of game histories who tell us the most about games will ask big questions. For example, how do game play and design change? In what ways is such change inflected by societal, cultural, and other factors? How do games change when they move from one cultural or historical context to another? These kinds of questions forge connections to other areas of game studies, as well as to history, cultural studies, and technology studies.

The second characteristic we seek in game-changing histories is a wide-ranging mix of qualities partially described by terms such as diversity, inclusiveness, and irony. Histories with these qualities deliver interplay of intentions, users, technologies, materials, places, and markets. Asking big questions and answering them in creative and astute ways strikes us as the best way to reach the goal of not an isolated, general history of games but rather of a body of game histories that will connect game studies to scholarship in a wide array of fields. The first step, of course, is producing those histories.

Game Histories is a series of books that we hope will provide a home—or maybe a launch pad—for the growing international research community whose interest in game history rightly exceeds the celebratory and descriptive. In a line, the aim of the series is to help actualize critical historical study of games. Books in this series will exhibit acute attention to historiography and historical methodologies, while the series as a whole will encompass the wide-ranging subject matter we consider crucial for the relevance of historical game studies. We envisage an active series with output that will reshape how electronic and other kinds of games are understood, taught, and researched, as well as broaden the appeal of games for the allied fields such as history of computing, history of science and technology, design history, design culture, material culture studies, cultural and social history, media history, new media studies, and science and technology studies.

The Game Histories series will welcome but not be limited to contributions in the following areas:

• Multidisciplinary methodological and theoretical approaches to the historical study of games.

• Social and cultural histories of play, people, places, and institutions of gaming.

• Epochal and contextual studies of significant periods influential to and formative of games and game history.

• Historical biography of key actors instrumental in game design, development, technology, and industry.

• Games and legal history.

• Global political economy and the games industry (including indie games).

• Histories of technologies pertinent to the study of games.

• Histories of the intersections of games and other media, including such topics as game art, games and cinema, and games and literature.

• Game preservation, exhibition, and documentation, including the place of museums, libraries, and collectors in preparing game history.

• Material histories of game artifacts and ephemera.

Henry Lowood, Stanford University

Raiford Guins, Indiana University Bloomington

Foreword

Janet H. Murray

SimCity, designed by Will Wright, was an oddity on its release in 1989, a simulation game that combined the strategic complexity of a board game with the visual responsiveness of an arcade game. The scenario was taken not from the popular videogame tropes of questing, space wars, or fast-moving vehicles but from the significantly less dramatic world of urban planning. It was a highly spatial game but without a leaping Mario or a chomping Pac-Man character for the player to guide. It was a highly complex puzzle, but without the time pressure of a Tetris session or the rewarding ending of an adventure game. It expected players to become immersed in a world that combined tax assessments and industrial zoning with monsters and natural disasters. And it did this all so skillfully that it captivated millions of players in a decades-long successful run, spinning off an even more spectacularly successful and equally category-defying game, The Sims (2000).

The problem with looking at any successfully designed and highly innovative artifact is that once it is realized and widely accepted, it seems inevitable. It is hard to look at it as something that might never have existed, whose every component is the result of specific design choices within a specific historical and social context. It is particularly hard to look at videogames in this way, since they have achieved the status of a mass art and entertainment form only within the past fifty years, and serious criticism and academic discourse about them is about half that age. Looking back on its release from the third decade of the twenty-first century, we can see Will Wright’s SimCity as a classic, milestone innovation in the cultural history of digital media. Building SimCity offers us a new way of formulating and contextualizing that achievement and for thinking about videogames in general.

The landscape of videogames is constantly changing, and our means of mapping that landscape are also rapidly proliferating. The current richly interdisciplinary field of game studies has antecedents in the study of predigital games and draws on many diverse sources, including empirical approaches from the social sciences, theoretical frameworks from the humanities, and design methodologies from engineering, industrial design, and educational computing. As new consumer platforms emerge, they lead to new proto-genres and new critical branches. Videogame making is itself multidisciplinary and collaborative, drawing on craft practices handed down by apprenticeship and often practiced in the context of particular corporate cultures. And since videogames are instantiated only when they are played, videogame players and fans are themselves the subject of multiple analytical approaches. Furthermore, as media genres change, new modalities have arisen for documenting game design and gameplay including critical video compilations and ephemeral streaming play sessions.

Building SimCity offers a unique contribution to this energized and divergent discourse: a deep dive into a single masterwork. SimCity is highly worthy of attention, as is its creator, Will Wright, who is unusually self-reflexive and generous in sharing his creative practices and wide-ranging intellectual curiosities. Chaim Gingold brings to his subject a happy combination of scholarly rigor, technical virtuosity, and industry experience. He is equally engaging and lucid whether he is describing postwar systems theory, the illusion of life created by cellular automata, Silicon Valley investment fever, the delicate give-and-take of game development teams, or the surprisingly relevant intricacies of character representation in the Commodore 64. Gingold’s approach is grounded in the material reality of the code itself, while seeing that code not as an isolated technical structure but as a specific social practice reflecting a deep intellectual and political history and a particular place and time. He understands the code, and the mental models behind the code, and the models behind those models, and he shepherds us through these multiple layers of abstraction with wit and clarity.

Gingold begins by examining the other ways in which cities have been modeled, most notably in the framework of learner-centered education, as exemplified by the creative teaching of Doreen Gehry Nelson. He then introduces us to the extension of those role-playing, block-building processes in the exploratory computer-based microworlds of Seymour Papert and the educational philosophy behind the Xerox PARC innovators led by Alan Kay. Most important, Gingold revisits the dichotomy between analog and digital, reminding us that digital models are also analogies of the real world. This is a crucial insight, since much of the misunderstanding of computation by both laypeople and computer scientists can be traced to a naive sense of computational models as identical with the world they represent. This belief is currently so entrenched that it is possible for philosophers and computer scientists to discuss transporting consciousness into a machine and substituting the virtual reality of videogames for the actuality of the physical world. So it is useful to be reminded of Vannevar Bush’s mechanical computers as the forerunners of the digital machines. Though we can no longer watch the gears turn once computation moves to electrical circuits, the structures of representation are still there, and they are still abstractions of the domains they represent.

Gingold guides us through the two abstract representations—system dynamics and cellular automata—that Will Wright drew on in structuring SimCity. These chapters present us with the strengths and limitations of both, so that we see them as products of their time, with particular cultural and social connotations, as well as understanding their appeal as resources for game design. Jay Forrester’s powerful but reductive understanding of feedback mechanisms tapped into a midcentury desire to understand complex behavior of social organizations with the same precision as we understand physical processes, a way of thinking that was a perfect match for the tools of the new discipline of computer science. The mathematician John Conway’s Game of Life (1970) demonstrated how a simple behavior built into every square on a two-dimensional grid could produce emergent patterns that resembled skittering insects or other seemingly animated phenomena. It was Will Wright’s inspiration to combine these strategies in service of his propensity as a game designer to be more playful and constructive, to lose himself in building imaginary worlds rather than in setting them up for the player to bomb. Drawing on Forrester and Conway, Wright put playful world building at the center of the experience, offering a player an intoxicating control over a complex procedural mechanism, in exchange for a greater cognitive and imaginative investment.

We have many models of masterwork studies in other fields like literature and film, but I know of no other attempt to describe a videogame as a cultural object with the kind of Geertzian thick description offered here. Each chapter builds up another layer of context without losing sight of the individual creator, Will Wright, whose unique curiosities, tastes, programming processes, and intellectual ambitions shaped his interpretation of urban planning into this particular playful object. The chapters detailing the corporate history of the game are particularly valuable, since Gingold has had privileged access to Wright and to his partners and firsthand experience of corporate and independent game design. These sections of the narrative are similar to descriptions of American film production at various stages of industry organization in Hollywood, and they offer a new way of thinking about the interplay between auteur, collaborating creators, and industry demands. It is a particular strength of this narrative that it is full of key insider information gleaned through candid interviews, without the distraction of the usual tropes of larger than life personalities and Silicon Valley success porn. Gingold’s account of the odyssey of Will Wright through the games industry of the late 1980s and 1990s is specific and informative, never losing focus on the central drama of how a particular game gets made.

The heart of Gingold’s in-depth analysis is his meticulous documentation of SimCity as an interactive digital artifact. With ingenuity and precision, through economical prose and elegant diagrams, he takes us inside the black box and makes the intricately interconnected gears of the simulation visible and understandable, even for a nonprogramming reader. To play SimCity is to be enchanted by its responsiveness and seeming organic processes. The city grows in ways that reflect the player’s actions, but also according to inherent rules of growth. Gingold’s analysis makes clear the multiple layers of computation that produce this result in collaboration with the player’s carefully directed actions. Gingold’s designer’s eye identifies key details, from the ways in which the code is optimized in its bit-by-bit representation of each cell, to the role of animations in instantiating pollution and traffic. He notes the clever inclusion of mechanisms of decay and the way in which the rules for growth are far from neutral but represent a particular theory of how cities should develop. He traces how the code sets up two separate representations of time, as well as multiple pacing options, and how it all works together to reinforce the player’s sense of manipulating a coherent and complex model. In short he displays the artistry behind SimCity at multiple levels of abstraction, in the same way an art critic might analyze brush strokes, pigmentation, light sources, and perspective in a masterpiece of painting. We see the technique; we learn to distinguish fortunate pieces of happenstance from hard-earned products of exquisite craft; we experience the reduction of magic into its manifold, demystified components; and then we step back, behold the resulting whole, and marvel all over again at the irreducible illusory experience. In the case of SimCity, we see the workings of the gears, and then we zoom out and see the living city, full of possibilities, awaiting our playful engagement.

Gingold’s reading of SimCity is exhaustive and will undoubtedly prove of lasting value in understanding the provenance and structure of the game and in thinking about the source of its particular powers of engagement. But it is not definitive. As with any masterwork, there is always room for other interpretations and contextualizations. Indeed the groundbreaking detail with which Gingold has made the code visible should be tremendously helpful to other scholars and critics in understanding and writing about SimCity from other perspectives.

In addition to the insight it provides into its subject, Building SimCity should prove useful as a model of an in-depth description of a digital, interactive art work. Gingold’s lucid, code-free representation of a complex computational object, his historical awareness of traditions of computational representation, and his balancing of auteur perspective with recognition of the give and take of teams and organizations provide a model for the interpretation of videogames in particular and of digital artifacts in general.

But I have an even grander hope for the usefulness of this book. In an age of global connectivity and existential threats to human life, computer simulations can help us model, interpret, and intervene in complex patterns of causality. We need to get better at making simulations, at playing with them, and at seeing through them to their underlying assumptions. And yet we are mystified by simulation. We mistake simulations for reality, and some who should know better would characterize reality itself as a simulation. Because we cannot see the gears beneath the surface, we cannot understand how computational objects work. One of the great dangers of our moment is this black box view of the very artifacts best suited to helping us understand the world. The solution to this confusion is to get more people more skilled at understanding the design of computational simulations. We need to create more transparent simulations and engage in more concrete conversations about how they work and what they hide, as well as what they make visible.

Will Wright’s SimCity can be seen as an important step forward in the collective cognitive task of understanding complex systems through interactive simulations. Playing with SimCity we learn to connect our actions with the results on the map, and to deduce the hidden rules from observing the overt behaviors of the system, and by extension, we get a bit smarter at seeing ourselves as part of the many complex systems that shape our lives. Gingold’s analysis is a fitting complement to Wright’s achievement: he makes the invisible computational components of this magically responsive world as concrete as the moving parts of a physical mechanism, revealing not just their cleverly crafted functionality but their richly textured historical and social lineages. My hope is that readers of this book will return with deeper insight to the enduring playful magic of Will Wright’s SimCity and be newly inspired—as designers, scholars, and players—by the untapped humane potential of computational simulations.

Acknowledgments

It fills me with warmth and gratitude to think of the people who helped make this book.

The intellectual and spiritual fingerprints of my PhD advisers—Michael Mateas, Noah Wardrip-Fruin, Nancy N. Chen, Henry Lowood, and Janet H. Murray—touch every aspect of this book, a list which must also include Sha Xin Wei and Will Wright, both of whom also served on my MS committee (alongside Michael and Janet). All continue to be a wellspring of inspiration.

This book is indebted to Noah J. Springer, my editor at MIT Press, with whom it has been a pleasure to work, as well as Doug Sery, who helped get it going. Elizabeth Agresta gracefully shepherded the book through production. Henry Lowood and Ray Guins, the Game Histories series editors, provided indispensable encouragement and support. Henry, your confidence, enthusiasm, and advice have meant so much to me. My editors, Richard Moss and Mandy Modell, have been wonderful teachers and intellectual playmates whose thoughtful reading and gentle nudges have profoundly improved the quality of my writing and thought. I am deeply grateful to Richard for helping me raise and meet my expectations for the project and to Mandy for nourishing the book’s scholarly arguments and technical explanations.

Jeff Braun’s open-hearted reminisces and reflections, alongside those of many other respondents and interview subjects—Bruce Abell, Don Bayless, Jon Boorstin, Eric Bowman, Walter Bender, Michael Bremer, Bill Budge, Brian Conrad, Claire Curtin, Bob Derber, Jamie Doornbos, Cliff Ellis, Mick Foley, Ken Forbus, Jay Forrester, Fred Gerson, Robin Harper, Don Hopkins, Bill Janeway, Bruce Joffe, Ken Karakotsios, Alan Kay, Aki Kodama, Shintaro Kono, Henry Kressel, Jim Mackraz, John Maloney, Bobbi (Robina) Mapstone, Jenny Martin, Virginia McArthur, Justin McCormick, Avram Miller, Shigeru Miyamoto, Doreen Gehry Nelson, Radia Perlman, Ocean Quigley, A. J. Redmer, Joe Scirica, Alvy Ray Smith, Cynthia Solomon, Rachel Strickland, Will Wright, and some anonymous sources—breathed life and substance into this project. The kindness and intelligence of the Maxis community, to whom this project is indebted, has never ceased to impress and stands as a testament to Will and Jeff’s character and integrity.

Many friends and colleagues took the time to read drafts, talk shop, and inject new materials into my life. I especially thank Richard Moss, Mandy Modell, Janet Murray, Alan Kay, anonymous reviewers, and my dissertation committee for vital guidance and feedback. Sam Arbesman, Marc ten Bosch, Shawn Douglas, Gonzalo Frasca, Jesper Juul, Luke Iannini, David Kanaga, John Murphy, Doreen Nelson, Yoshiki Ohshima, Alvy Ray Smith, and Brad Smith provided thoughtful playtesting and helpful suggestions. Jeff Braun, Robert Ochshorn, Josh Leong, Eric Bowman, Jim Mackraz, Gauri Shodhan, Shalin Shodhan, and Kelsey Williams read drafts, checked information, and offered encouragement. Jimmy Maher, Stefan Helmreich, and Phil Salvador helped research some historical miscellanies. Dedre Gentner, Bruce Clarke, Gregg Zachary, and Larry Owens generously shared references, recommendations, and encouragement. I am profoundly grateful to Don Hopkins, Jon-Paul Dyson, Bart Simon, Fred Turner, Bret Victor, Sha Xin Wei, and Emily Harris for exchanges that propelled and shaped this project, and to Topiary Landberg, David McConville, Ander Monson, Chris Reath, and Jen Pahlka for manifesting indispensable connections. This book is richer thanks to the creative and intellectual companionship of Lea Redmond, Alex Rosmarin, Eric Kaltman, May-Li Khoe, and Petri Purho.

Jio Javier transcribed many interviews, as did a few others whose names I do not know. Kendra H. Millis prepared the index. Matt Peckham of Nintendo of America energetically facilitated exchanges with Nintendo Co., Ltd. and then some. At the Santa Fe Institute, Caroline Seigel patiently fielded numerous queries.

Thank you, from the bottom of my heart, to the many image makers, archivists, institutions, and collectors whose efforts and generosity helped fill these pages with so many wonderful visuals, including Electronic Arts, Jon Boorstin, Andy Freeberg, Ed Kashi, Alan Kay, Chris Kohler, MITRE, Nintendo, Kevin W. Ng, and Cynthia Solomon.

I am indebted to Doreen Nelson for hours of spirited and far-ranging conversation and for inviting me to observe a teacher training workshop at Pasadena Art Center. You are an inspiration, and, as with many of the other fascinating people this project led me to, it has been a pleasure to get to know you. (And thank you, Claire Curtin, for introducing me to Doreen’s work.)

The luminous color photographs of Doreen Nelson’s collaborators and their effervescent creativity appear here thanks to the efforts and generosity of multiple institutions and individuals. I am able to thank, by name, a mere handful of people. Alan Kay gave permission to use photographs from his 1991 Scientific American article (and provided generous context). The bulk of the photographs were taken by Charles Eames, Grant C. Taylor, and other photographers working under the aegis of the Eames Office, and come from UCLA Library Special Collections. Kelly Besser prepared the Doreen Nelson collection and patiently answered my questions; Molly Haigh oversaw image digitization. At the Office of Charles and Ray Eames, Kelsey Williams gave invaluable counsel, assistance, and encouragement; her colleague Genevieve Fong handled permissions and licensing. At the Library of Congress, Ryan Brubacher provided my first tantalizing glimpse into these photographic treasure troves. This book, regrettably, is but an amuse-bouche.

Multiple institutions supported the research that informed this book. The University of California, Santa Cruz, the Computer Science Department, and Michael Mateas and Noah Wardrip-Fruin’s Expressive Intelligence Studio all provided the initial sponsorship for my dissertation research (in 2011). Bret Victor and Alan Kay brought me into the orbit of SAP’s Communications Design Group, which provided further funding. Y Combinator Research provided support during the final month of my PhD (in 2016). I am profoundly grateful to my clients and creative playmates. Whether they know it or not, they, along with my family, helped make this book possible.

My sincerest apologies to all those whose names and contributions I have overlooked.

To my countless teachers and mentors: thank you from the bottom of my heart. I am grateful to my friends and family, especially my parents, for scaffolding a life full of play, creativity, adventure, warmth, and learning. My parents, of course, are my original teachers; they taught my siblings and me to see the world with wide-eyed wonder, revel in its theatricality and tangibility, and prize unfamiliar perspectives.

I cannot single out every influential teacher, so I’ll simply add, with delight, that it was while writing this book that I began to understand the great gift of Lynn Pickles, my beloved first-grade teacher, her approach, and her influence on me in a whole new light. She had, of course, employed ideas from the Project Approach, Maria Montessori, Reggio Emilia, and constructivism. Glenn Gingold, I miss you. A special thanks to my many Maxis/EA teacher-collaborators, especially Will Wright, Jason Shankel, and Lisa Dennis.

I am fortunate and honored to count Janet Murray as a lifelong mentor and friend. Thank you, Janet, for helping me become the designer, scholar, and person I am today. You’ve coached and guided at critical moments, read interminable drafts, and, as always, helped me conceptualize the project as a whole and think through many details. This book, and my career, are a testament to your intellectual companionship and, above all, your dedication to teaching multiple generations of scholars and designers; the world is surely a better place for your efforts.

Emily, Tali, and Lev, I dedicate this book to you. Emily, you—alongside our growing family—continue to shape who I am becoming, and your ideas and enthusiasms, as you know, have infused this book’s entire outlook; of course we talked Vygotsky on our first date. Thank you for your unflagging encouragement. To Tali and Lev, our beautiful children, I hope you derive as much as joy as we do from learning about the world. May you sustain and play in a good one.

Introduction: SimCity’s Mystique

Maxis was in turmoil. Despite offering an ever-broadening and bewildering selection of titles, their survival depended on SimCity, but sales were drying up. The last release, published in 1993, was now three years old, and development of a new version, SimCity 3000, was beset by countless difficulties that now posed an existential threat to the entire company.

Jeff Braun, the firm’s CEO and cofounder, was haunted by a feeling that the powerful venture capitalists that had taken Maxis public had undermined him and his company by killing a hard-won financing deal. As Maxis’s finances and social fabric came unglued—and its stock price continued its ominous descent—the board of directors, which Braun chaired, ousted him as CEO. To help make its quarterly numbers, Maxis had accelerated development of multiple titles. Now, unnoticed by quality assurance testers, SimCopter shipped with an unsanctioned Easter egg (a hidden feature) that sometimes put Speedo-clad studs onto its virtual streets, kissing everything and everyone in sight. In the era of “don’t ask, don’t tell,” this amounted to a full-fledged PR fiasco and costly product recall.¹

Will Wright, Maxis’s cofounder and SimCity’s creator, didn’t want to get involved with SimCity 3000. He remained committed to a dollhouse simulator that, its recent cancellation notwithstanding, continued to limp along, as it had for many years. When Maxis’s vice president of product development announced the obvious—that SimCity 3000 was behind schedule—the new CEO fired him.

In 1997, Maxis finally succumbed to wound and dysfunction and sold itself to Electronic Arts, an ascending giant of videogames. Eight years earlier, Maxis had released SimCity, a singular title best known for bringing urban planning to the masses in the form of a beguiling simulation toy. Electronic Arts was best known for testosterone-spiked sports games, published with metronomic efficiency.

It was an odd match. And yet, in retrospect, it is unclear whether Maxis’s disintegration and acquisition is cause for mourning or celebration. Without Maxis, SimCity is unlikely to have reached maturity; it was too strange for Wright’s publisher. And no company besides Maxis would have incubated a dollhouse simulator that took almost ten years to develop and would go on, against all bettors, to earn billions as The Sims. Maxis, now a juggernaut’s lunch, had been a unique voice in an increasingly homogeneous industry.

But if the Maxis train wreck hadn’t finally ground to a halt, The Sims might not exist. Maxis tried to starve and squash it but was too disordered to stamp it out, let alone see the project through. And while Electronic Arts also struggled with the easygoing yet puckish Wright, they ultimately shipped The Sims.

Why did Maxis, which had so brilliantly recognized Wright’s prodigious talents and SimCity’s unusual promise, fail so miserably with The Sims?

Approach

SimCity is a portal onto a tiny simulated city. This portal also opens into the teeming complexities of simulation: the potency of imagination and the human drama of those, like Maxis, who try to realize their dreams in software. Shape-shifting machines, computers are able to simulate whatever we wish. Their myriad forms reflect manifold histories and desires. Urban planners, policymakers, engineers, scientists, mathematicians, spies, weapons designers, military strategists, generals, investors, entrepreneurs, artists, players, tinkerers, entertainers, educators, activists, and more have all given form to the shape-shifting machine. These transformations are reciprocal; those who summon the manifold forms of computing often find themselves forever altered, as did Maxis.²

This book uses SimCity to ask a question: How do people put the world in a machine? The question is multifaceted; so are the aims of this book.

One facet highlights simulation as culture. Whether in the lab or living room, simulations are imaginative cultural practices. Although scientists and engineers have long employed imagination as part of their simulation work, it was Wright’s conscious appreciation of the role of imagination that enabled him to remix so many different simulation practices and turn them to explicitly playful and expressive ends. SimCity’s hybridity testifies to another aspect of simulation as culture: its collectivity. This book shows how simulation, like any other cultural endeavor, is a collective activity in which practices arise, evolve, and are passed around. By tracing SimCity’s genealogy, Building SimCity follows the development of multiple simulation traditions, showing how sociocultural influences, including economic, intellectual, and technical factors, shape simulation practice.

Another facet concerns simulation as cognition. This book proposes a framework for thinking about how simulations (and other dynamic representations) transform how we think, feel, and act. Building SimCity argues that all simulations—whether realized through role-play, electromechanical analogs, digital computers, or other materialities—enact dynamic analogies with certain representational affordances. This theory is used to interpret SimCity and its forebears and is developed with the aid of diverse examples, especially the epochal analogs of Vannevar Bush (whose formative pre-Memex work has received surprisingly scant attention from digital media scholars). This framing challenges the conventional analog-digital dichotomy; Building SimCity argues that digital computers are in fact a species of analog device. An overarching aim of this book is to help digital media designers zoom out and see their work as kin to practices that extend back into the history of computing and beyond, across a dizzying variety of materials and cultural forms. From this vantage, for example, tangibles and (more) embodied interaction—a likely future of human-computer interfaces—aren’t new arrivals in the house of computing but old friends that helped lay its foundation.

The last facet regards software. Building SimCity uses software to follow the manifold ways in which people shape computing toward human ends. The major aim here is to advance methods for studying software through an expansive yet detailed case study.

How should software be analyzed? The question is pertinent to legislators (squinting at corporate “algorithms”), designers and engineers (who maintain and build software), scholars (who might study games or the history of computing), and more. Software impinges on practically every aspect of human (and nonhuman) life. To rephrase a prominent Silicon Valley venture capitalist, software has eaten the world.³ The structure and interpretation of software are, as a result, of ever-widening importance.

From this book’s vantage, software is where cultures intermesh with computing machines. Software directs the computer’s protean ability to simulate, or enact, any other symbolic machine. Software, as a result, reflects nothing so much as people: their histories and dreams, intentions and fears, worldviews and milieus. This perspective follows from approaching software itself as a form of simulation—an idea that follows directly from Alan Turing’s seminal work.⁴

Building SimCity analyzes software as media, material, and sociocultural phenomena. As media, software is analyzed as harboring cognitive, ludic, and experiential dimensions.⁵ As for software’s material elements, code, algorithms, machines, schema, and math are all fair game, and diagrams help make them legible, even to nontechnical readers. As far as critique is concerned, this book is more concerned with SimCity’s overarching simulation approach—how agency, subjectivity, and ideology are enacted—rather than the political fictions it reproduces.

To follow software’s sociocultural dimensions, a number of techniques are used. Close attention is paid to the stories (or discursive practices) people tell about software. This includes books, manuals, box designs, presentations, graphics, and code. These co-constitute software.⁶ A software program’s broader sociocultural context is also illuminated through biography, institutional setting, and history. Building SimCity is particularly interested in how software practices develop across time and traces the genealogy of ideas and artifacts alike. This book sees learning as a sociocultural process; we learn through apprenticeship,⁷ a process that simulation would seem to augment, or at least inflect. Close attention is paid to the ways that simulation (and thus software) artifacts like SimCity help reproduce simulation culture. Finally, Building SimCity examines how simulation artifacts are taken up by different communities and how simulation practitioners, by functioning as interdisciplinarians, broker relationships between different social groups and intellectual frameworks.⁸

The SimCity Network

Neither videogame nor serious simulation, SimCity has all the hallmarks of an “evocative object,” which Sherry Turkle defines as “an object that fascinates, disturbs equanimity, and precipitates thought” on account of how it stands “betwixt and between” categories.⁹ SimCity’s mystique, in other words, emanated not just from Wright’s technical artistry but the diverse traditions he synthesized.

By evocatively straddling multiple worlds, SimCity seeded what Fred Turner calls a “network forum,” a notional place where members of multiple communities exchange ideas and legitimacy, imagine new identities, and synthesize new intellectual frameworks and social networks.¹⁰ The SimCity network forum’s most outwardly visible manifestations were SimCity, Maxis, and, of course, Wright himself, who rose to emblematic status within multiple communities by synthesizing and cross-pollinating contributions from each. Functioning as a “network celebrity,”¹¹ he brokered communication between the worlds of venture capital, science, education, business, public policy, and videogames through the inherently supple contact languages of simulation, computing, and play.¹² The flexibility of the “Sim-” prefix—which could precede practically anything—epitomizes how Maxis deployed the language of simulation to bridge far-flung communities.

SimCity must be understood in reference to the diverse traditions that informed it. While SimCity’s genesis is now the stuff of game industry legend, a quick recap helps explain SimCity’s hybridity, Maxis’s predicament, and Building SimCity’s overarching narrative.

In 1984, Wright released an action game, Raid on Bungeling Bay. Afterward, he continued to tinker with its custom level editor, gradually transforming it into something he called City Planner. As the project progressed, Wright read dozens of books and synthesized a wide range of influences. Raid’s landscape morphed into SimCity’s, and its animated elements—like boats, tanks, and missiles—became a boat, a train, traffic, and, of course, explosions. To put a city in a machine, Wright turned to the work of Jay Forrester. Although his Urban Dynamics (1969) left unexamined the suffocating racism animating “the crisis of the cities” that inspired it, Forrester’s book provided Wright with a blueprint for translating cities into a socioeconomic computer model. But Forrester’s model wasn’t much more than a spreadsheet; Wright needed more approaches to bring his simulated cities to life. For rich spatial dynamics well suited to fires and floods, Wright incorporated another simulation technique, cellular automata, which originated in the 1940s with an attempt to create machine models of biological organisms.

Why would someone play with an urban planning simulator? Wright enjoyed it as a laboratory for learning about urban theory, but this was unlikely to appeal to others. The pleasure he took in designing cities, however, just might. Wright took design cues from Pinball Construction Set, the hit 1982 “software toy” that enabled players to design, play, and share simulated pinball tables through its precocious graphical user interface.

Although Brøderbund, which had published Raid, expressed interest in Wright’s new project, they were unable to agree on its final form. Brøderbund wanted something more game-like; Wright did not. About a year later, Wright met Braun, an entrepreneur who quickly grasped City Planner’s potential and shepherded it to market as SimCity.

SimCity inherited the luscious immersion of a videogame as well as the accessibility of the graphical user interface (then a cutting-edge innovation). Urban Dynamics lent realism to SimCity’s economic model as well as a playfully absorbing instability. Cellular automata, which had recently been taken up by scientists studying dynamical systems theory and artificial life, imbued SimCity with the radiant halo of vanguard science.

Members of the Whole Earth network,¹³ alongside eminent scientists, cultivated relationships with Wright and Maxis—they shared a common history and interest in computer simulation, complex systems, and artificial life. Urban planners cheered SimCity’s accuracy and playability, which qualified it as realistic to the gaming community, which then bestowed their highest honors on it. Nintendo licensed SimCity for an in-house adaptation, underscoring its appeal and accessibility. Journalists intimate with the history of computing seized on SimCity as a spellbinding entry point to the mysteries of simulation, which helped players imagine that they were engaged with rarified simulation practices. Mainstream press outlets, like Newsweek and the New York Times, took note. Inspired by SimCity’s critical and commercial success, scientists at the Santa Fe Institute imagined their work as having a broader reach. Wright, in turn, came to see himself as a science popularizer. All this led quite naturally to requests from enterprise firms for bespoke simulations like SimRefinery, for Chevron. Consequently, venture capitalists envisioned Maxis as a future leader in scientific, enterprise, and educational simulation, and they invested. The SimCity network took root as multiple communities buttressed one another’s interpretations of SimCity and engagements with Maxis.

But the powerful currents that propelled Maxis’s growth had a less benevolent side; they ripped it apart. SimCity’s multifaceted sparkle led Maxis astray, and they floundered at the crossroads, unsure of who they were and where they were going. The SimCity network forum resolved into a taut and irreconcilable web of contradictions. Tension around Wright’s creative process mounted, embattling his simulated dollhouse project. “I had the golden goose and I’m looking for other golden gooses,” a laughing but wistful Braun told me. Tempted by greed, torn by indecision, and trampled by rapid technological and market change, Maxis unraveled. SimCity’s ambiguous potentialities built Maxis and then pulled it apart.

What is the legacy of SimCity and the network forum that grew up around it? This network produced SimCity, Maxis, numerous Sim titles, and Wright the auteur and celebrity. SimCity reproduced authoritarian notions of governance and city planning, simpleminded ideas about crime, policing, and taxation, and a downright colonial and extractive outlook—unexceptional shortcomings for a computer game. While SimCity helped naturalize the idea of simulations and computers as opaque black boxes, it also disseminated the idea that complex systems were worth playing and understanding, and that computer simulation could be for everybody. Like Tetris, SimCity enlarged the audience for computerized entertainment by expanding our sense of what it might be. And SimCity accomplished this in large part by promulgating the idea of the open-ended sandbox—now an ascendant genre of digital entertainment.¹⁴

Positionality

I approach this project as both theorist and practitioner. My formal training, while multidisciplinary, resides in computer science and digital media design. My professional design education began in earnest at Electronic Arts (EA), where I apprenticed with Wright not long after The Sims’s launch.

I grew up in the 1980s in a college town in West Virginia, where my father’s work as a mathematics professor brought my family into contact with a wide range of computing cultures. Computers were everything from Nintendo and Apple to Unix and NASA; they were science and art, programming and play, product and pastime. They were a social activity, pursued with my brother, friends, and academicians through a lively exchange of floppy disks, enthusiasm, and expertise. Professionally, my lucky break came at Georgia Tech while pursuing a master’s degree. My adviser, Janet Murray, received an email from an EA/Maxis recruiter, and I ended up interning with Wright on Spore (alternate working title: “SimEverything”).

The Sims, released in 2000, was a recent blockbuster success—its expansion packs practically printed money—and EA was eager to invest in both The Sims and Wright. As usual, Wright was realizing outlandish ideas on multiple fronts. Outside EA, he had founded the Stupid Fun Club (in the run-up to The Sims’s launch), which sought Hollywood partnerships for its inspired hijinks, such as unleashing eccentric robots on an unwary public.¹⁵ Inside EA/Maxis, it was all hands on deck for the well-intentioned but ill-fated The Sims Online (TSO). Sequel and expansion pack teams for The Sims steamed alongside, but not Wright’s tiny but seasoned Spore concept team, which had been reassigned to the TSO flagship. Another intern and I were stationed just outside Wright’s office, within arm’s reach of his Elvis shrine, and basically inside TSO’s nerve center, where strained executives tried to steer that mammoth undertaking. I was, as a result, Wright’s sole full-time Spore developer and a regular respite for him from TSO fever.¹⁶

I had by then met many brilliant professors with eclectic interests, but Wright’s bookshelves and belongings boggled me. My job was to make software toys for, as I saw it, the world’s most talented toy maker. I was in Wonka’s Chocolate Factory. To my delight, Wright opened a closet, dusted off some old computers, and we plugged them in. He proudly showed me SimEarth code, and I pored over the source for SimCity 2000, SimAnt, and more, blueprints of childhood playthings that had once utterly transformed my sense of the world. Cities and planets were like living things; an ant colony was a delicate, scruffily robust mechanism. As for Wright, he was apprenticing me into a way of thinking: this stuff was more than code; it was a master class in design. Primitive prototypes of The Sims, for instance, vividly illustrated the exploratory power of prototyping, which I was now doing for Spore. The machines were littered with dead ends: design documents and software, some of it quite elaborate, abandoned long ago.¹⁷ Strange threads connected projects: the code for SimCity 2000, released in 1993, clearly referenced (what became) The Sims. It all suggested a complex social and creative history. Later, when I returned to EA as a Spore designer, I participated in the team’s growth from small bud to lumbering behemoth. I saw firsthand the social process of creative technological development, which was every bit as messy as Wright’s old computers had suggested.

Later, when I returned to school for a PhD, it was the work of historians and philosophers of technology like Bruno Latour who helped me make sense of my EA experiences.¹⁸ My academic training, which includes computer science as well as the social sciences and humanities, functioned similarly. What I know as a designer and what I learned from apprenticing with Wright form a deep background to this project. The knowledge and relationships I acquired at EA aided the many interviews that inform the Maxis and SimCity chapters. With rare exception, the development narratives in game design textbooks and industry postmortems are sanitized affairs scrubbed clean of the strains, struggles, blind alleys, confusion, and interpersonal conflicts that often characterize the painful realities of production. I offer this book as a corrective example of design in the wild (to pinch a phrase from Edwin Hutchins).¹⁹ This approach foregrounds how people wrestle with their own uncertain present—an approach favored by historians of science and technology.²⁰

As this project developed, it became clear that one motivation was understanding my own past and that of the communities to which I belong. This book, in turn, is an offering to the many communities whose work has benefited me. Given my own position within the SimCity network and its members’ generous contributions of insights and reminisces, Building SimCity can also be seen as an effort by the SimCity network to understand itself.

Organization

Part I, “Simulation’s Grasp,” introduces material that underpins the entire book.

Chapter 1, “Building Imaginary Cities,” makes the opening move in situating SimCity among contrasting simulation approaches. Children enact cardboard and make-believe cities in a simulation method developed by educator Doreen Gehry Nelson. She, like Forrester, was prompted to simulate cities by the urban crises of the 1960s. But her background, in teaching, the arts, and civics, led to a radically different approach, illustrating how context shapes simulation practice. The chapter traces the origin of Nelson’s simulation technique and its links to military and business simulation gaming, Frank Gehry, Charles and Ray Eames, Apple Computer, Maxis, and more. Nelson’s career exemplifies how simulation practitioners, by virtue of their interdisciplinarity, broker relationships between communities and establish network forums for the exchange and generation of ideas, identities, and legitimacy. Comparing Nelson’s simulation technique to SimCity begins to yield a comparative framework for analyzing SimCity (and other simulation approaches) in terms of subjectivity, agency, and legibility. The apparent rift between cardboard and computers provokes a question taken up in the next chapter: What exactly is a simulation?²¹

Chapter 2, “Simulation as Analogy,” presents a framework for analyzing simulations. Vannevar Bush’s early career (1920s–1930s) was spent developing analogs—dynamic instruments for grappling with the perplexities of power grids, atoms, and airplanes. Aided by Bush’s theorizing, this chapter offers a theory of simulation grounded in analogical reasoning. Simulations establish dynamic analogies between a mechanism and a phenomenon and can be understood as representations offering newfound affordances. This chapter retheorizes the analog-digital dichotomy and argues that programmable digital computers are a kind of analog device. Early-twentieth-century analogs enacted their creators’ inclination to think across representations: graphical, symbolic, and tangible. Modern computers swept this world away, but it lives on, shaping the technology and culture of the computer practitioners we’ll meet in the next three chapters.

Part II, “Paving the Road to SimCity,” traces historical developments that shaped SimCity’s design and reception.

Chapter 3, “System Dynamics: A Society of Bits,” explores the roots of SimCity’s linchpin economic model. It began with servomechanism control systems, which Jay Forrester was familiar with from his wartime engineering work. Such feedback loops became, in his computer simulations, models for just about everything. Favoring intuition over data, his approach was well suited to expansive and imaginative uses: supply chains, cities, the world—even a spiritual experience. This chapter seeks a proper historical account of system dynamics situated in control systems, digital computing, and postwar “systems management.”²² Close attention is paid to the processes that promoted a simulation community of practice, thus preparing the way for the adoption of system dynamics into SimCity.

There is no neat history of cellular automata because multiple communities, often working in isolation, advanced the practice. Chapter 4, “Cellular Automata: Synthesizing the Universe,” attempts a coherent account of this fragmented history and shows how it helped set the stage for SimCity and Maxis. It wasn’t until the 1980s that a cellular automata community of practice cohered and gathered scientific respectability, propelled by the emerging fields of artificial life and dynamical systems theory, and the proliferation of inexpensive graphical microcomputers. A close look at this history helps explain SimCity’s design and aesthetics, Maxis’s partnership with the Santa Fe Institute, and why Maxis titles like SimCity were enthusiastically received in mainstream culture as avatars of computer simulation, artificial life, and complexity science.

Chapter 5, “A Children’s Construction Set,” explores the historical origins of SimCity’s user interface. The graphical user interface (GUI) did more than bestow accessibility on SimCity; it supported the kind of self-directed, creative, and playful experience Wright was after—just as its progenitors had intended. As digital computers became more commonplace in the 1960s, researchers began to ask who and what exactly computers were for and how responsible stewardship of this new technology could be encouraged. This chapter follows valiant (and often frustrated) attempts to cultivate cultures of computing in which people would be active creators and shapers, not passive consumers, of knowledge, culture, and computer technology. These traditions gave rise to BASIC, the Apple II, and the GUI, all of which helped popularize computing and, not incidentally, fostered Wright’s career and SimCity’s well-received design.

Part III, “SimCity’s Architects,” closely analyzes SimCity and Maxis.

Chapter 6, “Designing SimCity,” traces SimCity’s development. While Wright authored SimCity’s core simulation and overall experience, it was Maxis, the social assemblage Braun engineered, that refined its interface and brought it to market. Biographical and historical context illuminate Wright and Braun’s approaches and the wide-ranging syntheses that formed the genre-defiant SimCity.

Chapter 7, “Maxis at the Crossroads,” chronicles the history of the SimCity network and Maxis’s struggle to reconcile the opposing forces that gave rise to it. This chapter argues that SimCity’s success, and Maxis’s trajectory, were shaped by how Maxis trafficked at the crossroads of multiple cultures. This crossroads, while enriching, also posed a dilemma: Where was Maxis going? And who were they? While farcical at times, the point is not that Maxis was a mess, but that these sorts of messes characterize design in the wild and that this crossroads heightened their mess and their success.

Chapter 8, “How SimCity Works,” elucidates SimCity’s simulation design. Working from source code, this chapter examines how SimCity’s representations—from internal structures to the exterior user interface—combine to produce a compelling illusion of a living city. These software mechanisms are traced back to the diverse simulation traditions Wright intermixed. Melding these traditions was more than a technical matter; it meant reconciling divergent, and often incompatible, worldviews. Extensive diagrams assist this technical-historical argument. A side effect of this method, which I explain more fully in “Reverse Diagrams,” the appendix, is opening SimCity’s technical interpretation to nontechnical readers.

Chapter 9, “Playing SimCity,” takes up SimCity’s ludic affordances. Using play theory, this chapter analyzes SimCity’s ability to draw people in and hold their attention. What, in other words, makes it fun? This play-centric analysis also illuminates how paradox and ambiguity enabled a wide variety of actors to adapt SimCity to their own purposes—thus underwriting the growth of the SimCity network.

The Conclusion, “The World in a Machine,” summarizes the book’s key ideas and argues that the principal critique of SimCity—that its obfuscated commitments constitute a “hidden curriculum”²³—are emblematic of computing writ large, whose machinations are concealed through software. Building SimCity then reviews its approach toward the challenge of studying software. Finally, I argue that SimCity’s ambiguous appeal, and Maxis’s collapse—not to mention its initial success—stem from Wright’s genius for surfacing the ambiguous and contradictory potentialities of computing, simulation, and play.

I Simulation’s Grasp

1

Building Imaginary Cities

An old prospector retreats into the wilderness, frustrated by city life. Searching for a new home, the prospector discovers a pristine valley untouched by people. A lake, fed by a clear cold stream, tumbles down from the mountains where the prospector soon discovers a remarkable material that can biodegrade any waste. Word of this discovery, called purium, quickly spreads, and the federal government initiates a mining operation. A town springs up, its population doubling every month—snowballing growth that menaces the landscape’s natural beauty and strains the boom town’s infrastructure and social fabric. Under intense developmental pressures, citizens and civic leaders make consequential decisions such as where to build housing, roads, and the airport. They must communicate, plan, and improvise. At the heart of their struggles are questions about values: What do they want and not want in their city? How is land to be used? Tensions must be reconciled, and balances sought between nature and development, individuals and collectives, and diverging interests and desires.¹

This is the scenario of Purium, a city simulation game that took shape at a workshop hosted by the Smithsonian Institution in early 1971.2 According to a magazine article,

Junior planners were faced with questions about a city of the future. How would they get people to and from their jobs? Where would they build an airport? What would they do about electric lines in a city? Pupils used paper, cardboard, and plastic foam blocks to make the cities. They decided what they would do about old buildings, crowds, cars, noise, and pollution. They planned homes, schools, hospitals, and stores. . . . Students were allowed some make-believe . . . they could mine from the mountains make-believe stuff that would make the city’s trash rot easily. They could dangle buildings from a balloon over the city.³

Almost two decades later, a budding San Francisco Bay Area company called Maxis published SimCity. Although it wasn’t created or marketed with education in mind, it intrigued educators. According to Jeff Braun, Maxis’s cofounder, “It was never our intention to go into the education market, but the education market came to us and said: ‘This is what we need if you’re gonna work with us.’” What the market needed were teacher guides for SimCity. Maxis decided to work with educators for a number of reasons. Brøderbund, Maxis’s distribution partner, was deep into a hot educational software market. And along with the venture capital Maxis received in 1992 came an appetite for new markets. Meanwhile, Will Wright, the creator of SimCity, was busy making titles like SimEarth and SimAnt for an uncertain market. Maybe that market was education.⁴

But in order to sell into the education market, SimCity would need to undergo some kind of translation.⁵ What began in 1989 as a simple pamphlet about SimCity for teachers became, a few years later, an earnest effort to meet the desires of educators. In 1992 Maxis hired Claire Curtin, an experienced educational software producer with a graduate degree in educational technology, away from Brøderbund (where she produced Carmen Sandiego) to become Maxis’s educational project manager. Curtin, who would later codesign The Sims with Will Wright and Roxana Wolosenko, contracted curriculum writers and recruited California educators for an advisory board, who reviewed and field-tested guides. One of the educators Curtin hired was Doreen Gehry Nelson, who had developed City Building Education, a method in which students crafted and role-played simulated cities. Nelson became a regular visitor to Maxis, for whom she authored multiple teacher guides, and Curtin made some trips to Los Angeles to see Nelson’s City Building Education in action. The experience of “watching a classroom actually go through a couple of days’ worth of creation,” says Curtin, was an inspiring and unforgettable experience.⁶

§§§

Starting with cities made of cardboard, children, and make-believe—rather than computers—forces us to reckon with fundamentals: How do people make a simulation? Where do simulations come from? What do we simulate when we simulate a city? Nelson wished to simulate the city so as to “relate the invisible fabric of social, political, and economic relationships to the built environment.”⁷ Her simulation approach is very different from that of SimCity. It is co-constructed by a group of people—kids and teachers—through role-play, negotiation, and crafting. Community is front and center.

[picture]

The first city Nelson’s students built, 1970. Oakwood area of Venice, California, as redesigned by Nelson’s first city-building students, grades 3–6, Westminster Avenue Elementary School.

A vivid counterpoint to SimCity’s computerized cities, Nelson’s classroom cities spotlight the living, breathing people who design and imagine simulations into existence. Simulations make worlds, and in examining the history of Nelson’s simulation practice, we see how history shapes simulation and how examining such backstories prepares us to interpret the design and presumptions underlying a simulation practice. Nelson’s career and far-ranging collaborations also illustrate how simulations do more than manifest the invisible fabric of reality; they reconfigure it.

§§§

By the time Maxis hired Nelson to work on teachers’ guides, she, along with the teachers she had trained and their students, had been designing, building, and performing cities made of “happy trash”⁸—craft materials like cardboard, toilet paper tubes, construction paper, and egg cartons—for decades. Her city building exploits began in the late 1960s, but it was at a Smithsonian Institute workshop where the method crystallized and the name “City Building Education” was chosen.⁹

Design, architecture, and art have always been part of Nelson’s life. She has one sibling, the architect Frank Gehry (b. 1929), who is almost ten years older and whom she considers a “second father figure.” As a child, everything he said to her was “just wonderful . . . still is. He influenced me so much, I couldn’t walk down the street with him without him saying, ‘Look at that tree, and look at how that tree grows, and look at that building. . . . And look at this, and look at that.’”¹⁰ Construction play with scrap materials was a formative experience for Gehry, who had, as a child, played with their grandmother in transforming bits of wood into “cities, bridges, buildings.” It was, he recalls, “a license to play” that would go on to serve him as an “emotional and intuitive” “anchor.”¹¹

Nelson created imaginary cities as a response to the troubles of real ones. In 1965, while she worked as an elementary school teacher, the Los Angeles Department of City Planning began the LA Goals Program to envision the city’s future and advise municipal leaders on social and architectural matters. With the 1965 Watts riots, hope turned to fear, and the program was absorbed into LA’s response to an unfolding crisis. Nelson joined a task force on city planning and education charged with researching what people wanted for the city’s future. She was stunned by a lack of creativity: “Nobody could even think of anything. . . . They could think of Disneyland because it was already there. And they wanted a monorail.” To address a failure of civic imagination among adults, Nelson turned to children—a natural move for a school teacher. Nelson took a committee from LA Goals to a classroom, where she had the children build a tabletop city. She discovered subtle ways to encourage and unblock their creativity. She denaturalized familiar elements of the city, for example using abstract terms like pathway and shelter rather than road and house—words that point toward the design problem to solve, not clichéd solutions. This created an opening for imagination and creativity to enter and take up new and unfamiliar forms, such as pathways that meandered or soared through the sky.¹²

[picture]

Nelson’s students at work. Many photos are from the Eames Office “Kid City” slide show; some were taken by Charles Eames.

Model city details, 1970–1977. Multiple photos from Eames “Kid City” slide show.

Nelson used her school as a laboratory for experimentation. She endlessly adapted her classroom, a bland shoebox bungalow, and found that her students’ ideas were better than hers; they redesigned it together, and she began coaching her colleagues to do the same.¹³ In 1969, she and Ruth Glatt, with whom she team-taught at Westminster Elementary in Venice, diverged from the school district’s curriculum—the history of westward expansion—to explore the future of their own community. Rather than ask students to be “imitators and replicators,” and enact “a numbing program that only fed the mind with facts,” Nelson sought to “challenge them to think today about the world of tomorrow” and “apply creative thinking to everyday life.”¹⁴

“Boundaries were formed around the community, and by decreasing the scale, the students brought a large section of the Venice landscape into their classroom.” Through a “‘land grab,’” students took ownership of their neighborhood and then enacted “a giant simulation of the community planning and government process,” creating a scale model “City of the Future” set 100 years hence.¹⁵ Glatt, a lover of arts and crafts (which Nelson despised), helped the kids “pretty it up” so they “loved it even more.” Although Nelson “was perfectly happy with it being rough,” she came to appreciate the potency of ornamentation.¹⁶ Nelson recruited architecture students, who helped them plan and make models, establishing a pattern whereby outside experts like mayors, firefighters, and developers answered students’ questions and shared their expertise. The presence of experts also created a sense that something important was happening. Teacher authority was decentralized, and students became agents of their own learning.¹⁷ To support these experiments, Nelson researched learning theory for her master’s degree and developed a theoretical basis for this work inspired by scholars like John Dewey and Jerome Bruner.¹⁸

[picture]

Students at Westminster, Sulphur Springs, Pasadena, and Compton, 1970–1976. One photo shows a consulting architecture student. Most photos from Eames “Kid City” slide show.

She was stunned by the model city’s potency as a vehicle for conceptual transfer. Take movement, for example: “If I taught them about the heart, and the fact that things move around in the body, could they now go out onto the city and build a movement system that reflected what they had learned about the heart? And yes they could.” Looking back, she was struck by her inability to escape the theme of the city. She had thought that after the city, she and her students would build a “a marine biology world,” and then a “business,” but now, over fifty years later, “I’m stuck with the city . . . The city is like a classic metaphor somewhere in our psyche for our human thinking. We navigate in this thing all the time called the city and we obviously have a very deep connection [to it].”¹⁹

There were roadblocks. As part of a design exercise she asked students to photograph their homes (using donated cameras), but they only produced excuses.²⁰ It dawned on her that they were embarrassed. She was stuck. How could she get them to reimagine their city if they couldn’t even imagine rearranging their own homes? Her students clearly felt powerless. She also felt powerless, as a teacher and as a woman.²¹ Taking inspiration from the cultural moment of the late 1960s, which, she observes, was all about change and empowerment, Nelson asked herself: How can I help my students feel powerful? How do I feel powerful?

She found power in transformation. Just as her older brother had changed the family name from Goldberg to Gehry to garner protection from antisemitism, she too sought to transform herself and her students. They would become something new, adopt a costume—“a new skin.” She would “cover them up. They needed to become something besides who they were. Instead of being you, you needed to be a hammer. And then if you were a hammer, you could think about where you lived, and who lived with you, and what kind of a building it would be, and how it would be arranged.”²² She had her students select and transform into their favorite objects—bacon and eggs, a diamond, a Coke bottle—which fostered feelings of safety and empowerment.²³ A child transformed into a Coke bottle, for example, would be free to imagine her environment: a vending machine, a refrigerator, or something else.²⁴ Costumes enabled her kids to “fool around,” play, make things, and reimagine their environment. In Nelson’s analysis, these character transformations, a way of “making new us’s,” were profound. The multidimensionality of people could be explored, contributing to the formation of new communities.²⁵ Nelson came to see transformation as the central creative act of artists, designers, and scientists. This insight was borne of conversations with artists like Claes Oldenburg (she co-owned and helped run her husband’s art gallery, the Rolf Nelson Gallery) and, later, inventors and scientists like Buckminster Fuller, as they dissected their intuitive “leaps of insight.”²⁶

[picture]

Classroom model, “My house” floor plan, and costume activities. Many photos from Eames “Kid City” slide show, including two by Charles Eames (bottom).

She wanted her students to be creatively emboldened, feel in control of their lives, and exert imaginative agency over their built environment. Through transformation, the surrounding city—big, imposing, and filled with problems and possibilities—could be brought into the classroom, wrestled with, and reinvented. As they “began building a city, I kept telling my students that ‘It’s got to be bigger.’ I thought that if they did something big—something they couldn’t put in their desks—then they might be empowered to do some big things in their lives.”²⁷

The model cities were mesmerizing. “The children,” writes Nelson, “were very proud of their work, and the number of people who came to see their fantasy city of Venice gave increased weight to their ideas.”²⁸ The same was true of Nelson, whose work attracted increasingly widespread attention. In April 1970, the students presented their work to the Los Angeles City Planning Board. Striking his gavel, the board director facetiously recommended accepting the children’s plans. With tears in his eyes, the child mayor ran to Nelson, who was in the back of the auditorium: “They think we’re just little kids and that our ideas can’t work, but they haven’t done such a great job!” The planning board asked that the Venice model and map be displayed in City Hall’s rotunda. Government officials, academics, and journalists enthused. Major television networks—ABC, NBC, and CBS—began to pay attention.²⁹

On December 28, 1970, ABC aired “America: Alternative Futures,” a two-hour documentary on the future of the world.³⁰ It included segments on Nelson’s model cities, Stewart Brand’s Whole Earth Catalog, the authoritarian rehab program Synanon’s “games,” and the seminal city simulation games of Richard Duke, who invited Nelson to a two-week conference at the University of Michigan, where he was a professor of urban and regional planning and director of the Environmental Simulation Laboratory. While Nelson’s work had been informed by art, architecture, and education, Duke’s city simulations, some of which used computers, emerged from the tradition of military and business simulation gaming. Both made extensive use of role-playing.³¹ (Duke believed that “games are a form of communication”³² through which players, “each holding a unique perspective, sincerely seek to communicate and/or negotiate a compromise,” thus revealing their “collective intuition.”³³) “He was the first one that told me that what I was doing was a simulation. I didn’t have the word for it,” says Nelson.³⁴

[picture]

Giant “Future City” in Compton, “Helium City,” city map (with hands), and model classroom. “Future City” combined areas made by students in multiple classes who surprised their teachers with an integrated transportation network. Mainly c. 1973.

As a result of the national media attention, the Smithsonian Institute invited her to co-teach a class in early 1971. Unaware that they knew one another—let alone that they were siblings—the organizers had invited Nelson to co-teach with her brother, Frank, at that time a relatively unknown architect who had been running an after-school class called “Fantastic Cities.” In a further twist, neither seemed aware of the other’s work with children and model cities.³⁵ On the plane to Washington (and merry with drink), they conceived of Purium, a game named after a miraculous element that eliminated pollution.³⁶ (The name was inspired by Purim, an approaching Jewish holiday often celebrated by dressing up and drinking.³⁷) Taking a cue from Duke’s city simulations, Nelson conceived of Purium as “a short-term game simulation.”³⁸ Although Nelson was “intimidated by the idea of professionals, such as architects, playing her Styrofoam and cardboard city building games,”³⁹ the Smithsonian workshop was a smashing success.

As summer approached, and the 1970–71 school year drew to a close, Nelson wrote to Charles and Ray Eames, whose office sat across the street from her school, inviting them to visit her students’ “City of the Future.”⁴⁰ The Eameses politely declined Nelson’s invitation, but promptly responded to an illustrated letter from her students. The Eames entourage, film-screening equipment in tow, was received by the principal, who deflected them away from the troublesome Nelson. Alerted by a student, Nelson rushed downstairs and confronted the screaming principal. “Charles and Ray were sort of like deer in the headlights.” As they entered her classroom, a ten-year-old greeted them: “How do you do? I’m the mayor of our city. Can I give you a tour?” Nelson, who later grew close to Ray, recalls asking her if it was the physical artifacts that had enchanted them. “No,” Ray told her, “Charles and I were just taken away by the ability of these kids to articulate what they had done.”⁴¹ At every step, student initiative brought forth a new collaboration.

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Learning about tensegrity, student photographer (in the first city), model classroom, and drawing a city map. Tensegrity photos (by Charles Eames) from the Eames slide show. Mainly early 1970s.

That summer, Nelson returned to the Smithsonian to run more city building workshops. While in Washington, Charles Eames invited her to a meeting with advisers to the National Endowment for the Arts (NEA).⁴² Soon after, Nelson had a $30,325 NEA grant that underwrote a year-long experiment at three schools (including her own Westminster school).⁴³ That fall, when the new school year began, the Eames Office began production of a three-screen slide show entitled “Kid City” about Nelson’s work.⁴⁴ Eames staffer Jon Boorstin, who contributed to the slide show, temporarily quit so as to make a documentary film, also titled Kid City, about Nelson’s work (with additional NEA funding).⁴⁵ The Eames Office eventually gave her studio space, made multiple films about her work, collaborated on her books, and took thousands and thousands of photographs.⁴⁶ (Most of this chapter’s images come from this collaboration.)

According to Nelson, Charles made the slideshow to support the NEA’s congressional fundraising efforts. Originally founded in 1964 as part of President Lyndon B. Johnson’s Great Society, the NEA saw stupendous growth under the formidable leadership of Nancy Hanks, a Nixon appointee. As NEA chair, Hanks was advised by a council, also appointed by the president, whose ranks included Duke Ellington, James Earl Jones, Harper Lee, and, from 1970 to 1976, Charles Eames. An architect by training (and self-identification), Charles lobbied the NEA to include architecture in its burgeoning Artists in the Schools Program.⁴⁷ Consequently, the Eames Office did far more than take Nelson under their wing; Nelson helped Charles and Hanks expand and shape the NEA, which directly supported her work.

Boorstin’s NEA-funded film depicts Nelson and Gehry having a falling out with a classroom teacher. This, Nelson told me, was characteristic of the early NEA-funded collaborations. To her astonishment, the NEA wished to fund a second year. The Los Angeles Unified School District, however, wanted nothing more to do with Nelson’s program (if she was involved), so she shifted to a different LA County school district.⁴⁸ The mess and clutter of her method posed a huge challenge. So did learning how to train teachers who could integrate her method with existing curricula. For Nelson, staying organized as her operation grew posed a challenge all its own. (In retrospect, Nelson believes that “I was the problem.”) It was “depressing for many, many years,” but she persevered, refining and iterating her approach.⁴⁹

Over time, Nelson’s work accumulated more grants (public and private), magnetized new partners (actors, architects, and filmmakers; city council members and professors; teachers, students, and school administrators), and spread to new schools, all of which led to even more attention.⁵⁰ She wrote books, penned research articles, partnered with and taught at academic institutions, and founded a nonprofit in 1974 whose advisory board came to include Ray Eames, Jonas Salk, Gloria Steinem, the deans of CalArts’s School of Music and UCLA’s School of Education, the artistic director of the Mark Taper Forum, and renowned architects: Gehry, Lawrence Halprin, Sim Van der Ryn, and Richard Saul Wurman.⁵¹ She cold-called Jerome Bruner, a towering figure in education, who became a supporter, as did Buckminster Fuller, who recommended “that City Building Education be included in all elementary and high school curricula.”⁵²

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In 1986, Nelson’s method was adopted at the Los Angeles Open School, a public magnet school founded in 1977 to help desegregate the Los Angeles Unified School District. Nelson’s involvement, curiously enough, happened through the intervention of Apple Computer. When Alan Kay, an Apple Fellow contemplating a new educational project, visited the Open School, he hit it off with the principal, who had centered the school around a Bruner-developed liberal arts curriculum.⁵³ (Although Kay is most famous for co-inventing object-oriented programming and the modern graphical user interface, his underlying interest is learning. When Kay first proposed his seminal Dynabook concept, he drew heavily on scholars like Bruner and Jean Piaget.⁵⁴) Kay secured major funding from Apple Computer for a sprawling research program, called Vivarium, that partnered closely with the Open School and the MIT Media Lab, where Kay was a visiting professor, and he got MIT’s Marvin Minsky and Seymour Papert involved. When a Vivarium staffer sought a method for teaching architecture and design, she found Nelson’s work.⁵⁵

The Vivarium program brought together computers, experiential learning, simulation, and complex systems thinking under the umbrella of an idealistic educational experiment. Children cultivated miniature worlds through city building, gardening, and computer simulation. Special desks unobtrusively housed the Macintoshes (donated by Apple) that were used to do things like simulate smog and plan gardens. The far-reaching Vivarium program sought to foster learning environments that would prepare children to understand, design, and inhabit complex biological, ecological, and urban systems. According to Kay, children would become motivated learners through the creation of “whole worlds.”⁵⁶ Bruner’s philosophy, as Kay and Nelson both saw it, placed creativity at the center of learning and provided a shared context for the Open School collaboration. Nelson helped integrate her City Building method into the curriculum, where it became a crowning success.⁵⁷

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The Open School in Los Angeles, c. 1991.

So how, exactly, did Nelson meet SimCity’s developers? Kay, who made at least one visit to Maxis, would certainly have recommended they talk to her.⁵⁸ Nelson believes Kay made the introduction, but thinks it’s also possible that they met in Santa Fe at an artificial life conference.⁵⁹ As far as anyone at Maxis can remember, though, she just showed up on their doorstep. Will Wright, SimCity’s designer, loved how Nelson had been “teaching city planning to kids for ages. I was impressed she had all these materials about building cities and she’d been using cardboard tubes and shoeboxes and stuff.”⁶⁰ Like Wright, whose SimCity also facilitated exchanges between previously separate communities, Nelson had become a kind of “network celebrity.”⁶¹ In this light, the question of how exactly Nelson met Maxis is beside the point; each possessed such an extensive social network that it would have been incredible for them not to have met. By the time they did, she had been collaborating with artists, architects, simulation game designers, and computer scientists for many years. What’s more, Nelson and Maxis’s shared interests went well beyond simulating cities. Wright had recently begun work on a title called “Dollhouse” that turned players into architects (this eventually became The Sims), and Nelson had long had her students redesign their classrooms and homes. “We fell into each other’s arms,” says Nelson.⁶²

Purium Instant City

To better convey Nelson’s method, let’s examine Purium more closely.⁶³ Purium unfolds on a three-dimensional model of the city and its environment. The city is also simulated with maps and charts and by students who play various roles: a mayor, commissioners, representatives, and citizens. The teacher begins by guiding students into “asking questions, discussing, and describing their ideal visions for community life.” They probe aspects of cities often taken for granted. For example: Where does trash go? What would you like to change about your city? What do you love about your neighborhood?⁶⁴ This activity helps students articulate what they “Need” (and “Don’t Want”) in their city—criteria that will be prominently posted.

In articulating their own goals, students develop a new relationship with their teacher. They are encouraged to “attempt projects without waiting for the guidance of external commands. Their own authority becomes internalized, and the teacher becomes a helper, assisting the class to see how it measures up to its own yardstick.”⁶⁵

Through role-play, students become citizens of districts, each of which elects a representative to the city council. Citizens also elect a mayor. City commissioners are designated for roles like education, parks and recreation, culture, aesthetics/natural beauty, and underground.⁶⁶ Students wear badges that signify their roles as citizens, representatives, and commissioners. “These multiple badges emphasize that in an interrelated entity like a town, each member has more than one role to perform.”⁶⁷

To complete the planning phase, the mayor, commissioners, and representatives discuss and establish land use patterns. As the clock begins to tick, the city rapidly grows and “students meet the unplanned variable of fate head-on.” If excess materials aren’t properly disposed of—say, in a landfill—then the teacher might dump them into the lake. Acting as a kind of game master, the teacher helps bring the world to life. Such pressures shape the unfolding simulation, forcing trade-offs and decisions.⁶⁸

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Students analyzing, planning, role-playing, and debating.

Frank Gehry and Doreen Nelson running a classroom Purium simulation at Westminster, October 1971. Film stills from Kid City, directed by Jon Boorstin.

Students find that it isn’t easy to honor their original intentions and criteria. Perhaps waste ends up in the lake, hospitals are located too far away, or the city is too noisy, making it hard for residents to sleep. While this can be “painful and frustrating,” the “anger catalyzes” students to solve problems and imagine new possibilities. “City Building asks the students over and over again: ‘What are you going to do about it?’” As a result, students eagerly seek advice from experts—the mayor, mail carriers, firefighters, and real estate developers—for future City Building activities (which won’t be so time constrained).⁶⁹

Play closes with reflection. The president (role-played by the teacher or another adult) arrives and receives a tour from the mayor and commissioners, who answer questions about the city and its development. Reviewing the city against their own criteria, students reflect on the planning process, accomplishments, and missteps. Finally, individual and collective experiences are recorded, leaving behind a history wall (an idea directly inspired by an Eames Office exhibit) that documents everybody’s experience.⁷⁰

Simulation in Context

It might seem strange to begin a book about computer simulation with cardboard, classrooms, and children, but these too are simulations. As a counterpoint to SimCity, Nelson’s method invites us to consider the sprawling breadth of simulation practice. Her work also reminds us that simulations are shaped by history and animated by people. We cannot understand a simulation tradition, in other words, without considering its practitioners alongside their ideas, intentions, and contexts.

Simulations have backstories. These tales, and the people who populate them, shape the resulting simulations. Nelson interwove ideas and practices from military and business simulation games, art and architecture, activism, and education. Social and cultural circumstances played an important part. The 1960s was a time of unrest and ferment. The Watts rebellion, for example, expressed the pent-up frustration of Black Americans to segregation, discrimination, and police brutality. To try to address this unrest, city leaders enlisted citizens like Nelson and asked them to help imagine a better future. In turning to children, and adopting the techniques of model building, Nelson manifested her background as an elementary school teacher engaged with art and architecture. The same urban crises would elicit very different responses from game theorists and computer simulation makers—work that laid the groundwork for SimCity, as we shall see in chapter 3.

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“Transportation Committee” drawing, wall-sized flowchart of activities, model classroom, and more.

First city map and model details.

Particular ideas and values shaped Nelson’s work. She assimilated contemporary re-imaginings of how to live and learn. The prospector retreating into the wilderness, beginning civilization anew, mirrors the back-to-the-land movement of disaffected young people underway in the late 1960s. Nelson’s work also dovetailed with resurgent education reform efforts of the 1970s, which also manifested in the work of researchers, like Kay and Papert, who brought together children and computers. Nelson, Kay, and Papert all took inspiration from thinkers like Piaget, Bruner, and John Dewey, who conceived of learning as active participation, an “organic assimilation starting from within.” The child’s personal experience of, and interest in, the world around them should guide learning, not a curriculum fractured into facts, subject areas, and particular points of view. As we will see in chapter 5, this approach had a profound effect on the development of computing and also helped set the stage for SimCity.⁷¹

Simulations make specific kinds of worlds and offer particular models of agency and subjectivity. What do we simulate when we simulate a city? Who gets to have desires, act on them, and make decisions? Where is agency located? Who is a player, and who is played? Nelson’s cities realize a democratic vision of subjectivity and community. Students role-play the social processes of citizenship and governance and must reconcile the desires, plans, power, and perspectives of others. SimCity players, in contrast, are situated like Robert Moses, the infamously powerful and often tyrannical architect of twentieth-century New York: they reshape the urban landscape according to individual whims.⁷² The computerized cities we examine in subsequent chapters construct citizens as passive mechanisms—living beings stripped of agency.⁷³ Citizens of Nelson’s world, by contrast, are played by real people. It is a world in which a group of concerned citizens can reimagine and reshape their world.

In Kay’s analysis, SimCity is technically impressive but not “good enough for children.” Its rules should be exposed so kids can see and change them.⁷⁴ The underlying model lacks legibility and agency. SimCity’s world is veiled: players cannot look under the hood; investigate its baked-in assumptions about crime, taxes, and mass transit; and then tinker with them. Nelson’s simulations instead invite students and teachers to observe, negotiate, and reinvent the rules of their make-believe world. Good learning environments, Kay argues, “make contexts visible, make them objects of discourse and make them explicitly reshapable and inventable.”⁷⁵ Many simulation makers argue that to truly understand a complex system, one must build a model of it oneself; being a mere player in someone else’s world is a poor substitute.⁷⁶ Nelson laments the fixity of SimCity’s make-believe world; players cannot invent new technologies or architectural ideas. Her city builders, by contrast, are free to perceive, discuss, and co-invent the rules of their world. They can build underground or float structures in the air. And they choose their own goals, defining the criteria by which they themselves will judge their cities (as in SimCity).⁷⁷

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Students’ feet from Eames “Kid City” slide show (some, if not all, by Charles Eames).

Nelson’s method makes clear that simulation models descend from particular historical traditions, embody certain beliefs about the world, and are animated by people.⁷⁸ Simulation is something that people do with materials; people perform and imagine them into existence. Digital computers are one kind of material to simulate with, but there are others. Cardboard, three-dimensional models, graphs, criteria, costumes, and people can also simulate. It is the creators and players of simulations, in the final analysis, who give them form and bring them to life through make-believe.⁷⁹

Nelson’s career also illustrates how simulation practitioners, by functioning as interdisciplinarians, open avenues of exchange between communities.⁸⁰ The transformative power of Nelson’s method is due in large part to how she brings diverse communities—children, teachers, artists, architects, engineers, inventors, designers, community members, city officials, computer scientists, filmmakers, academics—into conversation. In brokering these relationships, Nelson established a “network forum”⁸¹ whereby different communities exchanged ideas, bestowed legitimacy on one another, and synthesized new ideas, products, identities, and networks. Nelson did far more than leverage the talent and status of elite (and often male) figures; she offered a way for them to see themselves as contributors to the fundamental social good at a time, in the early 1970s, of profound anxiety over cities, race relations, and the future. Simultaneously, Nelson empowered herself, other teachers, and their students to see themselves as players in rarefied and elite spheres: art, design, city planning, and politics. Simulated cities brought the outside world into the classroom, but it also brought the classroom into the world outside.

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Students building a kite at the beach, visiting Watts Towers, the Bradbury Building, and Claes Oldenburg’s Ice Bag–Scale C at the Pasadena Art Museum. Photos from Eames “Kid City” slide show, early 1970s.

How did Nelson enable this efflorescence? What was the essence of her intervention? To answer this question through careful examination of her voluminous, brilliant, and ever-evolving method (now called the Doreen Gehry Nelson Method of Design-Based Learning) may be a fool’s errand. Instead, consider the relationships she brokered. By melding influences from design, education, and civics, she created a space for these communities to collaborate. It’s not unlike the stone soup parable, where a hungry traveler invites each passing villager to add a little something to a pot, empty but for a stone and some water: a bit of carrots, some onions, a little butter, a handful of peas. Beginning with a classroom of children, her own considerable teaching expertise, some civic context, and a basic fluency in design, Nelson solicited contributions from architecture students and architects, artists and designers, city planners and game designers, funders and filmmakers, children and community leaders, and others. Without the curious learners, the earnest civic engagement, or the florid creativity, it’s hard to imagine Nelson rallying collaborators as she did. She provided a forum for these communities to collectively imagine futures that were optimistic, sometimes fantastic, yet always grounded in the push and pull of social reality.

Nelson invited self-expression that also built community. Such interdependence, she believes, is what everyone wants; it is the glue that holds society together.⁸² At a time, like now, that also seems to be overflowing with crises, her true gift may be the interdependent and optimistic creativity that conceives and nourishes a brighter future for all—in a word: hope. As Nelson and a collaborator once wrote, “The small city sitting in the middle of the classroom becomes the center-piece for inventing the future, for using information and for connecting all disciplines.”⁸³

2

Simulation as Analogy

I especially love analogies, my most faithful masters, acquainted with all the secrets of nature . . . they clearly present to our eyes the whole essence of the question.

—Johannes Kepler, 1604¹

Doreen Gehry Nelson was not the first educator to use the city as a vehicle for learning about, playing with, and improving the world. Educator Caroline Pratt (b. 1867), who also took inspiration from John Dewey (and Maria Montessori), did something similar. She took her charges out into Manhattan to observe and inquire, where they encountered parades, mass meetings, and protests; boatmen and powerhouses. After learning new facts, “they hurried back to school to put them to use,” where they built and played with model cities made of paper, blocks, toys, and imagination. This caused the children to ask new questions. How does a water tower work? Such questions motivated them to seek answers from the science teacher and other experts. Curious about how electricity reached their homes, they visited a power plant, where they learned about generators, dynamos, and the people who run them. Some years the kids were so inspired that they built a working electrical system in their model city, “a mighty pretty sight.”² One year, they became excited about playgrounds and wrote a letter to New York parks commissioner Robert Moses expressing their appreciation, and he replied. Through all this, the children became interested in issues of social, economic, as well as infrastructural importance.³

Around the time that Pratt’s students began building model cities and power networks, scientists and engineers at MIT were doing something similar: they built dynamic models of power grids, bridges, dams, canals, and airplanes. These analog models helped lay the groundwork for digital computers—instruments that one day would be used to simulate cities, power grids, and much else.

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Caroline Pratt’s students learning “on the docks” and “experimenting in construction” at the Play School circa 1917.

It’s clear that models are helpful; otherwise, we wouldn’t bother with them. This bit of common sense, however, obscures a rather obvious question: Why are they helpful? What exactly are these models doing? Why bother simulating anything at all?⁴

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One answer comes from developmental psychologists Jean Piaget and Lev Vygotsky (both b. 1896). Working around the same time as Pratt, they found that children gain mastery through manipulating concrete objects. Children, they claimed, progress from concrete to abstract thought. We eventually outgrow solid things—mere stepping stones to airy abstraction.⁵

But what about the models built by MIT’s supposedly sophisticated engineers?

In a 1936 lecture hosted by the American Mathematical Society, Vannevar Bush (b. 1890), dean of MIT’s School of Engineering, offered an alternative explanation. Mathematics and its abstract symbolic systems—numerals, algebra, calculus—are inseparable from physical “aids to reasoning.” There are many possible “devices for supplementing pure reasoning,” beginning with the human hand, the original “mathematical apparatus.” Such a “device aids the mind because it approximately obeys some simple law, and can be made to indicate the combinations of such relationships.” One can, for example, count with fingers or pebbles because they obey the simple law of addition: put one pebble beside one pebble and you have two pebbles.

“The use of instruments of computation and analysis is as old as mathematics itself. Counting by the aid of piles of pebbles gave origin to the word calculus.”⁶ In ancient Latin, a calculus was a small stone used in counting, arithmetic (an abacus bead), gaming (a token), and voting. Pebbles helped people to play, think, calculate, negotiate, and reason. The word evolved to refer to reasoning and computation (calculate), mathematics (calculus), and the people and machines who calculate (calculator).⁷

Some aids to reasoning, such as the analytical engine dreamt up and designed by Charles Babbage (b. 1791), remained unrealized. But others had, such as the arithmetic devices of Blaise Pascal (b. 1623) and Gottfried Leibniz (b. 1646), as well as Lord Kelvin’s (b. 1824) harmonic analyzer, which predicted tides by means of gears, wires, and pulleys and inscribed the results. There were, of course, the ancient abacus, the slide rule, the planimeter, Léon Bollée’s (b. 1870) multiplying “three-dimensional cam,” and the vast shuffling sea of punched cards that banks, insurers, and businesses used to feed the insatiable appetite of their arithmetic machines—a commonplace by the 1930s. “Anything measurable may of course be used to represent a number,” which meant that spring deflections, optical devices, graphs, voltage, gears, shafts, rolling balls, glass spheres, fluids, dyes, and optical masks—to enumerate some examples—could be, and had been, cast as actors in dramas of analysis, calculation, and visualization.⁸ It might seem strange that these diverse materials might model our world, aiding and extending thought. But are they actually stranger than materials of inscription like paper and pencil, symbols, language, and pictures?

For Bush, analysis was inseparable from the materials that aided and performed thought and visualization, whether pebble, pen, gear, circuit, or dye. One could measure and reckon about the material world in so many ways. It was natural, of course, that the flesh of the world—in its rich and plentiful material, shape, heft, conductivity, mechanics, and flow—was perfect for measuring and reasoning about itself.

Vygotsky and Piaget, in arguing that children progress from concrete to abstract thought, reiterated a deep-seated Western bias against concreteness.⁹ They missed what Bush makes plain: children and adults both think with concrete materials. To put it more simply: it’s not a progression at all. Unit blocks and dynamic simulations help us think, whether you’re a child or a dean of engineering.

§§§

We perceive, think, and do with many things. Computer scientists have long used the word representation to refer to how information is structured inside the computer. There are many ways to encode the same information, but different representations offer distinct advantages, just as listing words in alphabetical order, as in a dictionary, makes them easy to find. In computer graphics, “representation” refers to the multiple forms a picture can take, both internally, as computational data structures, and externally, as visible image.¹⁰ Designers also use the word representation to describe information graphics.¹¹ Cognitive scientists use representation to describe our internal mental (and perceptual) structures.¹² They also use it to refer to external objects—concrete things like puzzles,¹³ games,¹⁴ numeration systems,¹⁵ and the entire external environment—whole systems of things and people with whom we might collaborate in processes of distributed cognition.¹⁶ A “representation doesn’t need to represent anything: it is simply the medium (internal and/or external) on which the task performer performs the task.”¹⁷ Representations can be material things, mental constructs, and electrical patterns; graphical, linguistic, and symbolic (numbers, words, equations). Representations can even be performative acts: behavior, acting, and speech.¹⁸

Representation is a powerful and flexible concept that will help us contemplate the workings of minds, machines with gears, digital computers, graphics, cardboard cities, and software—independent of what they are made of or where they reside.¹⁹ This is especially important when dealing with analogs, computers, and software—devices that blur the boundary between abstract formal systems and physical things.²⁰ This concept will also enable us to put software under the microscope and zoom in to its many subrepresentations: the data structures, algorithms, graphics, and user interface elements that produce ensemble representations like SimCity.²¹

Representations take many forms, from prose, poetry, and people to math, mind, and machine. Each offers distinct affordances—not qualities of objects per se, but of the relationship between a creature and its environment. They depend on enculturation as much as physical ability.²² A bicycle affords riding only to those who know how. To an engineer, a second-order differential equation affords reasoning about a physical system but is little more than a cipher to most people.

Similitude

By 1936, Bush was famous for creating one of the most powerful analytical instruments ever invented: the differential analyzer. New kinds of problems call for new approaches, and the differential analyzer was part of a new breed of tools for dealing with increasingly complex challenges. Engineers often called these tools analogs because two systems—model and phenomenon—were said to be analogies of one another. Algebra and calculus, the traditional tools of symbolic analysis, were “baffled” by the problems scientists and engineers now faced, which bristled with “non-linearity.”²³ Analogs, however, enabled engineers to experiment with dynamic models, or simulations, of the phenomena they wished to understand. Technical advances, such as improved tools and materials, enabled them to build what prior generations could only design,²⁴ and electricity, which engineers had become increasingly dexterous with,²⁵ imbued their instruments with automaticity.

In “Structural Analysis by Electric Circuit Analogies,” Bush showed how electrical circuits could be used to analyze the design of a bridge and simulate its response to a passing train or how a building might stand up to an earthquake. Rather than analyzing these structures by solving the equations that govern their physical behavior or subjecting scaled-down “mechanical models” to earthquake-like forces, Bush showed how the same job could be done by “analogous electrical circuits.” To apply load (weight) to the model, you applied current. “To get the stress in any” beam, you “read the current.”

This technique lent itself to interactive simulation. To “simulate” “the stresses produced when a train runs over a bridge,” Bush used light to excite photocells. To direct the light, he used a screen with an aperture cut into it:

Moving the screen in front of the row of photo-cells is then a very analogous procedure to running the train across the bridge. The examination of the structure for different kinds and loading of trains then involves merely the cutting out of a set of screens of proper apertures. The effect of a change in speed of the train is completely simulated by changing correspondingly the speed at which the screen is moved. A very thorough-going examination of a structure is hence possible.

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Circuit analogy for a truss bridge. Structural diagram (top) and its electrical analog (bottom).

Electrical circuits could also represent a sprawling power grid. By using modular components—building blocks, as it were—the MIT network analyzer could be flexibly reconfigured to “represent numerous actual systems”²⁶ and explore scenarios, like the failure of a power plant or transmission line, or the effect of multiple trains drawing power as they raced through a city.²⁷ According to Bush, a “power system covering several states is thus compacted into a single room for detailed examination” with the “miniature network having the same electrical proportions as the actual system.”²⁸

§§§

Scientific advances had revealed that the universe was brimming with dynamic self-similarity, establishing the underlying principles of analogs. In 1915, the eminent English scientist Lord Rayleigh observed that the “natural order” reveals “the great principle of similitude,” noting that diverse physical processes—stress and strain on a bridge, wave propagation, light scattering, air resistance, heat conductivity, and much else—have similar dynamics across scale.²⁹ Using the “principle of dynamic similitude,”³⁰ engineers studied model airplanes and ships inside wind tunnels and tow tanks.³¹ Bush’s former student Harold Hazen, who led the development of MIT’s network analyzer, built a miniature hydraulic model of the Cape Cod Canal to study how a proposed enlargement might affect tidal patterns.³²

So how, then, did electrical circuits come to stand for a bridge? It was, Bush explained, because the same differential equations govern “both the behavior of the [electrical] network and the motion of the structure.”³³ Bush, a master of the analog arts, explained the principles of this approach in a 1929 textbook: “A surprising amount of engineering analysis is concerned with circuits of various sorts . . . circuits of hydraulics, mechanics, thermics, acoustics, and even chemistry.” Through “circuit analogies,” one kind of circuit could represent another.³⁴

By the 1920s, electrical systems were well enough understood to use as analogs of diverse phenomena. The formulas for electrical circuits, Bush explained, are “much more convenient . . . than the formulas for mechanical systems. It will accordingly be convenient to solve first the electrical circuit which is analogous to the mechanical system of our problem, and then to interpret the results on the problem itself.”³⁵ Audio engineers, for instance, found that “mechanical transmission systems can be designed more successfully if they are viewed as analogs of electric circuits.”³⁶

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Circuit analogies from Bush’s textbook. Analogically equivalent electrical, mechanical, and hydraulic circuits.

Analog for audio engineering. Diaphragm and air chamber (left) alongside its electrically equivalent analog (right).

Such physical analogies, which enabled the transfer of mathematical techniques from one branch of physics to another, had been established scientific workhorses since the mid-1800s. Adapting the approach, James Clerk Maxwell had used imaginary physical analogs to help conceptualize and develop the laws of electromagnetism.³⁷ Concrete physical analogs, however, are something quite different. Bush and his engineering colleagues invented machines made from circuits and gears. Analogy linked these machines’ operation to a separate phenomenon, allowing the analog to stand for it.

These physical analogs enabled engineers to scale down massive systems like bridges, trains, earthquakes, power networks, dams, planes, boats, and canals and fit them into research labs, where they could be manipulated and measured as never before. Analogs made possible the investigation and design of systems that would have been infeasible by other means, frequently allowing engineers to bypass mathematical analysis altogether.³⁸ SimCity, like a classroom city made of cardboard, functions similarly.

Physical analogies exploit what seems to be a basic quality of the universe: it rhymes with itself. Patterns repeat. A miniature plane flying in a wind tunnel echoes the dynamics of a full-size plane soaring through the sky. The branching patterns of trees, arteries, and rivers look alike—all because similar dynamics give rise to similar phenomena. The architecture of nature, in other words, is self-similar.³⁹ It is ripe with potential analogy. This may also be so because, as some cognitive scientists contend, the core of human cognition is analogical.⁴⁰ We see analogies everywhere, in other words, because analogy is how we think. Nature rhymes because we’re wired to hear it that way. As a result, natural analogies abound, awaiting discovery, and engineered analogs await contrivance.

As Maxwell was well aware, devising an analogy stimulated creativity. And as Bush had also found, the right analog, by activating embodied ways of knowing, welcomes different kinds of minds, like those, as Maxwell said, who

are not content unless they can project their whole physical energies into the scene which they conjure up. They learn at what a rate the planets rush through space, and they experience a delightful feeling of exhilaration. They calculate the forces with which the heavenly bodies pull at one another, and they feel their own muscles straining with the effort.

To such men momentum, energy, mass are not mere abstract expressions of scientific inquiry. They are words of power, which stir their souls like the memories of childhood.⁴¹

Pluralism

Bush explained the differential analyzer with a comparison. A miniature plane in a wind tunnel could be used to study the aerodynamics of a full-sized plane, just as heating a viscous fluid could be used to study heat flow. These were examples of “simple analogy,” in which “a second system is set up which obeys the same laws” as the first. Two systems, “arranged to be dynamically similar,” extended the engineer’s mental and physical reach, allowing real or imaginary designs to be studied and experimented with. Bush described such analogous systems as “equation solvers”—they labored on your behalf, in other words.⁴²

The network analyzer operated by the same principle of analogy, except it had more flexibility. While it too was “an exact small-scale electrical replica of the system being studied,” its plugs could “be reconnected to represent any desired power system within its limits, and thus one instrument” could address “many power networks.” The differential analyzer offered even more plasticity. It also “provides a new system, which obeys the same laws as the one under investigation.” But rather than carrying over “an entire equation” (the wind tunnel) or constants (the network analyzer), the “terms of the equation are carried over individually as such and then recombined. This gives great flexibility . . . many diverse systems may be studied by a single substitute instrument.”⁴³

[picture]

Differential analyzer with schematic diagram.

The differential analyzer offered six integrators and a collection of input tables and multipliers—units that could be flexibly interconnected with one another, allowing numerous equations to be set up and performed. This was done by inserting gears into a configurable lattice of rotating shafts. Spur gears multiplied, differential gears added, and spiral gears transmitted rotations sideways, between the longitudinal bus shafts and the cross shafts connected to units on either side. Torque amplifiers ensured that the rotations would be transmitted without losing accuracy or force.⁴⁴

A crucial feature of the differential analyzer was that it dealt with mathematics as graphics. Dealing with mathematics in terms of pictures was perfect for applied physics and science, Bush wrote, as these fields typically dealt “with functions as a whole” and resorted to numbers only “as a rather laborious means of dealing with functions or the curves which represent them.”⁴⁵

§§§

As a tool to think with, the differential analyzer didn’t just calculate; it also transformed understanding. Bush marveled that “one of the most attractive aspects of the machine” is that through experience, “one acquires an entirely new appreciation of the innate nature of a differential equation:”⁴⁶ “Formal mathematics will become a live thing.”⁴⁷ Bush told a story about the differential analyzer’s head mechanic, who had only a high school education. In building and managing the analyzer, he came to know the fundamentals of calculus and could help professors operate and troubleshoot the machine. Bush observed that he “had learned the calculus in mechanical terms . . . he had it under his skin.”⁴⁸ How did this happen?

To use the machine, one must master “an art and a technique,” wrote Bush, “just as in the formal treatment of equations . . . technique must be mastered by any one who would become proficient in the art.” Graphical thinking was key:

The first step is always the preparation of a connection diagram . . . which shows the essential interrelations . . . This is more than a diagram—it is a process of reasoning, and as such it is recommended to those who seek to impart to youth the meaning, as contrasted with the formalism, of the differential equation.⁴⁹

The diagrams elucidated the relationship between math and machine. In algebra class, we were taught to show each step of our work. This makes visible, on the page, our entire thinking process. The resulting legibility allows us to retrace our steps, check our work, and take new steps. Differential analyzer diagrams functioned similarly, allowing scientists and engineers to work through the translation of an equation, step by step, into mechanism.

[picture]

“Standard symbols for units.” Bush offered a graphical language for creating differential analyzer connection diagrams.

Differential analyzer diagrams brought multiple representations—picture, math, and machine—to heel and helped one reason through their interrelationships. These diagrams were such an important means of reasoning that they continued to be used with the Rockefeller Differential Analyzer—even though wires, not rotating shafts, now conveyed the data.⁵⁰ The metal lived on in graphical form, a reflection of the diagrams’ potency and the staying power of the differential analyzer’s underlying design.

§§§

The culture of early twentieth-century engineering played a crucial role in shaping the differential analyzer, according to Larry Owens. In this world, students were expected to master multiple “language communities,” the symbolic discourse of “physics and mathematics,” as well as the tangible idiom of the “shop and laboratory.” Mathematics education had recently been reformed to be more hands-on.⁵¹ This engineer-led movement linked the symbolic, visual, and tactile with drawing, graph paper, and “mathematical laboratories” “well stocked with clay, cardboard, wire, wooden, metal, and other models and material, and apparatus for the investigation of form, mensuration, and movement.”⁵² Fluency with graphical drawing, considered to be a “universal language,” was critical, as it enabled students to translate between these “language communities” and think clearly in the midst of complexity.⁵³

[picture]

Example connection diagram.

Analogs—electric, mechanical, optical, twirling, gusting, vibrating, flowing, surging models of the world—emerged from this engineering culture, which treasured multiple modes of representation. Trained in the tactile craft of the shop and laboratory, the symbolic worlds of mathematics and physics, and the universal language of graphics, these engineers manifested a fluency with multiple modes of representation and analogical thought. This fluency enabled them to translate their complex technical problems, like the instabilities of regional power networks, into new forms—representations more amenable to analysis, experimentation, and design. And these translations often took the form of machines, reflecting not just the utility of automation but this culture’s esteem for tangible representation.⁵⁴

Concreteness is a powerful way of learning, thinking, and doing. Bush’s story about the head mechanic learning calculus illustrates as much. Multiple representations afford multiple ways of knowing and doing, allowing novices to grasp and savor systems, like mathematics, whose feel and flavor is otherwise perceptible only to experts. “For the sake of persons of these different types,” Maxwell put it in 1870, “scientific truth should be presented in different forms, and should be regarded as equally scientific, whether it appears in the robust form and the vivid colouring of a physical illustration, or in the tenuity and paleness of a symbolical expression.”⁵⁵ Multiple representations welcome diverse minds.

Engineers treasured the “vividness and directness of meaning” offered by tangible devices, which were seen as having “considerable educational value.”⁵⁶ Numerical methods, by contrast, had “an artificiality irksome to the physically minded.”⁵⁷

In infusing their machines with their own cultural values—concreteness, graphics, and multiple modes of representation—early twentieth-century engineers profoundly inflected the forthcoming culture of computing. This style dovetailed with theories of how children learn, a resonance that would powerfully reemerge once digital computers became graphical, and especially once computer scientists invited children into their work. Pictures, symbols, and things are a potent brew.⁵⁸ We should know; graphical user interfaces are now ubiquitous. From mobile devices to desktop computers and from SimCity to spreadsheets, we point and tap our way through graphical and symbolic machine worlds.⁵⁹

Analogy

In the 1930s, Bush hired Claude Shannon, a recent graduate of the University of Michigan, to help maintain the differential analyzer and assist visiting scientists while his lab focused on a new machine, the Rockefeller Differential Analyzer, which replaced the rotating shafts with an electrical switchboard, among other improvements.⁶⁰

[picture]

Shannon’s analogical table, a convention of the genre.

Shannon would eventually work on the Rockefeller machine, which piqued his interest in relay circuits as a medium for computation; he then helped lay the groundwork for digital computers by proving, in his master’s thesis, an equivalence between relay circuits and Boolean logic. It was a new analogy. Rather than equate electric circuits with power networks, mechanical structures, or differential equations, Shannon equated circuits with binary logic. With this analogy, one could easily analyze, simplify, and design circuits that did things like recognize passwords, count votes, and add binary numbers.⁶¹

Bell Labs researcher George Stibitz, who had been creating calculating devices out of telephone relays, immediately grasped the import of Shannon’s work. Taking it up, Stibitz built more sophisticated calculators using circuits, Boolean algebra, and binary numbers, dubbing them digital.⁶² The word had referred to digits, as in numerals, a word derived from fingers and toes. It is through our hands that we begin to take the measure of and manipulate the world. Digital would now come to refer to these new calculating instruments made from circuits, logic, and binary numbers.

Analog now means the opposite of digital. In this framing, analog refers to analog computers (not digital ones), representations that are continuous (like film) rather than discrete (like pixels), and stuff that is physical (vinyl records) rather than electronic (streaming music). Consequently, Shannon’s use of analogy to describe the equivalence between circuits and logic perplexes. If analog and digital are opposites, then how can the groundwork, the very firmament of digital, be analog?

The problem is that we’ve misconstrued “analog.” As conventionally understood, analog is an ahistorical category that conflates the postwar “Electronic Analog Computor”⁶³ with prewar instruments, like the differential analyzer, which employed the principle of analogy. The differential analyzer, however, wasn’t an analog computer but an “analyzer” (or “instrument”) which enacted an analogy.⁶⁴

Another problem is the analog-digital dichotomy itself. This supposedly clean division becomes murky when we consider that “analog computers” were not simply wiped out by superior digital machines but had coexisted, often as hybrids, until well into the 1970s.⁶⁵ The analog-digital dichotomy has become so naturalized that even analog computer historians rarely pause to explain how the word analog came to signify a computer, predigital computer, or the quality of continuousness (as opposed to discreteness).⁶⁶

How did this confusion arise? How did the word analog come to be severed from analogy and pressed into service to mean the opposite of digital? The answer seems to be that computer practitioners and historians gazed into the past and retroactively applied their own categories. “Digital,” introduced by Stibitz to distinguish his machines from the differential analyzer tradition, stuck to the newfound electronic computers that processed discrete binary data (1s and 0s).⁶⁷ “Analog,” plucked from prewar usage, was given a new meaning: calculating instruments that processed continuous data. Postwar, a lively debate opened between two polarized camps of electronic computer practitioners, giving momentum to a social schism and category dichotomy: analog versus digital.⁶⁸

Grafted onto the diverse instruments, many of them ancient, that preceded digital computers, analog came to mean everything from slide rules, astrolabes, astronomical clocks, and orreries to the differential analyzer.⁶⁹ The abacus and its discrete beads, however, was claimed for digital—even though its movable beads are meaningful only as numerical analogs!

Analog, as conventionally understood, conflates multiple historically situated practices to erect a dubious universal: “analog” as continuous. Prewar analogs, however, give the game away: analogs employ analogy. If we wish to understand analogs as a category, then let us do so sensitive to their historical and cognitive dimensions.

The original meaning of analogy is mathematical, as in proportional.⁷⁰ Extended to logic, language, biology, and beyond, the word’s original meaning was preserved; only through computing, it seems, has analog been corrupted.⁷¹

Analogy plays a vital role in creativity. According to cognitive scientist Dedre Gentner, analogies establish mappings between the internal relationships of two domains. Consider the analogy between an atom and a solar system. It draws out an internal relationship: orbiting. Electrons orbit a nucleus as planets orbit a star.⁷² Relationship mapping benefits thinking and creativity in a number of ways. Analogies highlight important details, helping us focus on the relevant aspects of complex domains. Mappings help us transfer concepts between domains and make inferences. Since analogies don’t come to us fully formed, we must tinker with and reformulate domain representations in order to establish workable mappings. Such re-representation and restructuring is a creative process that forces us to reconceptualize domains in a search for coherence.⁷³ Playing with analogy and building analogs is a deeply creative, clarifying, and generative process.

[picture]

A 1965 editorial cartoon depicting the schism between analog and digital computer practitioners.

The electrical analogs of Bush, Shannon, and others were the fruits of a creative process in which multiple conceptual and material domains were tinkered with to establish analogies between them. These analogies enabled practitioners to reason about one domain in terms of another. Electrical circuits could stand for sound reproduction, bridges, electricity transmission, differential equations, and logic. The process of establishing such correspondences illuminated each domain and allowed insights to flow between them. Simply playing around with a differential analyzer diagram might yield new understanding, as did establishing the underlying analogy between math and machine that informed the differential analyzer’s design in the first place. Analogy enabled insight and synthesis. But there was more: the power of the concrete. Manifested as things, analogs performed like the domain they corresponded to. The differential analyzer performed equations. Dynamic similarity enabled this. Analogs are instruments of simulation.

Digital is not the opposite of analog. Digital is a kind of analog. And we continue to stack analogy upon analogy. Digital numbers become pixels, which become images; samples become sound. When we simulate with a digital computer—one of the most evocative and flexible analogical instruments ever devised—we still construct a dynamic system that is analogous to another. It’s analogies all the way down.⁷⁴

Perhaps analog’s conventional (ahistorical) meaning is best understood symbolically, as a signifier of loss, an aching nostalgia for the soul-satisfying vividness of the concrete.

Simulation’s Affordances

Thinking of simulations as analogs calls attention to one of their most important, although subtle, benefits: the conceptual work of domain mapping. Translating one domain into another, finding the analogy that snugly fits both, and highlighting the appropriate concepts is a profoundly creative process. It requires dexterity with multiple domains of knowledge and craft, a familiarity with machines, graphics, and subject matter alike. Analogies, in other words, enable the synthesis of more than analogs; they precipitate insight and understanding. We might end up with new concrete objects to think with, but the real prize, in the end, is how we rethink the world. This concords with the argument, made by many simulation practitioners, that understanding something requires more than playing with a simulation of it; it means building it yourself.⁷⁵

Simulations support a crucial aspect of cognition: it happens in and through the world. Just as engineers built and employed analogs, architects use scale models, military planners have used sand tables as miniature battlefields, and DNA’s double-helix structure was discovered with the aid of Tinkertoy-like models. Thinking, in other words, is an embodied process. It happens in our heads, bodies, and environment, and diverse representations help us weave it all together.⁷⁶

Simulations entail a transformation. Changes in scale and materiality enable phenomena that would otherwise not fit in a laboratory, workbench, or school—cities, power networks, bridges, trains, atoms, and airplanes—to do so. Diagrams and analogs alike put the out-of-reach within our grasp, allowing us to see and manipulate something in new ways.⁷⁷

Dynamic similarity opens the door to automation. Whether animated by electricity, fluids, or human hands, simulations perform—they labor on our behalf, evaluating power line stability, integrating equations, calculating stress in bridges, evaluating propositions, and more.

By combining a plurality of representations, simulations welcome diverse kinds of minds to otherwise abstruse subjects, as the differential analyzer did for calculus, SimCity does for urban theory, and classroom cities do for children.⁷⁸ One can enter a domain symbolically, graphically, tangibly, or even socially.

The social implications of concrete representations are profound. External models—whether a map, classroom city, computer program, or network analyzer—are sites of shared attention, collaboration, and distributed cognition.⁷⁹ Novices and experts alike can observe and learn from one another, and apprenticeship can flourish.⁸⁰ As we shall see, simulation artifacts excel at promulgating simulation practices.

Legacy

Bush’s differential analyzer was a tremendous success. They were installed and used by the military, industry, and academy across America, Europe, and Russia.⁸¹ The machine’s twirling shafts brought scientists and engineers closer to the mysterious frolics of airplanes, ballistics, electronics, power networks, engineering structures, annuities, control systems, cosmic rays, magnets, radio waves, stellar structures, earthquakes, atomic structure, and quantum mechanics.⁸² By the end of World War II, the Rockefeller Differential Analyzer had become the most important computer in the United States.⁸³

The architecture of the differential analyzer invited expansion. Bush observed that “the machine is not yet completed; in fact it is questionable whether it will ever be complete, for it can always be extended by the addition of units to cover greater order or complexity of equations.”⁸⁴ The bus of rotating shafts allowed new modules to plug in and communicate, and the whole ensemble could be reconfigured to solve new problems. The design was agnostic about what materials it was made out of; it had already been realized with varying degrees of electrical and mechanical components.

Because of this, the differential analyzer served as a springboard for new applications and improvements. Many people who would later make vital contributions to modern computing experimented with its underlying design. Engineers at the University of Pennsylvania replaced mechanical components with electronics, achieving faster speeds and greater accuracy; they would later create ENIAC, which spurred the seminal EDVAC report that established the architecture of programmable digital computers used to this day.⁸⁵ Cheap but functional differential analyzers were built out of Meccano construction toys.⁸⁶ One of the Meccano builders went on to help create EDSAC, the first EDVAC implementation, and make numerous important contributions to computing.⁸⁷ Differential analyzer shortcomings inspired Jay Forrester to create Whirlwind, the first interactive digital computer.⁸⁸ After they obsoleted it, digital computer practitioners programmed their machines to act like, or simulate, differential analyzers.⁸⁹ They adopted the term bus to refer to the hardware that interconnects digital components.⁹⁰ The differential analyzer also served as the model and inspiration for what came to be known, postwar, as analog electronic computers.⁹¹ By the mid-1950s the Rockefeller differential analyzer was obsolete, eclipsed by these rapid advances in digital computing, and scrapped.⁹²

Digital computers were a body blow to concreteness. Mathematics would no longer be felt as a live thing.⁹³ The multimodal culture of engineering in which Bush, Shannon, and so many others had grown up had birthed the digital beast, which laid waste to their analog tools. Many mourned the loss. Until his death in 1974, Bush griped about those “damn digital computers!”⁹⁴ A colleague longed for the analog machines’ “vividness and directness of meaning” and contrasted them to the “abstract affair” of digital computers whose “computational processes are fairly deeply submerged.”⁹⁵ Hands-on experience and understanding, lovingly described by Bush as a “barnyard grasp of things,”⁹⁶ a “soul-satisfying” engagement with tactile materials, craft, and clear thinking, seemed to be in full retreat.⁹⁷

Nevertheless, by century’s end, thinking with and through the world became broadly accepted in the form of embodied theories of cognition. This turn manifested in a variety of fields—games, robotics, cognitive science, object-oriented programming, graphical user interfaces—as well as Maxis titles like SimAnt and The Sims.⁹⁸ In 1991, Sherry Turkle and Seymour Papert wrote,

That the computer should be an ally in the revaluation of the concrete has a certain irony; in both the popular and technical cultures there has been a systematic construction of the computer as the ultimate embodiment of the abstract and formal. But the computer’s intellectual personality has another side: Computers provide a context for the development of concrete thinking.

Through their special “ability to make the abstract concrete,” computers support an “epistemological pluralism.”⁹⁹ But the real irony here may be that digital computers were conceived by a culture that treasured cognitive multiplicity as well as concreteness. Computers aid thought by concretizing it in myriad representations. As digital computers coevolved with the cognitive sciences, this led, full circle, back to embodied philosophies of mind. In this sense, Vannevar Bush—alongside Claude Shannon, Alan Turing, John von Neumann, and many others—may be today’s most influential yet unheralded philosophers of mind.¹⁰⁰

The differential analyzer didn’t just inspire and inform the development of digital computers; it also advanced a particular methodology: constructing tools that augmented thought and problem solving by concretizing it. The same spirit animated Memex, a proposed artificial store of “books, records, and communications”¹⁰¹ that could be instantly recalled and associatively cross-linked, like our own memories.¹⁰² Bush suggested the idea in “As We May Think,” a 1945 essay that inspired hypertext,¹⁰³ the World Wide Web, the underpinnings of Internet search engines,¹⁰⁴ online encyclopedias, and Douglas Engelbart’s seminal research into the possibilities of interactive computing.¹⁰⁵ Just as Bush had rescued engineers and scientists from mathematical thickets, freeing them to focus on the more profound and creative aspects of their work, he now envisioned tools that would help researchers manage and represent knowledge more broadly. The humanities, and who knows what else, might also be swept up in the transformative project of inventing new “aids to reasoning.”¹⁰⁶

Bush didn’t just influence the technology and culture of digital computing. He also helped devise the institutions that funded its development. As chair of the National Defense Research Committee, Bush architected a military, academic, and industry alliance that supplied America’s World War II efforts with crucial technical breakthroughs. The approach he championed would later be adopted by the military and civilian agencies that pumped money into computer research and development for decades and decades to come.¹⁰⁷

II Paving the Road to SimCity

3

System Dynamics: A Society of Bits

Our social systems are a great deal more complex than the information-feedback systems that have already been mastered in engineering. Are we ready to tackle them?

—Jay Forrester, 1960¹

Although it was too late to save the Galactic Empire, the Seldon Plan might hasten its revival. Two foundations populated by scientists and engineers would be seeded at opposite ends of the galaxy. Like medieval monasteries, they would preserve the know-how that would shorten the forthcoming dark ages. Foreseeing that one foundation would face a series of ten crises over 1,000 years, Hari Seldon made holographic recordings to guide them through each Seldon Crisis.

Psychohistory enabled Seldon to foresee the fall of the Galactic Empire. By combining mathematics, statistics, history, and sociology, psychohistorians could predict peoples’ behavior and perhaps steer the course of history. Forecasting an individual’s behavior, like that of a single molecule, was impossible. But a gas, which is made of innumerable molecules, can be modeled statistically—which is how psychohistory treated societies.

These events transpire in the Foundation novels, which Isaac Asimov began writing during World War II (while pursuing a graduate degree in chemistry).² But psychohistory wasn’t pure science fiction.

Published in 1972, Limits to Growth foresaw a collapse of global civilization if human development was left unchecked. The report on the “present and future predicament of man” sparked widespread debate, from academic journals to Playboy and the Wall Street Journal.³ Instigated by the Club of Rome, an international team of “scientists, educators, economists, humanists, industrialists, and national and international civil servants” that had gathered to discuss the “world problématique,” the book presented the results of a global computer simulation that tracked the interwoven dynamics of population, resources, pollution, technology, and capital.⁴

Psychohistory was founded by Hari Seldon, but the techniques used in Limits to Growth were the brainchild of Jay Forrester,⁵ whose early career was spent engineering control systems and computers for war. Postwar, Forrester brought his expertise to a new project: computer simulations that addressed the problems of businesses, cities, and the global predicament of mankind.

§§§

SimCity, alongside Will Wright’s other best-known work, The Sims, are unusual in that they model social systems. Mechanical systems like balls, bullets, bridges, and trains can be modeled with software and, as we’ve seen, with electromechanical analogs. But social systems are not so straightforward. How does one simulate a society on a computer?

To build SimCity and address this riddle, Wright borrowed heavily from Forrester’s work. The culmination of Forrester’s simulation career was, in many ways, the Limits to Growth. He had begun in the 1950s with simulations of industrial systems. Over time, he took on cities and eventually the world. Forrester’s approach was straightforward: aggressively abstract a phenomenon into a system of feedback loops and variables that could stand for everything from warehouse inventories to socioeconomically stratified populations. These levels (values) and rates (flows) were then programmed into a computer, which simulated their evolution, allowing one to see what happened to a model company, city, or planet. This approach undergirds Limits to Growth, SimCity, and much else and is so versatile that it can be used to model just about anything—as Wright and many others have done.

This chapter follows the evolution of Forrester’s simulation practice, which he eventually called system dynamics. System dynamics offers a ready-made analogy, a simulation tool kit for modeling the world in a particular way. A phenomenon is interpreted from a top-down perspective that represents entities, such as people, in aggregate, and interlinks them through feedback loops. While Forrester intended system dynamics as a serious simulation practice, Wright combined it with a highly visual, vivid, and responsive aesthetic informed by computer games, toys, and the graphical user interface. To make SimCity, Wright reconciled the top-down (and nonspatial) representation of system dynamics with other approaches, like cellular automata and game sprites, where behavior emerges, bottom-up, from interacting parts situated in space. As a result, the system dynamics approach helps explain not only the dynamic feel of SimCity, but the underlying assumptions and mechanisms that undergird its world, as well as Wright’s broader design sensibility. SimCity’s design, in other words, cannot be understood without grasping Forrester’s work.

Making sense of system dynamics means following the influences that shaped it. Forrester was an engineering émigré in the foreign land of social science, but his journey was part of a mass migration in which experts brought tools developed for war to peacetime problems. As immigrants do, Forrester brought the old country with him. He still saw the world in terms of feedback loops, a reflection of his prior work at the MIT Servomechanism Laboratory, which developed control systems that helped aim guns and keep radar antenna level while the ships they were mounted on pitched and rolled.⁶ His use of computer simulation reflected his experience leading the development of Whirlwind, a landmark digital computer originally intended as a flight simulator. And Forrester’s new home, the MIT School of Industrial Management, directed his attention to new problems: management and social systems. Operating as an interdisciplinarian, Forrester generalized and refined his approach, which blossomed into a robust community of practice that persists to this day and includes conferences, a professional society, commercial software, and a journal.⁷

Servomechanisms

Modeling the world in terms of feedback loops sounds very much like cybernetics, which Norbert Wiener described in 1948 as the “entire field of control and communication theory, whether in the machine or in the animal.” The term cybernetics derives from the Greek word for steersman. (Cybernetics, in turn, yielded cyborg and cyberspace.) Wiener believed that the theory was applicable to everything from engineering to biology, psychology, and sociology. Many phenomena, in other words, could be viewed through the lens of mathematics, feedback, and control.⁸

It is curious, then, that Forrester identified system dynamics not with the cybernetic tradition, but with an older tradition: servomechanisms.⁹ A servomechanism, or servo (Latin for slave), is a device that automatically guides the performance of a machine or process. Automated control systems, like the mechanical governors used to regulate nineteenth-century steam engines, have a long history. Navies employed fire control systems for tracking and leading targets since before World War I, and autopilots steering boats and planes were introduced between the wars.¹⁰ Although such automatic control systems had existed for quite some time, it took a leap of insight to see these diverse practices as related. In 1934, Vannevar Bush’s student Harold Hazen, now a professor at MIT, published “Theory of Servo-Mechanisms,” a formative paper for the field of control theory.¹¹ He wrote that servomechanisms were used in thermostat-controlled heating and for “ship-steering” as well as

speed control of steam turbines and water wheels by governors; the control of these governors by master clocks or by power-indicating instruments; the stabilization of ships by gyroscopes; the operation of gyro-compass repeaters; the automatic stabilization and guiding of aircraft; and in fact the automatic recording or control of almost any measurable or measurable and controlable physical quantity. . . . A servo-mechanism may thus be defined as a power-amplifying device in which the amplifier element driving the output is actuated by the difference between the input to the servo and its output.¹²

A thermostat servomechanism actuates a heater so that a room reaches a certain temperature; it tries to close the difference between measured and desired temperatures. A ship autopilot, similarly, is a servomechanism that actuates a rudder in response to the difference between the ship’s measured and desired heading. A servomechanism uses feedback to govern some quantity.

Hazen’s seminal paper was indebted to Bush and his department. Hazen had led development of the network analyzer, so he was familiar with the practical problem of feedback and control within power grids, and he had worked with Bush on the integraph, a precursor to the differential analyzer, which used servomechanisms to amplify signals, drive shafts, and move a pencil. With this device, he had employed a “‘back-coupling’”¹³ technique—a kind of feedback loop—to study the vacuum-tube oscillator, another kind of feedback circuit. Not only was Hazen intimate with diverse feedback systems, but he was skilled in the analog arts, which helped him think abstractly across domains. In the 1930s, as MIT’s institutional priorities shifted toward basic science, Bush pushed Hazen to publish his work on servomechanisms as a generalized theory.¹⁴

Consequently, the US Navy Bureau of Ordnance asked Hazen to offer a course on servomechanisms to its officers. But circumstances dictated that Gordon Brown, a former Hazen student, take over. Bush, who had been dean of MIT’s engineering school since 1932, moved to Washington, DC, to head the Carnegie Institution, and in the resulting shuffle, Hazen was promoted to head of the electrical engineering department and Brown assumed his servomechanism duties. Through his work with the Navy and military contractors, Brown developed a new servomechanism paper that became so foundational to wartime fire control that its publication was suppressed by the government. Brown’s MIT Servomechanism Laboratory, which also received funding from the National Defense Research Committee (NDRC), which Bush chaired, became a vital center of American control systems research.¹⁵

In Bush’s estimation, “the anti-aircraft problem” was of the utmost importance. This had prompted him, in part, to form and lead the NDRC. Six months later, the Pearl Harbor attack underscored the threat’s magnitude. Attacking airplanes had to be tracked and their motion anticipated so that they could be intercepted by precisely arced shots. Further complicating the problem were the powerful guns themselves, whose heft and inertia made them hard to precisely control and were mounted on swaying gunships that floated on pitching seas. An entire system of perception, calculation, communication, and articulation—people, gyroscopes, multi-ton gun turrets, calculating machinery, communication systems, and control logic—had to cohere with fine precision. Solving these problems led to syntheses that articulated the fundamental problem of communication, control, and feedback in newly general terms (and technologies), setting the stage for postwar system sciences such as cybernetics and system dynamics, as well as information theory and digital computing.¹⁶

Brown’s recruits for the Servomechanism Lab included Jay Forrester, a recent electrical engineering graduate from the University of Nebraska.¹⁷ At one point during the war, Forrester shipped out to repair an experimental system on a Navy vessel in the Pacific theater—and experienced a nighttime attack that disabled the ship and killed nine sailors.¹⁸ As a veteran of Brown’s Servo Lab, it was natural for Forrester to align his work with servomechanisms—a mature discipline that predated cybernetics by well over a decade.

§§§

Getting a feel for control systems will help us understand how and why Forrester saw the social world in terms of servomechanisms. It will also help in understanding why Forrester, and later Wright, would intentionally introduce control instabilities into their simulations for representational effect. Such instabilities imbue SimCity with both naturalism and a capricious playful aesthetic, like a bouncing ball.

Imagine trying to catch a baseball. You watch it arc through space, anticipate its location, and move your body into position. This unremarkable act is, in fact, exquisitely complex. You must overcome perceptual limitations such as the speed at which signals travel from eyes to brain to muscles (latency), how these signals degrade as they travel, and the vagaries of perception (noise). Your body takes time to move (more latency), and you may already be moving in the wrong direction (inertia). To compensate, you anticipate the future location of ball and body, running and reaching for the ball’s expected location (leading), all while unconsciously accounting for your own latency and inertia. To protect against noise, you calm yourself and track the ball’s location over time and make controlled movements (dampening).

This can go horribly wrong. Run too fast or far, and you overshoot the mark. Turn around too quickly, and you might fall into the mud. If you are too cautious, you won’t make it in time. And if the sun is in your eyes, tracking becomes harder.

Consider the anti-aircraft problem. Perception, whether through human eyes or machine radar, is noisy, and noise complicates tracking, as does an enemy airplane’s evasive maneuvers. Timing is crucial: you want to aim your gun at where the target is going, not where it was. But every system has some latency, and this must be compensated for. Compensate incorrectly and lead too far, and you miss the mark. Electrical signals articulate the guns, but those signals must zip through electrical wires and then be amplified into powerful movements. But relaying and amplifying signals can lead to distortion. Properly articulating a multi-ton gun turret means accounting for inertia, much like trying to precisely park a heavy and fast-moving truck on a dime. It’s easy to go too far. Reverse direction to correct your mistake, and you might go too far again. Repeat, and you have an oscillation, or “hunting,” around a desired goal—much like a drunk weaving back and forth across a line during a sobriety test. With too much instability, the drunk’s stumbling movements amplify until they fall over, just as reinforcing oscillations can cause a machine to shake itself to pieces.¹⁹

These sorts of challenges are inherent in any control system. Apply this schema to a social system, like an economy or city, and analogous dynamics can be found. Housing shortages motivate a city to approve development. But builders need time to secure financing, and construction takes time (latency). As housing costs go up, people might move to another state. By the time new homes are ready, housing may already be in oversupply, especially if speculators caused overdevelopment (leading and inertia). A boom-and-bust business cycle may be nothing more than hunting behavior—oscillation around a desired goal.

Whirlwind

Forrester’s digital computing career began with simulation. By the early 1940s, the Navy had invested vast sums in specialized flight trainers—simulators that put flight crews in mock cabins and cockpits so they could learn to fly existing warplanes. Extending this line of work, the Navy envisioned a more generalized device that would operate as both a flight trainer and an aircraft simulator that performed the kind of studies typically done in a wind tunnel. This dual-purpose simulator, moreover, wouldn’t be limited to just one aircraft model, but could simulate numerous proposed designs, thus serving both training and design objectives. Responsibility for the project, which became known as the Aircraft Stability and Control Analyzer (ASCA), ultimately landed with Brown’s Servomechanism Laboratory, with Forrester at the helm.²⁰

While Forrester’s team originally intended to use a differential analyzer-style device as the computing element, they found that this approach couldn’t provide both the requisite flexibility and real-time performance. Through a gradual shift in priorities and funders, ASCA morphed into an ambitious real-time digital computer initiative called Whirlwind, now funded by the Office of Naval Research—one of the postwar institutions made in the image of Bush’s NDRC. Then, again, under the new sponsorship of the US Air Force, the project pivoted to the emerging political climate of the Cold War and became the Semi-Automatic Ground Environment (SAGE), a massively ambitious command-and-control system for tracking and intercepting Soviet airplanes.²¹ The machine’s ongoing morphing reflected the flexibility of a modern digital computer, enabling it to enlist in ever-changing duties, as well as its origin in interactive real-time systems like fire control and flight training.²²

Mechanized war is a complex undertaking. Developing and manufacturing warships, machine guns, fire control systems, ship stabilizers, autopilots, computers, nuclear weapons, and the like requires diverse expertise. And wielding these new instruments of war, from radar and cryptography to bombers and submarines, obligated cunning strategic analysis. Bomber formations, convoy sizes, and antisubmarine tactics needed optimizing. Game theory, symbolic logic, feedback control theory, statistics, network theory, and probability—to name a handful of analytical traditions—were conscripted into this effort. The crucible of World War II formed new disciplines: operations research, systems engineering, and project management. Gargantuan multidisciplinary efforts like the Manhattan Project, backed by practically limitless resources, engendered not just newly sophisticated technologies, but new analytic approaches and management strategies.²³

After the war, enlisted men demobilized and reentered civilian life, as did technologies like fire control computers, rockets, and atomic weapons, which were repurposed for peaceful ends like nuclear power and space exploration.²⁴ The SAGE computer system, developed to track and intercept hostile aircraft, formed the basis for a computerized airline reservation system named SABRE.²⁵ Bush’s “As We May Think” struck Douglas Engelbart like a lightning bolt, who read it in a Red Cross library while stationed in the Philippines, inspiring him to pursue human-computer augmentation research.²⁶ As mathematicians, economists, statisticians, engineers, and scientists demobilized, they brought home new tools and expertise. Robert McNamara and his “Whiz Kids” brought know-how honed through optimizing bombing runs to the Ford Motor company, where he eventually rose to the presidency. (He later became secretary of defense and then head of the World Bank.) The US Air Force established multiple operations research groups, such as the nonprofit RAND Corporation. Intragovernmental rivalry, as well as growing resistance by some staffers toward the Vietnam War, stimulated RAND to apply their analytical tools to Lyndon B. Johnson’s Great Society, where they might address social ills like segregation.²⁷

It was in this climate that the president of MIT invited Forrester to become a professor of management at the MIT School of Industrial Management (later renamed the Sloan School). He had, after all, managed a multibillion-dollar project for the military, and he was deeply familiar with the new digital computing machines that were gradually reshaping the business landscape. Forrester’s management skills and computer expertise would transfer nicely. The recently founded school, with a multimillion endowment from Alfred Sloan (the former CEO of GM), was predicated on the notion that a technical institution would produce a unique type of management school, different from and perhaps better than those at liberal arts institutions. Besides, it was 1956, and Forrester “felt the pioneering days of digital computers were over.” He was ready for a new challenge.²⁸

Forrester’s Analogy

How would Forrester address his expertise to the problems of business? Multiple paths presented themselves. One option was operations research. But Forrester found it lacked “compelling practical importance”²⁹ and rarely “made the difference between corporate success and failure.”³⁰ But what, really, could feel as thrilling as installing military equipment in a live theater of battle? Another option was applying computers to business management. But, alas, this field, like the state of digital computing in 1956, also seemed mature; it would never match the excitement of building something like Whirlwind. And computers were already in widespread use in manufacturing, banking, and insurance. There was hardly any room, it seemed to Forrester, to make a meaningful impact.³¹

Forrester soon found a problem that piqued his interest. “Chance intervened when I found myself at times in conversation with people from General Electric.”³² They were confused as to why their Kentucky factories experienced cyclically fluctuating orders. Sometimes multiple shifts were required to manufacture enough household appliances to meet market demand, yet in other years, plants would lay off half their staff. “It was easy to say that business cycles caused fluctuating demand,” Forrester reflected, but this explanation left him feeling “that something was wrong or incomplete.”³³ After interviewing them about their hiring and inventory practices, Forrester sat down and made a “simulation using pencil and paper on one notebook page”:

It started at the top with columns for inventories, backlogs, employees, orders, and production rate. Given these initial conditions and the policies they were following, one could decide how many people would be hired in the following week. This gave a new condition of employment, inventories, and production. Each line could be computed from the preceding line. It became evident that there was potential for an oscillatory or unstable system whose behavior was entirely internally determined.³⁴

Business cycles were a superfluous explanation. Fluctuating production cycles could be explained internally, within the logic of a closed system, rather than externally, as a result of mysterious market forces. The manufacturing system was engaged in “hunting” behavior—chasing a number but never arriving. It looked, in other words, like a control system in need of tuning—a servomechanism out of whack.

Forrester had stumbled on a direction aligned with control theory. In a 1956 faculty research seminar note, he articulated the philosophy that would propel his economic and social models. Traditional economic models failed to capture

the structural form of the regenerative loops that make up our economic system. The flows of money, materials, and information feed one another around closed re-entering paths. In one of these paths a disturbance causes a cascade of events that return to affect the initial disturbance. For example, such a system often has characteristics which can convert a single isolated impulse of disturbance into a series of oscillations. . . . The behavior of such loops (their tendency to amplify or dampen disturbances, their natural frequency of oscillation, their ability to shift the phase or timing of events which feed into them) is determined by characteristics which are usually omitted from the models in the literature.³⁵

Forrester searched for the right “source of analogy”—not physics, as its laws “usually relate to open systems rather than information-feedback systems,” and fragments rather than wholes. A “better analogy,” he wrote, would be found in the “engineering and military models of telephone systems, of aircraft, of military systems, and of missile controls.” Economic systems, in other words, with their feedback loops and built-in latencies, were like the servomechanism systems he knew so well. Forrester drew a straight line from Hazen and Brown’s seminal control theory to “industrial practice . . . a system of flows dealing with materials, money, manpower, orders, and capital equipment, all of which are integrated by an information flow and decision-making network. This is a dynamic information-feedback system viewpoint.”³⁶ As an economic theory, Forrester’s approach deviated from neoclassical economics, which emphasized equilibriums, not dynamism.³⁷

In devising this new analogy, Forrester established a conceptual fit between two domains: control systems and economics. The idea of a business was reconceptualized to fit his well-developed understanding of servomechanisms.³⁸ Just as a servo might actuate a ship rudder to achieve a desired heading, factories adjusted production to meet perceived demand. Manufacture too much, and the factory becomes idle and layoffs ensue. As with any control system, delays in perception or actuation could led to overshooting and oscillation. Resistance to change, whether “habit” or “production lead times,” was like the heft and inertia of a multi-ton gun mount. “Fluctuating inventories” and “bank balances” became “capacitance or reservoir effects.” His analogy accounted for both structure and behavior.³⁹

Forrester was now able to define his research program. First, he would apply servomechanism concepts to economics. Second, he would harness advances in simulation, which digital computers had made extraordinarily cheap, underwriting advances in the “art of simulation, wherein an analog of a real system is set up and operated at an accelerated time scale,” thus enabling the study of “complex, non-linear systems.” Servomechanisms and computer simulation had matured through military applications and were now ripe for new applications.⁴⁰ He would use digital computers to simulate economic systems using two layers of analogy: digital computers became control systems, and those control systems became social phenomena.

Industrial Dynamics

Two years later, in 1958, Forrester published “Industrial Dynamics: A Major Breakthrough for Decision Makers” in the Harvard Business Review. Management was transforming, he wrote, into an “exciting, dynamic, and intellectually demanding profession” whose “goals are rooted as deeply in the public interest as the broad objectives of the legal, medical, and engineering professions.” (The rhetoric makes one wonder who exactly Forrester was out to convince: managers or himself.)⁴¹

The immodestly titled article claimed that feedback control loops “are fundamental to all life and human endeavor, from the slow pace of biological evolution to the launching of the latest satellite,” and create never-ending regenerative processes that “keep the system in continuous motion.” Business was no different. Management’s task was “to interrelate the flows of information, materials, manpower, money, and capital equipment,” a project that computers, by simulating businesses as “systems of information feedback control,” could transform. By translating a business into a feedback control system, its nonlinear behavior could be studied through simulation, thus helping managers eliminate unwanted instabilities.

Polished graphics supported Forrester’s translation of business into the domain of control theory, illustrating how oscillations, instabilities, and amplifications were inherent to a business’s organization. These figures underlined how Forrester’s program, at its core, was about understanding the ways in which feedback structures produced dynamic behaviors.⁴²

This first foray produced an unintended side effect: reusable technical infrastructure. Envisioning that managers would collaborate with simulation experts,⁴³ Forrester had partnered with Richard Bennett, an experienced programmer, and asked him to code a set of equations. Realizing that this would entail unnecessary labor, Bennett chose a different tack and instead created a compiler called SIMPLE, for Simulation of Industrial Management Problems with Lots of Equations—a title that cheekily explains why Bennett opted to build a compiler rather than code Forrester’s equations by hand (and perhaps also their short-lived collaboration.)⁴⁴

A compiler is more than a software tool; it abstracts a mode of thought, defines a new language for expressing ideas in, and saves labor for those aligned to its grain. By manifesting Forrester’s analogy as a ready-made kit, SIMPLE and its descendants would accelerate the expansion of Forrester’s simulation practice.⁴⁵ Forrester was establishing techniques for seeing, thinking about, and representing the world. The stage was set for new models to be built, and by an increasing number of people.

§§§

By 1960, Forrester and his collaborators had established a “fundamental concept of systems structure, which realistically characterizes information-feedback systems, be they technical, industrial, or economic systems.” This fundamental concept was two entities: levels (a.k.a. state or stocks) and rates (a.k.a. flows or valves).⁴⁶ Levels represent an accumulation of something, like inventory or population, that change over time as a result of inflows and outflows described by rates. Each rate is defined by an algebraic equation. Into this “straightforward model structure,” writes Forrester, “we can cast any combination of flows, time delays, amplification, structure, and nonlinear decisions.”⁴⁷

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Some “Industrial Dynamics” figures.

The MIT School of Industrial Management helped Forrester cultivate a community of practice. The straightforward analogy of levels and rates, combined with a language and compiler, eased model building and enabled more people to pick up the practice. Projects were done by staff (sponsored by the Ford Foundation) and students in collaboration with industrial organizations (that sponsored them). Companies like Texas Instruments and Kennecott Copper sent emerging managers to the school’s year-long master’s degree in management program. Some of these students, who often had engineering backgrounds, used industrial dynamics to simulate aspects of their respective industries.⁴⁸

Teaching industrial dynamics prepared the way for Forrester’s publication, in 1961, of Industrial Dynamics, a textbook and how-to manual.⁴⁹ It made public, for the first time, the fundamental techniques of industrial dynamics, a visual notation for diagramming them, the improved DYNAMO compiler, and the precise simulation mechanics.⁵⁰ The book was deeply technical, articulating both a philosophy and a technical methodology. Whereas the Harvard Business Review article addressed itself to managers and attempted to persuade them of the utility of this new style of analysis—best “handled by experts,” Industrial Dynamics explained in detail how to be such an expert. Now people with the inclination and means, situated beyond the MIT School of Industrial Management, could adopt his techniques. The book offered an abstracted, codified, and reusable set of techniques.

Industrial Dynamics introduced a graphical language to help practitioners translate a business into levels and rates. It had four core elements:51

Levels. Boxes indicated a quantity of stuff such as inventory, money, or people. Special “sources” and “sinks,” shaped like little clouds (not shown), indicated levels outside the model. If modeling a factory, such externalities might include consumers or sources of raw materials.

Flows. Solid lines indicate the flow of material between levels.

Rates. “Valve” symbols represent functions that regulate flows.⁵²

Information. Dashed lines indicate informational flows.

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Forrester offered a graphical language for making flow diagrams. Left: Model diagram from Industrial Dynamics. Right: A closed-loop figure Forrester later used to explain his ideas.

These diagrams helped bridge the gap between model and represented domain. The differential analyzer’s “connection diagrams” offered graphical primitives—a construction set—for reasoning through the interrelation between mathematics and machine. Forrester’s “flow diagram”⁵³ functioned similarly, concretizing simulation elements in a way that aided analysis, design, and communication, and reinforcing the simulation schema across a community of practitioners. Industrial Dynamics didn’t just articulate a particular method and technology of simulation but a way of thinking about the world.

§§§

Simulation makers, Forrester taught, practice a kind of “intuitive” “‘art.’”⁵⁴ Bush had also emphasized the mastery of “art and a technique,”⁵⁵ but Forrester’s “art” was far more intuitive and free-form. To begin, one defined “the goals and the questions to be answered.”

Next, the factors that bear on the answers must be visualized, interrelated, and described. This should not be a formal step of mere statistical procedure but rather the point where intuition and insight have their greatest opportunity. This is a step for the philosophical, sensitive, perceptive observer. . . . Here we need experience, alertness, and a strong intuitive feel for the nature of information-feedback systems. We need to look in the proper places for the policies, delays, and information sources that determine dynamic system behavior.⁵⁶

The next stop was articulating “an unambiguous, clear, verbal description of the factors and their interrelationships” followed by a mathematical model written in DYNAMO that could be simulated by the computer.⁵⁷

Forrester believed that deciding what to include and exclude from a model was vital. Yes, it was a highly contingent and subjective procedure, but the practice dictated that what was inside the model would produce the dynamic behavior of interest, not external factors like business cycles or fluctuating consumer demand. “In practice,” Forrester wrote,

there will be no such thing as the model of a social system, any more than there is the model of an aircraft. An airplane is represented by several aerodynamic wind-tunnel models for various purposes, plus cockpit arrangement mock-ups, models for maximum stress loading, etc. In designing a dynamic simulation model of a company or economy, the factors that must be included arise directly from the questions that are to be answered.⁵⁸

Forrester preached expertise and intuition over hard data. Unencumbered by a rigorous methodological adherence to data and aided by the aggressive abstraction afforded by the generic concepts of levels and rates—easily mapped to quantities from any “intellectual discipline”—Forrester’s simulations could easily be repurposed to represent any kind of feedback system. Magnificent possibilities awaited: “DYNAMO is suitable for the simulation of any information-feedback system, be it biological, chemical process, river basin control, economic, or industrial.”⁵⁹

He began to see psychological and social forces as part of the picture: “We must feel free to include technical, legal, managerial, economic, psychological, organizational, monetary, and historical factors.”⁶⁰ Serving on the board of Digital Equipment Corporation (DEC)—a company that sold machines descended from Whirlwind’s design and whose founders included engineers who had worked for him on Whirlwind—prompted Forrester to stretch his thinking beyond “physical variables like inventory into much more subtle considerations” such as leadership style and organizational traditions.⁶¹

Forrester’s simulation practice was poised for growth. He had defined an analogical domain into which practically anything could be mapped, established supporting technologies and techniques, and disseminated these practices through classes, publications, and software. What would come next?

Business Games

Forrester didn’t have the only business simulation game in town. Some people, in fact, used business simulation games to teach management. Forrester took pains to distinguish his approach. While “they superficially appear very similar . . . educationally they move in the opposite direction.”⁶² At stake were radically different conceptions of agency and learning. Playing a simulated world entailed superficial learning, while building such a world entailed deep understanding.

Business games also emerged from the postwar demobilization and offered a contrasting synthesis of management, computing, and simulation. In the mid-1950s, the RAND Corporation developed a simulation game, Monopologs, for the Air Force, in which players performed inventory management of high-value widgets. “The game simulates reality, but compresses time and space so the players can conveniently experience some essential problems of management.” RAND’s Logistics Department had considered an approach like Forrester’s, but rejected a total simulation of the Air Force’s supply system as impractical; a game, however, allowed a person to “get a feel for some of the consequences of inventory management decisions.”⁶³ Game design was an excellent bedfellow to game theory, with which RAND researchers were well acquainted. And besides, games have served war since time immemorial. Monopologs was operations research and management training in the guise of a board game. Although the scenario was Air Force-centric, its designers noted that it dealt with “the universal problem of inventory management as a whole, both military and non-military.”⁶⁴

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Monopologs (1957) system. Management simulation game developed by the RAND Corporation.

Such games quickly filtered into the world of economics and business management.⁶⁵ Management was management and supply chains were supply chains, whether home appliance manufacture or the logistical gauntlet of bomber parts. As this effort matured, digital computers assumed simulation duties that had once been done manually.⁶⁶

Management game designers also made the leap from economics to broader social themes. Responding to a call by Richard Meier to broaden the focus of simulation games beyond management and war into education and training, Bruse Moncreiff initiated the Sumerian Game (1965). In it, the “student assumes the role of a priest-king of a Sumerian City-State in the late fourth millennium.” It was a creative synthesis of computer simulation gaming, history, and educational philosophy, as well as a “protest against the growing tendency in school curricula to ignore the pre-Greek civilizations.”⁶⁷ (Meier was also an important mentor and collaborator of Richard Duke, whose multiplayer city simulation games influenced Doreen Gehry Nelson’s simulated cities. Meier validated Duke’s use of games for serious purposes and encouraged him to use computers and apply to the Ford Foundation for money to pay for it.⁶⁸)

Forrester was not a fan. Besides the fact that business games were a competing line of work, there were irreconcilable philosophical differences. Games were “designed to reward the kinds of decisions that the game designer thinks are proper,” instilling opaque values and beliefs into players—a critique later leveled at SimCity.⁶⁹ The model was a “black box,” and even if it was good model, one could not “fathom its complexities by this type of external teasing.” Games also warped reality by dramatically compressing time. (But so did Forrester’s models.) Yes, they fostered emotional involvement, but for Forrester, that wasn’t necessary for creative management. For Forrester, intuitive decision making was a poor substitute for knowing. “In short, the management game is a game.”⁷⁰ (Despite Forrester’s critique, he and his colleagues had dabbled in such business games. The approach paid off and is still used today in the Sloan School as the Beer Game, a tabletop simulation of the beer industry’s supply chain, as part of new student orientation.⁷¹)

Industrial dynamics and business games offered radically different conceptions of human agency. In a business game, participants played the model. In industrial dynamics, participants built the model. They wrestled with the analogical mapping between levels and rates and the represented phenomenon. Rather than use simulations to train managers, he would teach managers to simulate.

Urban Dynamics

The 1960s was a time of unrest. The Vietnam War and civil rights movement laid bare deep-seated social divisions and injustices. Civil disobedience and demonstrations boiled over into hot conflicts like the Watts rebellion in Los Angeles. In response, writes Jennifer Light, President Lyndon B. Johnson enjoined “military strategists, systems analysts and social scientists” rather than military troops. Defense think tanks were ready. The “perceived failures of urban renewal” of the 1960s had already stimulated demand for a “more scientific approach to managing cities.”⁷² To address the domestic crises of the 1960s, intellectuals, administrators, and defense contractors adapted expertise and techniques developed for defense purposes toward domestic social ills.⁷³

At the end of 1967, RAND hosted the Workshop on Urban Problems.” It was jointly funded by the Ford Foundation, which also sponsored Richard Duke’s city simulation research and paid for the computer time Forrester used to develop his next project: urban dynamics.⁷⁴

In January 1968, John F. Collins completed his second four-year term as mayor of Boston, chose not to run again, and began what would become a thirteen-year visiting and consulting professorship at MIT. Collins was regarded as a progressive mayor who oversaw a large-scale urban redevelopment program—which included the wholesale razing of a vibrant neighborhood.⁷⁵ Collins was initially given a year-long appointment as a visiting professor in urban affairs—affiliated with political science, civil engineering, and the Sloan School—as part of MIT’s interdisciplinary push into urban studies. A victim of the 1950s polio epidemic, Collins needed an accessible space, and the office next to Forrester’s qualified (its usual occupant was on sabbatical).⁷⁶

Collins drew Forrester’s attention to “the crisis of the cities, the greatest domestic crisis to challenge America in a century.”⁷⁷ In talking to Collins, Forrester “developed the same feeling” as when he spoke to corporate executives, “an uneasy sense that something was wrong or incomplete.” Forrester suggested they pool their respective expertise in cities and in modeling, to which Collins agreed:

I told him we would need advisers who knew a great deal about cities from personal experience, not those whose knowledge came only from study and reading. We needed people who had struggled with cities, worked in them, and knew what really happens. . . . Collins listened and said, “They’ll be here on Wednesday afternoon.” . . . He delivered the people and it was out of the following discussions that Urban Dynamics developed.⁷⁸

Forrester came to these conversations “knowing the conceptual nature of the structure being sought,” into which the “specific details of the structure” gleaned from expert interviews would be “fitted.”⁷⁹ The conceptual structure into which this expert knowledge would be fitted, of course, was the servo-inspired analogy of levels and rates. Cities would be artfully and aggressively abstracted into their essential feedback structures. The models would be unencumbered by a rigorous adherence to quantitative data, or really any kind of data, for that matter. Reliance on expert interview, qualitative research, and industrial dynamics explains why Urban Dynamics, Forrester’s 1969 book, includes a mere six citations—five to Forrester’s own work (and one to psychologist Kurt Lewin).⁸⁰ The resulting model, “a system of interacting industries, housing, and people,”⁸¹ would heavily influence SimCity’s linchpin economic model.

The high altitude of abstraction intrinsic to industrial dynamics, alongside a maturing practice of simulation making, enabled industrial dynamics to morph into something new. Urban Dynamics, for the first time, clearly staked out applications beyond economics and tapped into the expansive potential that had lain dormant all along.

§§§

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Urban Dynamics figure, “The urban area in its limitless environment.”

Forrester began by delineating the model’s boundaries. Choosing the right “system boundary” would allow him to isolate the “urban interactions” that caused growth and stagnation.⁸² Cleaving inside from outside reflected Forrester’s closed-system approach: internal dynamics, not external forces, drove behavior.⁸³

A rare figurative illustration evinces this closed systems perspective.⁸⁴ The city has been reduced to a self-contained microworld. Its vivid inside/outside dichotomy is punctured by clearly defined avenues and vehicles that gird the model to its environment. The simulation focuses on the city in the middle, in which the minutiae of individual citizens, as well as the external environment, have been abstracted away. Beyond the city, we find more boundaries: clouds cleave to the horizon, echoing Forrester’s use of diagrammatic clouds to indicate externalities. Water frames the illustration from below. The outside world hovers at the periphery, peeking above the horizons of water and sky, outside the frame of the illustration and far outside the model’s scope.

The idea of a closed system was integral to Forrester’s approach, but the issue of unaccounted externalities remains: the closed urban area was part of a “limitless environment.”⁸⁵ Materials and people flowed in and out, but this was an open loop: pollution has to go somewhere; someone who has emigrated cannot affect the urban area. They have disappeared into a diagrammatic cloud. While every model has externalities, some of them, like Forrester’s treatment of race, politics, and homelessness—not to mention the conceptual moorings of his entire approach—are less obvious.

Phyllis Fox, who led development of DYNAMO and held a more robust academic credentialing than Forrester—including a ScD from MIT in mathematics—notes that she, like most of Forrester’s staff, was not a believer. Servos tend to have cycles, and Forrester could match those to whatever he wished. He also used extrapolation, a “notoriously problematic, and unstable” method. Some staffers raised concerns, “but it was a hard wall to get through.” Fox kept quiet; it was a job, after all.⁸⁶

§§§

The Urban Dynamics book is an exemplary piece of technical writing that brilliantly interlinks prose, code, diagrams, and graphs. While a full explanation of the model is far outside our scope, what follows is a high-level summary that highlights mechanics and dynamics relevant to understanding SimCity.

The Urban Dynamics model is not of Boston, San Francisco, the Lower East Side, or of any city or neighborhood in particular, but urban areas in general. The model has three core subsystems: business, housing, and population. These occupy the top, middle, and bottom thirds of Forrester’s overview figure.

The top third of this figure represents businesses as either “New enterprise,” “Mature business,” or “Declining industry.” This division allows business development, growth, and decline to be modeled. The middle third represents housing, divided into “Premium,” “Worker,” and “Underemployed.” The bottom third represents classes of people: “Managerial-professional,” “Labor,” and “Underemployed.”

In trickle-down fashion, businesses and housing are generally created by and for the wealthier classes, gradually decay, and are taken up by lower social strata. Housing eventually decays into slums for the underemployed and is eventually scrapped, alongside declining industry.

Model dynamics are enacted by flows that connect levels. The flows, represented by arrows, are controlled by rates, which are represented as valves attached to the arrows. Rates have cryptic names, like “MPB” (near the lower-left corner), which means “Managerial-professional births” (and implicitly deaths); and “MD,” “Manager departures,” the migration of managerial-professionals out of the city. People flows represent births and deaths, the migration of people into and out of the city, and class mobility.

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Forrester’s overview of the Urban Dynamics simulation model.

A relatively small number of levels represent physical things—business, housing, people—as well as abstract social quantities like perceived attractiveness. These levels are all statistical aggregates; the model does not care about the gender or color of individuals, or the location of people, housing, and businesses. A vast network of subdiagrams interlinks levels and rates, encoding a huge number of assumptions about why different classes migrate to and from cities, their differential birth rates, and the effect of policies like taxation (bad for growth but attractive to the poor), welfare (counterproductive), urban renewal, and labor training programs.

Forrester’s mechanisms and terminology, such as feedback loops, time delays, and goal-seeking behavior, reflect the conceptual debt to servomechanisms. Servos seek goals, and this concept is used to represent the goal-seeking behavior of people. Achieving these goals means regulating flows that represent processes such as business creation, migration, housing construction, and taxation. Many of the goal-seeking behaviors respond to ratios between quantities such as taxes needed/assessed, housing needed/available, and jobs desired/available. Such ratios strongly influence the city’s attractiveness.⁸⁷

Population movement into and out of the city is central to the model and is driven by the notion of relative attractiveness—the attractiveness of the urban area versus its external environment. Attractiveness is based on many factors, including the housing and labor markets. Unsurprisingly, people are attracted to jobs.

By distinguishing between real and perceived quantities, the model simulates people as responding to past events. This, as we’ve seen, is a classic control theory problem. Mapped onto social phenomena, it represents the fact that changes take time to percolate. Housing construction, for instance, is subject to both perceptual latency (modeled by smoothing) and speculative effects (modeled with a positive feedback loop). Simulated people chase their goals, but they do so imperfectly; they miss the mark with frenzied speculations and outdated perceptions.

Growth and decay are influenced by the amount of available space. The model doesn’t keep track of where buildings lie, only how much space they take up and how much space is available in the limited urban area. Mostly empty cities grow slowly, and as space fills in, they grow more quickly. (Most growth rates in the model work like compounding interest.) As the area gets full, new development slows, and pressure is put on available space. Older housing and businesses are scrapped, simulating an urban renewal program.⁸⁸ In short, space is modeled indirectly, as a statistical aggregate, and allocated toward an ever-changing mix of homes and businesses.

Forrester’s urban model is an endogenous system. It is a closed and formally described model that accounts for growth, decay, and policies that Forrester deemed relevant. The system is closed, both as a closed-loop simulation, and as a body of work, as it has few references to outside texts (or data). The Urban Dynamics book offers a complete account of Forrester’s simulation approach, his theory of urban dynamics, and his urban model’s simulation mechanics. One could, in theory, reproduce the model entirely from this description. Forrester’s prose narratives bring the model to life, explaining how the simulation works and immersing readers into its make-believe cities. All these qualities made the system ripe for adoption. It was perfect for someone like Wright.

§§§

Forrester was keenly aware that there is no single model of a system and that an overarching set of questions must guide the development of a particular model. In this case, the question was: What are the underlying causes of urban growth and decay? The model, as Forrester reiterates throughout the book, articulates a particular theory of urban behavior. The model is a theory. Forrester conducted experiments with the model, trying out different urban policies (e.g., taxation, urban renewal, subsidized low-income housing), in an attempt to determine their effects. He then interpreted the model behavior, narrating the hard-to-see causality within—for example:

Figure 4–8a shows the effect of introducing such a low-cost-housing program at year 0. The housing available for the unemployed begins to rise immediately. Because of the increased housing, more underemployed are attracted to the city and the underemployed population rises for the first 10 years. But the low-cost-housing program exerts continuous pressure on the available unfilled land, making the area less favorable for other types of construction.⁸⁹

Speaking from a position of immersion within his own make-believe model, Forrester’s omniscient narrative offers a helpful gloss on some rather dense and opaque simulation machinery. Zoom into the fine details, and descriptive labels like “premium housing” endow the model with meaning, transmuting abstract levels and rates into social and material objects. Forrester’s careful explanations, in other words, are integral to his simulations.

In parallel with the repeated warnings that the model is a theory, a slippage, perhaps inescapable, occurs throughout the book. As the preceding excerpt demonstrates, the graphs and narrations read as if they describe a real city, confidently describing its causal flows. The contingent and representational nature of the modeling act is elided, collapsing representation and represented. Such stories help the simulation spin to life in our imagination, but this make-believe is in tension with the model’s ostensibly scientific aims.

The social world, Forrester argued, was hard to understand and control because of its complexity. “Complex systems,” according to Forrester, are structures with “high-order, multiple-loop, nonlinear feedback,” to which “all social systems belong.” Such systems are counterintuitive, and our understanding of them is naive.⁹⁰ As a result, “the intuitive solution to the problems of complex social systems will be wrong most of the time.”⁹¹ Forrester saw urban dynamics as an antidote. Model building and analysis could attune decision makers to the critical points of intervention, heightening their ability to address the underlying structural causes of the urban crisis specifically and social ills more broadly.⁹²

[picture]

Graph and caption redrawn from an October 31, 1969, New York Times article about Forrester’s Urban Dynamics.

This counterintuitivity found supporters among a resurgent conservative moment, and Forrester found an eager audience at the Nixon White House, whose Department of Housing and Urban Development helped fund his urban dynamics research. Forrester had incorporated the policy ideas of Collins, a conservative Democrat who had lost faith with Johnson’s Great Society and favored urban renewal—bulldozing “blighted” neighborhoods to attract middle-class whites. So while Urban Dynamics was roundly slammed by urban planners, social scientists, and experienced urban simulation practitioners, it was pitch-perfect for a resurgent conservative moment. While Forrester’s antiwelfare conclusions no doubt appealed, the real prize was his notion of counterintuitivity, which was eagerly taken up by conservative and libertarian intellectuals as “Forrester’s Law.” Interventions in complex social systems had unintended—and undesirable—consequences, so it was best to not regulate or intervene and instead practice “benign neglect.”⁹³

Models of Segregation

Drawing the boundary between the inside and outside of a model is a sticky business. Boundaries reflect the model builder’s ideological priorities and dictate representational possibilities. Race and politics were integral to America’s urban crisis, but Urban Dynamics makes few explicit references to either. In excluding race from the model (populations are distinguished only by economic status), Forrester makes the starry-eyed assumption that American life is color-blind (as least as far as the urban crisis was concerned).⁹⁴

Forrester’s model does not (and cannot) model harmful state policies such as redlining, in which communities of color are systematically denied access to the same favorable credit terms provided to white neighborhoods. Such policies amplify segregation and inequality. Forrester’s model recognized class, but not race. Moreover, it has no notion of spatial organization. There are no landscape, no neighbors, no neighborhoods. Space is abstract and indirect: the percent of developed land. Segregation and redlining, therefore, are not represented and cannot be represented by Forrester’s paradigm.⁹⁵ Similarly, while urban dynamics distinguishes people from housing, SimCity does not (people are simulated by the presence of housing). As a result, urban dynamics could, in theory, model homelessness while SimCity cannot.

There is, of course, more than one way to simulate a city. Thomas Schelling’s work on segregation provides a useful counterpoint. As an economist and game theorist, Schelling approached the world from a different perspective: individual actors made choices that gave rise to macroscale behavior. Forrester, by contrast, conceptualized people as aggregates. It’s a classic example of top-down versus bottom-up modeling. Schelling began publishing on “Models of Segregation” in 1969. The research was sponsored by RAND as part of its response to America’s urban crisis.⁹⁶

Schelling’s striking models showed how racial segregation easily emerges from the cumulated decisions of individuals. All it took was the slightest preferences to not be in the minority. Through the accumulation of many small decisions, slight preferences amplified into full-blown segregated neighborhoods. (Schelling was well aware that the dynamics he described were, in all likelihood, less powerful than segregation’s other causes: organized discrimination, suppressed civil rights, and social inequity.)

[picture]

Schelling’s one-dimensional segregation model. Top row: A starting random configuration. Bottom row: Results of preferential movements. Dots indicate “dissatisfied” members.

In Schelling’s one-dimensional spatial model of segregation, each agent wishes to avoid being in the minority of its neighborhood: the two symbols on its left and the two symbols on its right. Unsatisfied symbols (marked with dots overhead) move to the nearest satisfiable space, and out of these preferential movements emerge radical changes.⁹⁷

Schelling’s two-dimensional model is equally striking. Following these spatial models, he offers a numerical “neighborhood tipping” model, which considers how a population of Blacks and whites will evolve over time in response to tolerance preferences (for minority/majority status).

How to explain the differences in Schelling and Forrester’s approaches? First, their research questions were very different. Forrester examined how policy affected urban growth and decay (while ignoring race), while Schelling focused on segregation. These are distinct political visions. Schelling’s model acknowledges racial bias (or other preference types), while Forrester’s color-blind model recognizes only class difference.

Professional background influenced the approach taken and materials used and, most relevant to the upcoming discussion of SimCity, the placement of goal-seeking behaviors within the model. Steeped in game theory, which attends to individual choices and interactions between individuals, Schelling examined how large-scale patterns emerged from the microscale interactions of many agents, each pursuing their own agendas. In Schelling’s model, goal-seeking behavior emanates bottom-up, from individuals, while in Forrester’s model, goal-seeking behavior emanates top-down, from population aggregates seeking certain states of affairs.

[picture]

Segregation resulting from Schelling’s two-dimensional model.

Schelling’s model is far more legible. Rules for the spatial model are straightforward and easily explained, while the Urban Dynamics model has a vast number of equations whose complete exposition occupies an appendix. Materials reflect this difference. Schelling simulated by hand, often using coins, while Forrester’s model required a computer. This difference in complexity, and thus opacity, is also reflected in their respective narratives; the behavior described in Urban Dynamics requires a lot of explication, while the dynamics of Schelling’s models are easy to see.

Schelling repeatedly calls attention to his materials—the pennies and nickels that stand for families—vigilantly reminding us that we are looking at “stars,” “zeros,” and “squares,” which collectively constitute a metaphorical “‘neighborhood,’” and that these are only make-believe “‘segregated patterns.’” Schelling underlines make-believe pretense, while Forrester slips into the make-believe of his models, conflating theory and reality.⁹⁸

Perceiving the distinction between these alternative modes of simulation is crucial to understanding the simulation hybridization found in SimCity. SimCity, as we will see, reinterprets Forrester’s methods, reconciling a top-down simulation of aggregate social quantities with representations that are bottom-up, spatial, and more agent based. For now, it is enough to recognize that an inherent tension exists between these approaches, which we will later see reconciled in SimCity’s simulation architecture.

World Dynamics

With his urban model, Forrester decided that “industrial dynamics” was no longer an appropriate title for his simulation practice—“system dynamics” was. Early on in Urban Dynamics, Forrester wrote that “the term ‘industrial dynamics’ has become too restrictive, because the methods are applicable in many fields.” The essential ideas of a closed system, feedback, levels, and flows apply to “all systems that change through time.” These include “engineer systems, biology, social systems, psychology, ecology, and all those where positive- and negative-feedback processes manifest themselves in growth and regulatory action.”⁹⁹

Roused by these vast new horizons, Forrester embarked on an even grander simulation project: modeling the world. Published in 1971, World Dynamics applied the system dynamics paradigm towards simulating worldwide population, resources, pollution, technology, and capital.¹⁰⁰ Once again, the topic emerged through his association with the Sloan School, this time via a colleague who invited him to attend a meeting of the Club of Rome, an international elite that had envisioned a model for the “world problématique.”¹⁰¹ World Dynamics functioned as a Malthusian argument updated for the computer age, modeling human development as an autocatalytic process, which, left unchecked, would lead to catastrophic collapse. Together with The Limits to Growth, which followed in its footsteps, this work provoked widespread debate and discussion, for the first time bringing a simulation model to a popular audience.¹⁰² In the preface to World Dynamics Forrester wrote that

only by discovering how the ethical, political, physical, technical, economic and social forces of society interact with one another, can we understand the alternative patterns of future development. . . . System dynamics could be the unifying framework and vehicle for interdisciplinary communication. Not only is system dynamics capable of accepting the descriptive knowledge from diverse fields, but it also shows how present policies lead to future consequences. . . . Time is short. We must move quickly if we are to keep future options open.¹⁰³

Like Hari Seldon, Asimov’s fictional psychohistorian, Forrester constructed a model for forecasting humanity’s future. While Seldon foretold the fall of the Galactic Empire, Forrester saw the collapse of world civilization.

Although critics took issue with the methodology and assumptions, the irony is that the underlying thesis was correct; unchecked growth has led the world into the compound catastrophes of environmental degradation, extreme weather, mass extinction, and social dislocation.¹⁰⁴ At the end of the day, technical ingenuity cannot overcome the laws of physics. The green revolution of the 1960s saved billions from starvation, but the collapse point was simply nudged further into the future. Human ingenuity can increase the planet’s carrying capacity for humans, but not indefinitely.¹⁰⁵ Costs that are initially considered externalities—if we are aware of them at all—often turn out to be vital and integral to sustaining life itself. Limits to Growth was a funhouse mirror that reflected an uneasy truth: the architecture of our world is broken, its economic foundations a machine whose positive feedback loops were gradually and violently shaking itself to pieces.¹⁰⁶

System dynamics wasn’t really science or engineering. It was, as Forrester had said all along, predicated on intuition and art. But this was exactly what Wright needed to make SimCity: an analogical tool kit for modeling social systems on computers, accompanied by a technique that encouraged imaginative appropriation. System dynamics lends itself to appropriation because it does not require a great depth of programming knowledge, computer power, or hard data. You don’t need a supercomputer, a PhD in social science, specialized software, or a sophisticated programming background to build a system dynamics model. What you need is intuition and creativity.

Mental Models and Make-Believe

I was curious how Forrester approached the imaginative dimensions of his work. Did he and his colleagues ever see their model making in terms of make-believe or play? Did they take pleasure in designing, tinkering with, and running their toy models? To my delight, a ninety-seven-year-old Forrester replied to my email in 2016 but reported that he seldom gave interviews anymore. Despite his poor health, he did agree to answer a handful of questions. Imaginative system dynamics models may harbor “interesting possibilities,” he thought, but was not something that he had considered. No, it wasn’t play or pleasure but “work, aimed at a useful result.” He still saw the world in terms of levels and rates. System dynamics, he thought, was like the profession of engineering in 1870, poised to one day explode and remake the world. Already, he noted, it was “spread thinly worldwide, and can enter every human activity.” Forrester hadn’t ever played SimCity or other popular simulation games, but he did know about them. A few days later, I asked Wright if he continued to use system dynamics in his simulations after SimCity, and he regarded me quizzically. (It was a strange question coming from someone who had apprenticed with him.) What kind of question was that? “It’s really just a way of thinking,” he said, as “fundamental” and basic as “procedural thinking.” System dynamics was a schema for the world; an imaginative modeling strategy; a way to represent with feedback loops.¹⁰⁷

“Application of System Dynamics to the Study of a Religious Experience” arrestingly illustrates the imaginative potential of system dynamics. Presented at the 1992 International Conference of the System Dynamics Society by Arlen Wolpert, a figure called “Simplified Flow Diagram Model of the Dark Night of the Soul”¹⁰⁸ vivifies the potency of system dynamics in the hands of inventive model builders.

What I love about this model is not just how surprising and weird it is—that a simulation technique with deep military roots is used to represent a spiritual experience—but how it drives home the point that system dynamics is really, at heart, a tool of expressive representation.

In his way, Forrester knew this: “System dynamics builds two-way communication between” “simulation models” and “mental models.” We make our way through the world with mental models—the fragmented assumptions, tacit knowledge, and rich intuitions that frame lived experience—but these have their limits, especially when it comes to complex phenomena. Computer simulations revealed the “dynamic consequences” of our worldviews.¹⁰⁹ In operationalizing mental models, Forrester recreated, in digital form, Bush’s analog approach. The mind’s reach could be extended by concretizing thought as an analog.¹¹⁰

Analog concretization opens the door to metacognitive reflection—a crucial aspect of Forrester’s program. Wolpert, for instance, sought to understand his own transformative mystical experience. According to Forrester’s mentor Gordon Brown, simulations allow “the human mind” to better understand the “structures and assumptions of the mental models now being used for running our society.”¹¹¹ Simulation design blasted open the black box, forcing people to articulate and concretize their own tacit mental models and, in doing so, confront and reconceptualize their understandings.¹¹² System dynamics was a tool for thinking about how you think.

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“Simplified Flow Diagram Model of the Dark Night of the Soul” from Wolpert’s “Religious Experience” paper.

Like Lego blocks, the ready-made analogy of levels and rates are flexible primitives that can be applied to virtually anything. This generality was supercharged by Forrester’s style, which prioritized intuition over data, citations, and scholarship. The approach he cultivated, its way of seeing the world, its technical implementation, and the associated publications and social practices all worked to spread a tradition of computational make-believe. Forrester’s publications meet the willing reader halfway. They scaffold make-believe participation, inspiring and guiding readers in the craft of believing in and then making system dynamic models. Forrester had produced computational building blocks for make-believe that Wright could easily adopt. This is exactly what Wright did, first in SimCity and later in titles like The Sims,¹¹³ which applied the technique toward human psychology (an approach anticipated by Wolpert and Forrester).¹¹⁴

“System dynamics could be the unifying framework and vehicle for interdisciplinary communication,” wrote Forrester.¹¹⁵ With system dynamics as a flexible contact language, Forrester operated as an interdisciplinarian, brokering relationships between communities: control systems, computing, industrial management, urban planning, public policy, economics, and more. In doing so, he rose to “network celebrity” status.¹¹⁶ Forrester and his work were featured in outlets ranging from Playboy to the New York Times, foreshadowing what would happen to SimCity and Wright.

To build SimCity, however, Wright needed more than system dynamics. He needed something like Schelling’s model, which was bottom-up and spatial. He found this in cellular automata, the other major computational simulation tradition he adopted, which we turn to next.

4

Cellular Automata: Synthesizing the Universe

“I am become Death, the shatterer of worlds,” said the Lord Krishna. The blossoming eruption in the New Mexico desert brought these words to Robert Oppenheimer’s mind. It was as though the Hindu deity Krishna had revealed himself:

If the radiance of a thousand suns

were to burst into the sky,

that would be like

the splendor of the Mighty One—1

It was the first nuclear device ever detonated. Oppenheimer later remarked that by creating the atom bomb, he and his fellow “physicists have known sin; and this is a knowledge which they cannot lose.”² Vannevar Bush also witnessed the explosion; as head of the National Defense Research Committee, he had kick-started the ambitious weapons program and helped ensure that responsibility for nuclear weapons remained in the hands of the president—not Congress, and certainly not the scientists who developed it.³

Guilt and repentance followed. After the war, Oppenheimer was appointed to the US Atomic Energy Commission and spoke out against nuclear proliferation. Triumphant warriors, physicists enjoyed unprecedented prestige following the war, but the bitter chill of the Cold War would not permit Oppenheimer’s pacifism, which antagonized Washington’s national security establishment. Accused of being a communist, Oppenheimer was stripped of his security clearance by President Eisenhower.⁴ Physicists faced an existential choice. Should they continue working on weapons of mass destruction or turn their talents elsewhere? And if so, where?

“What is Life?” asked Ernest Schrödinger. It was an ancient question, to be sure, but Schrödinger, whose foundational contributions to quantum theory had earned him a Nobel Prize, put a new spin on the matter in a 1943 series of lectures. He looked at biology upside down, as it were, from below—the perspective of a physicist gazing upward into the kaleidoscopic marvels of life. The humble mind of a physicist knew a lot about “periodic crystals . . . one of the most fascinating and complex material structures by which inanimate nature puzzles his wits,” but the “nonrepeating pattern” of the chromosome fiber, the “material carrier of life” (what we now know as DNA), was an “aperiodic crystal . . . a masterpiece of embroidery” that posed an even grander challenge. Here was life, swimming upstream against the laws of thermodynamic decay: “An organism’s astonishing gift of concentrating a ‘stream of order’ on itself and thus escaping the decay into atomic chaos—of ‘drinking orderliness’ from a suitable environment,” was a scientific riddle of the highest order.⁵

And while his ideas were not new, his framing of the question and his towering prestige galvanized generations of researchers to enter the nascent field of molecular biology. Schrödinger’s timing was impeccable. Published as a book in 1944, the lectures opened a door that war-weary physicists eagerly entered. High on the triumphs of war but despondent about creating even more powerful weapons of mass destruction, they emigrated into the nascent realm of molecular biology. Here was a better place to apply their intellect, analytical techniques, habits of mind, and, of course, their hubris. A parade of physicists took on biology, and biologists absorbed the radiant status of postwar physics. The physicists and biologist who shared a Nobel Prize for their discovery, in 1953, of the double-helix structure of DNA all credit Schrödinger for influencing their careers and work.⁶ And many mathematicians, some of whom had made critical contributions to wartime work in weapons, computing, and cryptography—figures like Alan Turing and John von Neumann—also began to turn to questions of biology.⁷

§§§

In 1955, readers of Scientific American were treated to an article arguing that “there is no conclusive evidence for an essential gap between man and a machine. For every human activity we can conceive of a mechanical counterpart.” Cameras were “electric eyes.” Telephone exchanges and high-speed computers were both a kind of “brain-machine.” After giving an overview of Turing machines and neural nets, the author proceeded to describe a very curious machine, one that mimicked another biological function: reproduction. It had taken about ten years for Turing’s universal computer thought experiment to be realized as a piece of hardware, and this machine, similarly, was still in the speculative phase. The author described an infinite graph-paper world. A “two-dimensional lattice” made of “square” “cells” would interact with their “neighbors.” Some cell clusters constituted an “organism.” Working together, its “neurons,” “transmission cells,” and “muscle cells” could reproduce the “organism” and create offspring, eventually filling up the crystalline universe with copies of itself. It was estimated that 200,000 cells would be enough to comprise a single self-reproducing organism. Mutation and competition would then open the door to machine evolution.⁸

Fast-forward fifteen years. In 1970, readers of Scientific American’s Mathematical Games column encountered a curious new solitaire game, Life. It was easy to play. All you needed was a game board for Chess or Go and a set of counters like poker chips or checkers. After establishing an initial pattern by putting counters into cells—“live” cells had tokens in them—you applied the rules for birth, death, and survival: “births” occurred when an empty cell had exactly three living neighbors; “deaths” resulted from “overpopulation” (four or more neighbors) and “isolation” (only one neighbor). Out of these simple rules marvelous dynamics emerged: patterns oscillated, multiplied, flitted about, froze, and vanished. Adjust the starting pattern, and a new and unpredictable universe unfolded. Prize money was offered to anyone who could discover a pattern whose population would grow indefinitely. People with computers went gaga. They also had an unfair advantage: computer simulation empowered them to swiftly scamper through Life’s miniature universe. The prize winners’ discovery indicated that Life’s cellular universe might function as a universal Turing machine and thus be equivalent to a general-purpose computer.⁹

By the early 1980s, researchers had invented cellular rule sets for simulating and exploring a breathtaking assortment of phenomena: snowflakes, spiral galaxies, plants, crystals, heart tissue, number theory, tapestry design, image processing, and more. Taken together, these models inspired one physicist to study them not as representations of specific natural phenomena but rather as abstract “‘self-organizing’” “dynamical systems.” Scientists had recently begun to rigorously study “‘chaos,’” “‘strange attractors,’” and “nonlinearity,” and artificial cellular worlds might provide much-needed laboratory models of the universe as a self-organizing dynamic system.¹⁰ This research was massively influential and, like the earlier articles, reached a popular audience through Scientific American. The physicist concluded that the computer offered “a new way of thinking in science. Scientific laws are now being viewed as algorithms.” Physical systems could be studied as information processing systems.¹¹

These projects are John von Neumann’s self-reproducing automata (1955), John Conway’s Game of Life (1970), and Stephen Wolfram’s “Statistical Mechanics of Cellular Automata” (1983). All are part of a simulation community of practice called cellular automata. Reductionist science analyzes complex wholes—like ant colonies, cities, and brains—by breaking them down and studying their component parts, whereas complexity science does the opposite: it studies how wholes are greater than, or emerge from, constituent parts.¹² Cellular automata became laboratory models of complex emergent systems, whether biological, physical, chemical, or social, and computers became instruments—like a microscope or telescope—for seeing, thinking about, and experimenting with emergence. Automata have historically functioned as both philosophical provocations and popular playthings, and cellular automata are no different. From self-reproducing automata to the Game of Life and SimCity, cellular automata played a vital role in inspiring and propagating the worldview of emergence.

§§§

To build SimCity, Will Wright needed a way to model the world from the ground up, as a landscape of interacting elements. SimCity used system dynamics to represent overarching economic factors such as unemployment, migration, and housing demand. But to model spatial systems like floods, fires, and the plan of the city itself, SimCity employed a simulation technique called cellular automata. Unlike system dynamics, which models populations in terms of numerical aggregates, these cellular models are inherently spatial and bottom up: global dynamics emerge from local interactions. Just as system dynamics became a staple of Wright’s simulation tool kit, so did cellular automata, which also offered a profound “expressive power.”¹³

The histories of system dynamics and cellular automata mirror this dichotomy between top-down and bottom-up. System dynamics arose from a paradigm of feedback and control that sought to regulate complex systems, and its history revolves around a powerful central figure whose efforts traced a consistent trajectory. But practitioners of cellular automata are like so many diffuse elements whose accumulated interactions have given rise to a complex, rich, and ambiguous history.¹⁴ Examining this history reveals many islands of practice separated by time, terminology, agenda, and forum. (These disparate communities wouldn’t uniformly adopt the “cellular automata” moniker until the 1980s.) As a result, every history of cellular automata is partial, including this one. But while this archipelago thwarts neat historical narratives, it foregrounds the cross-pollinations and divergent ecologies that propelled this simulation tradition.

The “seductive powers”¹⁵ of cellular automata are central to this story. Their aesthetic-philosophical “holding power,”¹⁶ this chapter argues, emanates from how they rupture and illuminate metaphysical bulkheads. Cellular automata simulations, and the stories practitioners tell about them, dazzle our senses, destabilize perceptual patterns, and ask us to redraw our maps of reality. Cellular automata vividly dissolve commonsensical distinctions: between objects and their environment, between information and matter, between computation and mind, computation and life, and ultimately between computation and reality. To “pull the carpet out from under reality”¹⁷ in this way makes it hard to tell where the illusion lies. Were we mistaken in believing that objects have definite boundaries, or are we now in the grip of a new delusion?

Typically unfolding on a two-dimensional field, cellular automata readily lend themselves to visualization. Abstract and evocative forms fluidly transform, like clouds, inviting imaginative descriptions. Like ripples emanating from a pebble thrown into a pond, the crisp legibility of these lattice worlds heightens our sense of agency, should we intervene, and makes the action easy to follow. These effects are amplified by the surprisingly complex forms that unfold within these simple rule-governed universes. As practitioners discovered, computers with graphical displays amplified the appeal of cellular automata. And as computer graphics matured and spread, so did cellular automata.

The history of cellular automata illuminates more than SimCity’s aesthetics; it also helps explain SimCity’s reception by different communities. For the Santa Fe Institute, SimCity and Maxis functioned as popularizers of complexity science. For many, Maxis titles like SimCity were the closest they could come to chaos theory, self-organizing systems, and the mysteries of computer simulation.

§§§

This chapter divides the history of cellular automata into four epochs.

I. Universal computer. Originally inspired by ideas that blur the boundary between computing and biology, the cellular automata tradition must be understood in reference to the advent of modern computing. A close reading of von Neumann and Turing’s seminal contributions will help us unpack the philosophical commitments of cellular automata and why some practitioners would eventually try to understand the universe itself as a kind of computer.

II. Theory of self-reproducing automata. This epoch recounts the genesis of cellular automata in the 1940s as a mathematical thought experiment about biological self-reproduction. During this time, the cellular lattice abstraction is developed, and von Neumann’s work on the subject is published posthumously. Research advances along multiple avenues, but a coherent community of practice remains elusive.

III. Cellular automata. In the 1960s, researchers begin simulating cellular automata with computers. As a result, the tradition is radically transformed and becomes an empirical practice with changed aims. Cellular automata are used to model everything from physical and biological processes to abstract pattern formation. The paradigmatic cellular automaton, Conway’s Game of Life, is developed and disseminated, bringing many new recruits into the fold.

IV. Legitimation and popularization. The last epoch tracks the arrival of cellular automata in mainstream academic and popular cultures during the 1980s. By linking cellular automata to a burgeoning scholarship on complex systems, Wolfram helped seed the crystallization of communities of practice around cellular automata and artificial life. This work, in turn, is popularized by researchers, philosophers, and writers. These developments not only informed the design and reception of SimCity but set the stage for Maxis as a popular avatar of simulation, complex systems, and artificial life.

I. Universal Computer

World War II’s appetite for computing was voracious. Artillery and munitions development required the calculation of ballistic trajectories for the firing tables used by gunners. One source of calculating labor were human computers, typically mathematically gifted women. During the war one might find, at a computing center, a room full of “calculating girls”¹⁸ reckoning with pencil, paper, and desk calculators. Bush’s differential analyzers offered another source of calculating labor, some of which resided in the US Army and its collaborators at the University of Pennsylvania’s Moore School of Electrical Engineering. Moore School engineers had already upgraded some of their differential analyzer’s mechanical integrators with electronic components, and, inspired by a recent prototype of a purely electronic computer made with vacuum tubes, they set out to build a fully electronic differential analyzer–style machine called ENIAC: Electronic Numerical Integrator and Computer. Herman Goldstine, an Army officer with a PhD in mathematics who served as liaison officer to the Moore School, promoted the idea and secured funding for the project.¹⁹

One day in 1944, while construction of the ENIAC was underway, Goldstine bumped into von Neumann on a train platform. Although he was starstruck by the legendary mathematician, von Neumann’s nonchalance put him at ease—at least until the conversation turned to Goldstine’s work, when “the whole atmosphere of our conversation changed from one of relaxed good humor to one more like the oral examination for the doctor’s degree in mathematics.”²⁰

ENIAC interested von Neumann because of a project whose fathomless lust for calculation was such that all the calculating girls and computing machines in the land would never quench it. Unknown to Goldstine, von Neumann was working on a clandestine project deep in the American West, a race to beat the Nazis to a nuclear bomb.²¹ Bush, in his administrative role as head of the NDRC, had set the project in motion, but it was now overseen by the Army. In the end, the Nazi regime was defeated before the bomb was finished, saving Europe from yet another barbarity. But the war in the Pacific threatened to grind to a slow and bitter end, and the Japanese became the first (and we hope last) victims of nuclear weapons, as unconditional surrender followed the cataclysmic destruction of Hiroshima and Nagasaki.²²

Explosives within the bomb had to be shaped just right so that the implosion shock waves would compress the fissionable material into a critical state. Expertise with hydrodynamics was crucial, which is why Robert Oppenheimer recruited von Neumann to the project. Aerodynamics, weather forecasting, and sophisticated weapons design required the analysis of fluid dynamics, but such systems were analytically intractable. Just as Bush and his colleagues had turned to automatic machines to help them wrestle with nonlinear systems like power grids, so did von Neumann. Shock waves could only be made sense of empirically, through simulation, with either a physical model like a wind tunnel or through the laborious numerical calculations of human or automatic computers.

The problem, however, was the staggering volume of computations needed. Los Alamos employed human computers and the newest automatic computing equipment available. But the Manhattan Project’s appetite for calculation was insatiable and the pressure for computing relentless. The ENIAC project therefore was of great interest to von Neumann. Although ENIAC wasn’t completed until after the war’s end, his involvement left an indelible mark on the history of computing.²³

Goldstine was well aware that ENIAC had issues. Ingenious engineering aside, it was essentially a high-speed electronic differential analyzer. While the differential analyzers worked with continuous data (e.g., graphical curves, mechanical motion, electrical signals), ENIAC dealt with discrete data encoded in base 10 (as in our commonplace Hindu-Arabic numerals). It would be reconfigured to solve new problems by flipping switches and wiring cables—easier than messing around with oil and gears but still cumbersome. In many respects it was a naive effort; the electromechanical Rockefeller Differential Analyzer, which had already been completed, could be rapidly reconfigured by reading in punched paper tape.²⁴

As ENIAC was being constructed, Goldstine began envisioning a successor machine that would be smaller, faster, more flexible, and easier to reconfigure. Von Neumann immediately grasped that the fundamental problem was one of logical design. Working with numbers in base 10 was an unnecessary complication when base 2—1s and 0s—would do just fine. But more important, EDVAC, as the successor design would eventually be known, needed a logical architecture so flexible and so easily reconfigurable that it could compute anything. The machine should be able to perform calculations that hadn’t been invented yet, let alone dreamed of, by him or anyone else. To accomplish this trick, von Neumann would draw on recent developments in an abstruse part of mathematics.²⁵ Alan Turing had recently resolved a famous mathematical conundrum and in doing so had developed a curious idea: a mathematical model of a human being.

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Bush’s differential analyzers were part of an ancient family. Their kin included Lord Kelvin’s (1824–1895) tide calculator and the automatic fire control computers on Navy ships. All used mechanical integrators to do calculus. As Bush saw it, this family tree also included pebbles, the abacus, punched card machines, and much else. By 1936, when Bush gave his “Instrumental Analysis” talk, automatic calculating machines had already begun to remake banks, navy ships, and universities.²⁶

Blaise Pascal (1623–1662) had invented one of the first calculating machines: enter your figures, turn the crank, and it added (or subtracted). Gottfried Leibniz (1646–1716) made one that could also divide and multiply, reflecting that “it is unworthy of excellent men to lose hours like slaves in the labor of calculation.” Thinking that machines might rescue people from other kinds of tedious intellectual labor, he imagined a machine for solving algebraic equations, and then another for logic. Possessed of encyclopedic knowledge and deductive capabilities, this machine would bestow upon “humanity . . . a new kind of an instrument increasing the powers of reason far more than any optical instrument has ever aided the power of vision.”²⁷ Leibniz never completed it, but the idea, according to Martin Davis, inspired generations of mathematicians and logicians, and ultimately Turing’s seminal 1936 work.²⁸

Turing was not trying to design an automatic computer. Rather, he was trying to solve a problem set forth by the mathematician David Hilbert (1862–1943). Facing a crisis in the foundation of mathematics, Hilbert rallied allies as only a mathematician can: by presenting unsolved problems. These problems, Davis argues, indirectly addressed Leibniz’s project and were squarely aimed at basic philosophical questions: What did mathematicians really do, and how provably correct were these practices? Was math founded on intuition or logic? What are the limits of logical thought?

In contemplating these questions, Hilbert turned the tools of mathematics upon itself, developing what he called metamathematics, or proof theory. A proof begins with axioms and premises and makes a chain of deductions until it reaches a conclusion of some sort. Validating claims means checking if, given a set of axioms, a particular deduction is correct. Hilbert asked for a metamathematical proof that a system of logic was capable of deducing every possible truth. He also asked for an algorithm that could check, in a finite number of steps, whether a proposed conclusion could be derived from a set of premises, which became known as the Entscheidungsproblem, or decision problem. The French mathematician Henri Poincaré mocked the idea, writing that if mathematics were reduced to computation, then “we might imagine a machine where we should put in axioms at one end and take out theorems at the other, like that legendary machine in Chicago where pigs go in alive and come out transformed into hams and sausages.”²⁹

Optimistic until the end, Hilbert was in for a shocking upset. Although allied with Bertrand Russell, Alfred Whitehead, Ludwig Wittgenstein, and a young von Neumann, his program of proving the logical completeness of mathematics was ultimately buried by Kurt Gödel and Alan Turing. Gödel found that every logical system harbors true but unprovable statements; logic was incomplete. He crafted his ingenious proof by embedding statements about a system of logic within that same system of logic, coding metamathematical statements as numerical sequences. When Gödel divulged his results at a 1930 conference, von Neumann immediately grasped that the jig was up and ceased work on logic and the foundation of mathematics.

In 1936, a budding English mathematician named Alan Turing landed another brilliant and devastating blow. Turing proved that it was not, in fact, possible to check whether a proposed conclusion could be derived from a set of premises in a finite number of steps.

But it is how Turing solved Hilbert’s decision problem that has profound implications for our story. Turing focused on the mathematician that might validate a proof. As we have noted, human computers, often women, were commonplace before the widespread advent of automatic computing machines. Employing a mathematician’s razor-sharp instincts for abstraction, Turing saw that she sat at a desk with her attention focused on a piece of paper on which she read and wrote symbols. Stripping this scenario down to its formal essence, Turing conceived of her as an imaginary abstracted computer he called an automatic machine.

This abstract computer was made of a few components. It had an infinite paper “‘tape’” divided into many “‘squares,’” each of which could be inscribed with a “‘symbol.’” Just as a human reckons with pencil and paper, this tape recorded the problem at hand (e.g., 21 × 2), the answer (42), and “rough notes to ‘assist the memory’”—the intermediate steps of a calculation. At any moment the machine was “‘directly aware’” of only one such square, called the “‘scanned square,’” which contained the “‘scanned symbol.’” Like a person, the machine “can effectively remember some of the symbols which it has ‘seen’ (scanned) previously” by altering a simple internal state. This, along with the observed symbol, represented the computer’s “‘state of mind.’” A “configuration,” defined at the outset, dictated the machine’s behavior. This configuration, or algorithm, was encoded as a table of symbols. Each entry described a response to any “state of mind” (the internal state and scanned symbol): what symbol to write in the scanned square, whether to shift the tape scanner to the left or right, and how to change internal state. What we now call a Turing machine is this abstract computer.

To make the proof work, Turing needed to show that his automatic machine could perform any algorithm. He did this by proving it was possible to configure an automatic machine that could, in turn, simulate any other automatic machine. Turing called this special superflexible machine a “universal machine.” A universal machine took, on its infinite paper tape, not just a description of a problem (like 21 × 2), but a description of the algorithm, or automatic machine, for solving that problem. This was possible because he had already proved that any algorithm could be written as a bunch of symbols: its configuration table. We now use the phrase Turing complete to describe a machine that can simulate any other automatic machine. And universal machines, of course, can simulate other universal machines ad infinitum.

Taking inspiration, no doubt, from the automatic calculating instruments of the 1930s, Turing’s proof remade a mathematician into an automatic machine. It was also a logical blueprint for a universal computer. Generations of soul searching and reflection on the part of mathematicians about their own practices yielded a mathematical model of a human being. It could compute anything that was computable. And this machine swept away distinctions between physical machines, algorithms, and symbols. To a universal computer, everything was just data. Appearances to the contrary were an illusion.³⁰

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Fast-forward about a decade. Von Neumann is excited by ENIAC’s raw number crunching power but wants a simpler design that is nonetheless infinitely more flexible and easier to reconfigure. Rather than rewiring cables or making any other physical modifications, programs would be stored in memory, alongside the data—as in Turing’s universal machine. Programming thus became a simpler affair: simply change the program data (what Turing called a “configuration”). ENIAC had an outer world of cables and an inner world of data. Von Neumann’s proposed EDVAC design flattened these inner and outer worlds into one. Machines, programs, and data were interchangeable. The difference, after all, was an illusion.³¹

Von Neumann drafted a report in which he proposed organizing EDVAC into three main parts: arithmetic, control, and memory. The “central arithmetic part” was constituted by “specialized organs” for addition, subtraction, multiplication, and division. Control was governed by “general control organs” that enabled the device to be “elastic, that is as nearly as possible all purpose.” As in a universal Turing machine, the large “memory” was both a scratchpad for reckoning (the infinite paper tape corresponding to the mathematician’s notebook) and a place where “instructions” governing the machine’s operation were expressed in a “code” (the configuration written on the infinite paper tape).³²

Like Turing, von Neumann took inspiration from biological models. Turing saw a human computer sitting at her desk with pencil and paper and abstracted her into a mathematical machine.³³ Von Neumann subdivided EDVAC into multiple “organs,” deliberately using the term memory to describe the organs that “remembered” intermediate results.³⁴ (As opposed to “store,” which was in common use.³⁵) Perhaps most striking of all was the “Neuron Analogy” that suffused the EDVAC report. The arithmetic, control, and memory organs “correspond to the associative neurons in the human nervous system,” while “sensory or afferent and the motor or efferent neurons” corresponded to the “input and output organs.” (Their purpose, of course, was to interface with a “medium” that might be “sensed . . . by human organs.”)³⁶

Linkages between biology, machines, and math went deep. Von Neumann, in fact, made extensive use of a recently published paper in theoretical neurophysiology that discussed neurons and nervous systems in terms of mathematical logic.³⁷ Echoing Claude Shannon’s circuit-logic analogy, Warren McCulloch and Walter Pitts had demonstrated an equivalence between logical expressions and a “net of neurons.” Building on Turing’s work—which provided “psychological justification”³⁸ to their own—they proved that such “nets” can do anything a Turing machine can do. They had shown, as McCulloch later explained, that “brains were Turing machines, and that any Turing machine could be made out of neurons.”³⁹

McCulloch and Pitts provided a new way for mathematicians, scientists, and philosophers, as well as makers of computing machines, to enter the mind, writing that “‘Mind’ no longer ‘goes more ghostly than a ghost.’”⁴⁰ Von Neumann gladly followed them, using their diagrammatic notation for nervous nets to specify EDVAC’s “organs.” Their neuron model also enabled him to abstract away the “various mechanical or electrical devices”⁴¹ that might be used as computing elements. It didn’t matter whether EDVAC was made of relays, vacuum tubes, gears, cathode ray tubes, or fleshy neurons—these were simply materials that performed logic. Talk of “neurons,” “memory,” and “organs,” however, was more than a design lever. It reflected von Neumann’s deepening interest in biology, the “neurons of the higher animals,”⁴² and equivalencies between organisms and machines.⁴³

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After the war, general-purpose computer development proceeded rapidly. Conferences at MIT (1945) and the Moore School (1946) disseminated the ideas developed by von Neumann and his colleagues, touching off waves of computer development, from EDSAC in the United Kingdom (1948) to Whirlwind at MIT (1949). The EDVAC report was far from a complete specification, but it outlined the logical organization for building a general-purpose (Turing complete) computer.⁴⁴

These machines were unlike any automatic calculating devices that had ever been built. Because they were Turing complete, they were universal computers capable of simulating any other computer. A differential analyzer only simulated differential equations (and what those equations stood for), while the EDVAC design could simulate any algorithmic computing machine, including a differential analyzer.⁴⁵

Such machines could process numbers, like an arithmetic machine. And they could do logic, as Leibniz had dreamed. Indeed, you might write a program that fed on axioms and cranked out deductive sausages. One day it would reckon with sound, music, and pictures and play Chess, Checkers, and SimCity. It could do anything you might dream up and describe as an algorithm—a fixed series of logical steps. A universal computer could simulate any kind of computer, thus allowing new ones to be invented all the time through the creation of software, novel programming languages, data structures, and algorithms—all methods for describing new computing machines. The computer was no longer a person or machine that crunched numbers; it was a logic machine.

Your phone, laptop, and just about every computer in existence is a kind of universal computer—physical embodiments of Turing’s universal machine.⁴⁶ The dissolution of boundaries between machine, program, and data is what allows your phone to download and run new apps, as an application is just data to your phone’s operating system.⁴⁷ This is why old computers, like a 1980s-era Macintosh, can be simulated inside a web browser. And it is one of the ways hackers commandeer computers—by exploiting the power of universal computation. Much software, in fact, is Turing complete. It is possible to build a universal computer inside Minecraft and then run software on that machine, including a clone of Minecraft. “Hey, that’s a pretty nice computer there, but can it run Minecraft?”⁴⁸ If it’s a universal computer, it can. The heart of computing, in other words, is simulation.

II. Theory of Self-Reproducing Automata

Storing programs in memory gave rise to a new possibility: programs that build programs. In the 1950s this idea was called “automatic coding” or “automatic programming.” Rather than write programs as cryptic sequence of numbers (using a machine’s “instruction codes”), programmers used symbolic mnemonics. A compiler automatically handled the translation.⁴⁹

Seen through the lens of Turing’s universal computer, these were machines that created machines. A program, after all, was a kind of “automatic machine.” Thinking in terms of biology, as von Neumann clearly did, such recursive generativity constituted a reproductive process. Turing, von Neumann, Shannon, McCulloch, and Pitts had shown, or so they began to suggest, that the difference between organisms and machines might be an illusion.⁵⁰ Both used logic, calculated, reasoned, and reproduced. If people were thinking machines, then René Descartes’ mechanistic reductions could now be carried further, inside the mind. Machines, in turn, could be intelligent. And perhaps they might reproduce and evolve. Evolution, after all, was the creative force behind complex systems like brains.

In the mid-1940s, von Neumann cofounded a group interested in how artificial machines might serve as models of humans and animals. This group called itself the Teleological Society and eventually morphed into a multidisciplinary community organized under the “cybernetics” banner.⁵¹

Automata played a central role in von Neumann’s thinking. An automaton is an artifice that moves on its own accord, and the wellspring for the word automatic.⁵² Such devices had been employed since ancient times for entertainment, worship, and industry.⁵³ Machines that played games like Tic-Tac-Toe and Chess were automata, even if some were later revealed to be frauds concealing human players.⁵⁴ Lifelike automata are liminal objects that invite us to question the boundaries between alive and dead and between mechanism and mind.⁵⁵ Contemplating such artifices inspired Descartes (b. 1596) to conceive of animals (and human bodies) as mechanistic automata. (Rational thought was reserved for humans ensouled by God, a master artificer.)⁵⁶ Long before von Neumann arrived on the scene, then, the term automata had described both artificial and natural living things, and provoked the redrawing of metaphysical boundaries.⁵⁷

Automatic computing machines opened up new conceptual and practical horizons for lifelike automata, and it is in this context that we should understand von Neumann’s quest for a “general logical theory of automata” that could be applied equally well to “natural organisms” as well as “artificial automata.”⁵⁸ While this research program was multifarious, encompassing brains and mathematical measures of complexity, his work on biological self-reproduction is what concerns us here, as this is what laid the groundwork for cellular automata.

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Left: A fraudulent chess player automaton built in 1769. Right: Fourteenth-century manuscript depicting an automaton.

Von Neumann began with a “subdivision”: “The organisms can be viewed as made up of parts which to a certain extent are independent, elementary units.” Treating them as “well-defined” “‘black boxes’” allowed “the most difficult chapters of organic chemistry and physical chemistry” to be left behind, an axiomatization that allowed him to focus on “how these elements are organized into a whole, and how the functioning of the whole is expressed in terms of these elements.”⁵⁹ The juicy question, in other words, was how simple elements joined into complex wholes. The conceptual move paralleled McCulloch-Pitts, who had shown how simple neurons could be organized into networks that computed,⁶⁰ not to mention the simple elements comprising an artificial computer.⁶¹

How does nature achieve the “high complexity”⁶² of a brain or animal? Contemplating this question, von Neumann turned to an evolutionary answer. Organisms are capable of producing offspring more (and less) complex than themselves. That, after all, was how evolution produced a chain of beings whose complexity escalated. To mathematically represent this process, he looked to Turing. Just as Turing had defined a universal machine that could simulate any machine, von Neumann asked whether there was a universal automaton that could construct any other automaton—including those of greater complexity. What would be the simplest automaton that possessed this property?⁶³ Subjecting such a reproducing automaton to evolutionary pressures and mutation could give rise to automata of increasing complexity. One emergent system, evolution, might thus produce other emergent systems like animals and brains. This self-replicating machine would eventually be called a universal constructor.

Von Neumann proceeded to outline an existence proof. Just as Turing had stripped a human computer down to its mathematical essence to define his automatic machine, von Neumann stripped down a self-reproducing machine to its essential elements. The first step was to define a complete list of parts, the “elementary units,”⁶⁴ out of which these machines would be built. These elements were analogs of neurons, muscles, connective tissue, and metabolic energy producers.⁶⁵ He imagined a large number of such elements floating in a “reservoir.” A properly constructed automaton would, when introduced to the reservoir, seek out the necessary elements to reproduce itself. It would paddle about, collect parts, and then assemble those parts into a copy, or offspring.⁶⁶

This self-reproducing automaton was organized into four subsystems:

Description. A blueprint for another self-reproducing automaton.⁶⁷

Description replicator. This subsystem took a description as input and duplicated it, producing a second description.

Constructor. This subsystem took a description as input and fabricated it, producing an offspring automaton (made of parts found in the reservoir).

Control. The control subsystem orchestrated the other subsystems.

“I’m coming quite close to Turing’s trick,” von Neumann said.⁶⁸ Turing’s universal machines, given a paper tape that described any other automatic machine, could simulate that machine. When realized as an actual machine, this entailed an ontological flattening: the external patch cables that configured a machine like ENIAC were moved inside the device’s memory—onto the paper tape, as it were. To achieve a universal constructor, von Neumann effected a similar recursive trick: the description of an automaton and the elementary units comprising it were ontologically flattened into the same substrate. Introduce mutations in description copying, and you have the potential for “complication,” or “evolution.”⁶⁹ Excited by this vision, von Neumann bought the biggest box of Tinkertoys he could find and gleefully set to work.⁷⁰

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Eventually von Neumann’s colleagues impressed on him that an abstract two-dimensional model of the self-reproducing automaton would be far more tractable than Tinkertoys or any other physical material.⁷¹ Although biochemistry had been abstracted away, the physical messiness of fuel, energy, movement, and fusing remained. Stanisław Ulam, who had worked closely with him at Los Alamos, suggested he turn to a mathematical abstraction, an “infinite lattice.” Each “point,” according to Ulam, would interact with “a finite number of . . . ‘neighbors’” and would be “capable of a finite number of ‘states.’” Time would advance in discrete steps, each point assuming a new state based on the prior state of itself and its neighbors. Within this two-dimensional space one could identify “closed finite subsystems to be called automata or organisms.” An organism would just be an “‘activated’” region of space, a pattern that repeated in time or space, or both. With this new abstraction in hand, one could formally prove the “existence of subsystems which are able to multiply, i.e., create in time other systems identical (‘congruent’) to themselves.”⁷² Greasy kinematics would be replaced with a mathematically clean lattice, enabling the thought experiment to proceed in the realm of pure logic.

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Left: Lattice used in an ENIAC weather simulation. Right: Schematic detail of von Neumann’s “infinite array of square cells.”

The lattice idea, moreover, resonated with techniques Ulam and von Neumann had used for computer simulations of the weather and nuclear bomb implosions⁷³—experiments that complicated the distinction between theoretical and experimental physics.⁷⁴ As a result, the lattice abstraction anticipated the eventual simulation of cellular automata on computers.

Adopting the lattice suggestion, von Neumann gave his heap of Tinkertoys to a colleague’s young son.⁷⁵ He also turned the lattice’s points into square cells. The resulting cellular matrix he described as a “crystalline” or “cellular” structure.⁷⁶ (Points and cells were mathematically equivalent, but the cellular structure was a more evocative representation better suited to the task at hand.) As a formal mathematical tool, it enabled a self-reproducing automaton to be rigorously specified. While others would carry on the kinematic approach, von Neumann focused on the abstract cellular approach.

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Left: The twenty-nine states of von Neumann’s self-reproducing automaton. These twenty-nine states comprise the cellular universe in which universal constructors can be embedded. Right: Schematic design for his universal constructor.

The cells of this graph paper universe could be in one of twenty-nine possible states.⁷⁷ Each was a little tile that represented things like empty space, wires traveling in different directions, and signals on those wires. These tiles would fit together into mosaics, and some arrangements would function as a universal constructor. Using this formalism, von Neumann began the labor of designing, or at least proving the possibility of, self-reproducing automata. In a sense, von Neumann rebuilt EDVAC in simulated form—but this EDVAC could copy itself.⁷⁸

The work was first published in a 1955 Scientific American article by John Kemeny, a fellow Jewish Hungarian émigré who had worked on the Manhattan Project.⁷⁹ “Man Viewed as a Machine” pulled no punches, arguing that “there is no conclusive evidence for an essential gap between man and a machine. For every human activity we can conceive of a mechanical counterpart.” Cameras were “electric eyes.” Telephone exchanges and high-speed computers are both a kind of “brain-machine.” Kemeny gave a breezy and accessible account of von Neumann’s thought experiment. Such self-reproducing automata, Kemeny wrote, might even evolve if their “genetic tail” (the descriptive blueprint) were subjected to random mutations, and, of course, a competition over limited resources.⁸⁰

To see a collection of squares as an “organism” requires imaginative interpretation, to say the least. Abstracted and sliced up in this way, some narrative glue was needed to put the critters back together. Kemeny’s article underlines the vital role of graphics and text in bringing such cellular worlds to life. Not only do cellular universes demand narrative gloss, but their highly abstracted quality invites imaginative interpretation—core affordances of the cellular automata tradition.

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Kemeny’s diagram of a self-reproducing machine with original caption.

Consider Kemeny’s diagram of a self-reproducing machine. Shading, arrows, outlines, and text denoting “‘nerve cells,’” “‘brain,’” “‘muscle cells,’” “‘tail,’” and “‘feeler’” all scaffold our interpretation of the lattice as an organism.⁸¹ The dark outline is not part of the formal system but does help distinguish the organism—what Ulam called a finite subsystem—from its environment. The article demonstrates the evocative richness and perceptual ambiguity of a cellular world. You can see in them what you want, and they suggest and lend themselves to diverse interpretations. I see in Kemeny’s illustration the figure of a brain. The cellular lattice offers discrete things, places, and activities to narrate. Abstract forms invite storytelling, but Kemeny’s biological language goes beyond the needs of popular science writing. His prose reinforces the underlying analogy that links vacuum tubes and automata to neurons, organisms, and the mystery of biological reproduction. Not only is this world amenable to imaginative description, but von Neumann’s project demands it. He had abstracted messy materiality from life, but this purification was in tension with his representational aims. Graphics and text reintegrate the organic biology that had been abstracted away.⁸²

Succumbing to cancer, von Neumann didn’t live to complete these projects, and died, in 1957, at the age of 53. Ill and overcommitted, he may have also underestimated the immensity of the project.⁸³ Those who assumed the mantle of his program received more than a biological puzzle rendered in mathematics. Von Neumann had set in motion a study of how numerous identical elementary units gave rise to complex phenomena like animals, machines, and evolution. And the mathematical universe he defined harbored unforeseen representational possibilities.

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While von Neumann had focused on the cellular model, others took up the kinematic model of self-replicating machines.⁸⁴

In 1956, Scientific American published Edward Moore’s “Artificial Living Plants.” These would “extract raw materials from the air, water and soil” and “obtain energy from sunlight” in order to duplicate themselves. They might then be “harvested” for materials, just as “cotton, mahogany and sugar cane are now harvested from plants in nature.” Controls, of course, would be necessary to prevent them from becoming an invasive species and filling up the world. A speculative idea, to be sure, but so was going to the moon.

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Illustrations from Moore’s “Artificial Living Plants,” which describes an earnest but creepy vision of self-regulating robot plantations. Left: An artificial plant collecting materials. Right: “Like lemmings, a school of artificial living plants swims into the maw of the harvesting factory.”

Lionel Penrose, a psychiatrist and geneticist with wide-ranging interests, experimented with constructing self-replicating machines in the late 1950s, publishing his results in Nature, Annals of Human Genetics, and popular periodicals like Scientific American.⁸⁵ While DNA’s double-helix structure had been discovered, investigations into “the general theory of self-reproduction”⁸⁶ could shed light on how DNA actually replicates.

A chemistry professor took to self-replicating machines as a model for the origin of life, using modified toy trains.⁸⁷ Science, he noted, had successfully duplicated almost every function of living things. “Locomotion, energy metabolism, energy storage, perception of external stimuli, and nervous and cerebral activity” were performed by the “automobile, steam engine, storage battery, iconoscope, and digital computer.” What remained missing, he wrote in 1958, was self-reproduction, “so simple a process, in essence, that the lack of a working non-living model to date has been remarkable.”⁸⁸

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Stills from a 1958 film about Penrose’s self-replicating machines.

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It was Arthur Burks, a von Neumann collaborator, who carried the torch for the cellular approach.⁸⁹ Working with von Neumann’s unfinished manuscript, Burks completed the self-reproducing machine proof, publishing, in 1966, Theory of Self-Reproducing Automata. He steered his research lab at the University of Michigan toward topics bridging biology and computers, cultivating, in the process, what would one day become an influential community of practice.⁹⁰

In 1959, Burks conferred one of the first computer science PhDs on his student John Holland, who then joined the faculty.⁹¹ Holland spent decades probing the evolutionary potential of von Neumann’s vision, a project that would eventually find a large audience in the 1980s, when complexity science, cellular automata, and artificial life also met mainstream success—but that’s getting ahead of our story.

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Von Neumann’s twenty-nine-state design (left) uses many specialized states to route signals, whereas Codd’s simplified design (right) employs a sheath. (Note that these examples are not functionally equivalent.)

In 1968, Holland’s PhD student E. F. Codd simplified the self-reproducing machine proof, reducing the number of states from twenty-nine to eight. He accomplished this by modeling the world at a more granular level.⁹² Wires, for example, were represented as a conductive core wrapped in a sheath, rather than directional states (like ←→↑↓).

Interactive computer simulation helped Codd make the necessary creative leaps by allowing him to rapidly try out and play with different cellular universes.⁹³ Although Codd shared von Neumann’s goal of writing a proof, his methods were very different. Codd’s work marked a critical shift from analytical to empirical practice, a possibility underwritten by automatic simulation.⁹⁴

As far as computer simulation went, the working style was worlds away from Jay Forrester’s. With system dynamics, one built a world, pushed play, and then watched it unfold. But Codd was like a time traveler. He could pause the simulation while it was running and then rewind it. With everything frozen, Codd could change the state of the world. He could even change the laws of physics, push play, and see what happened.⁹⁵ Whereas computers formed the conceptual backdrop of von Neumann’s thought experiment, in Codd’s hands they became an indispensable tool: a vehicle for visualizing, traversing, and modifying cellular universes. Discoveries gleaned in the course of these voyages helped Codd simplify the proof. And interactive simulation, he noted, “has the effect of maintaining a high level of cerebral activity in the experimenter.”⁹⁶ It was thrilling, in other words. Codd’s experience foreshadowed the role that interactive graphics would play in popularizing cellular automata.

Codd undertook this work in the late 1960s, and his approach pointed the way toward a new way of engaging with cellular automata: as empirical phenomena. Other researchers had already begun to use computers, which were gradually becoming more commonplace—at least at elite research institutions—to do the same.

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Pattern growth with triangular, square, and hexagonal lattices.

III. Cellular Automata

Stanisław Ulam, the mathematician who had first suggested the “infinite lattice” abstraction to von Neumann, became an intrepid explorer of these simulated worlds. In the 1960s, he and his collaborators used the computers at Los Alamos to probe “a great number of such patterns and to survey certain properties of their morphology both in time and space.” These empirical explorations, Ulam noted, produced results that could not be “obtained theoretically”⁹⁷—not unlike the Monte Carlo simulations he and his colleagues used for nuclear weapons design.⁹⁸

The patterns they discovered exhibited a “complexity between inorganic patterns like those of crystals and the more varied intricacies of organic molecules and structures.” Although these were “admittedly somewhat artificial examples,” they could “throw a sidelight on the question of how much ‘information’ is necessary to describe the seemingly enormously elaborate structures of living objects.”⁹⁹ Ulam advanced aspects of von Neumann’s computationally inspired inquiry into life, but computer simulations, not mathematical proofs, would show the way.

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Three-dimensional pattern grown from single cube (at far right) after thirty generations.

They watched complex figures unfold inside the Los Alamos computers.¹⁰⁰ Simulations were done in two and three dimensions and with tessellating spaces composed of square, cubic, triangular, and hexagonal cells, transfixing Ulam and his collaborators. Intricate aperiodic forms emerged from utter simplicity.¹⁰¹

As Codd had found, computer simulation transformed the role of time. Experiments became performances in which a startling sense of aliveness emerged. By allowing cells to switch off, or “die out,” they found that multicellular forms appeared to move, split apart, and reproduce. Von Neumann’s biological agenda persisted, but with altered methods and goals. No longer would aliveness and self-reproduction denote a Turing complete universal constructor, as von Neumann had framed the problem, but aliveness would be a phenomenon exhibiting “both motion and some self-reproduction.” Adopting the lexicon of biology, discrete time steps became “generations,” squares could be “‘alive’” and undergo “death,” and squares had ancestors: a “parent” or “grandparent.”¹⁰² Dynamic activity summoned the lexicon of life.

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“Patterns grown with an erasure rule” could fragment into disconnected forms.

Dynamism also evoked play: “Another amusement we tried” was seeding two patterns, and labeling them with different colors. This “gives rise to a game for survival or a ‘fight’ between two such systems—in some cases both figures die out.”¹⁰³ In both Monte Carlo simulation and von Neumann’s game theory, the flexible concept of a game facilitated interdisciplinary contact and collaboration.¹⁰⁴ This project, however, didn’t follow a method or produce insights that scientists would easily recognize as a meaningful scholarly contribution. Was it mere amusement or high-minded research into mathematics and biology? Ulam and his collaborators were onto something, but what that something was remained to be seen. Perhaps their game hadn’t yet found the right audience?

§§§

One of Martin Gardner’s favorite parts of writing the Mathematical Games column for Scientific American, which he started in 1956, was getting to know “authentic mathematicians.” Key to the column’s success, in fact, “was the fascinating material I was able to coax from some of the world’s best mathematicians.” While recreational math was a recognized endeavor, the “line between entertaining math and serious math is a blurry one. Many professional mathematicians regard their work as a form of play in the same way professional golfers or basketball stars might.” But recreational math, according to Gardner, “has a playful aspect that can be understood and appreciated by nonmathematicians.”¹⁰⁵

One day in 1970, Gardner received a letter from the mathematician John Conway,¹⁰⁶ “one of the world’s undisputed geniuses.”¹⁰⁷ The letter described, among other Conway inventions, a fantastic piece of recreational math called “life.”¹⁰⁸ Gardner got out his Go board, a 19 × 19 lattice upon which black and white stones are placed, and tried it out. Astonishing forms appeared and scampered about, “including some that move across the board like insects.”¹⁰⁹ Here was something a nonmathematician could dig into.

Life’s origin is as follows. Professional success enabled Conway to spend “a fantastic amount of my time playing childish games”¹¹⁰ One day, he found himself flipping through the Theory of Self-Reproducing Automata. Pages and pages of grids, charts, and tables buttressed its proof. The scheme was “awfully complicated,” so Conway tried simplifying it and sat down on a sofa with a Go board and some stones.¹¹¹ Over the next two years (or so), he and his students tinkered with various rule sets.¹¹² Their experiments were similar to those that Ulam and his collaborators had undertaken, but Conway was totally unaware of their work.¹¹³ Like Ulam, Conway hoped “to see some self-reproducing animal . . . displaying some interesting behavior. In a weak form, living.”¹¹⁴ Finding the right rules for Life took two years of “experimentation at coffee time to get it right. And coffee time lasts all day here.”¹¹⁵

In his letter, Conway explained that “at last I’ve got what I wanted—an apparently unpredictable law of genetics.”¹¹⁶ The rules were simple. On each time step, a cell consulted its eight neighbors. New cells are born when they have three living neighbors; cells survive if they have two or three living neighbors; otherwise they die (of isolation or overpopulation).

Gardner described Life in his October 1970 column, including some patterns that Conway and his collaborators had discovered. These included stable “still lifes” (“beehive,” “block,” “snake,” “ship”) and oscillating patterns (“traffic lights,” “pulsar,” “beacon,” “clock,” “toad”). But the most striking pattern of all was the “glider,” which appeared to amble through space.¹¹⁷

[picture]

A glider’s five-frame amble.

Life, its creators knew, harbored many secrets awaiting discovery. It was for this reason that Conway shared his game with Gardner and his broad readership, offering a $50 prize to anyone who could, Gardner wrote, “prove or disprove” the conjecture that “no pattern can grow without limit . . . before the end of the year.”¹¹⁸ (Such a pattern was needed to prove that a Turing machine could be embedded within Life.¹¹⁹) With the prize offering, Conway recruited Gardner’s readership into his project, a brilliant marketing maneuver that turned many people onto Life. Some of them were equipped with computers, amplifying Life’s pleasures, and their odds of winning. MIT researchers soon claimed the prize. Using a computer program, they discovered a “glider gun” that emitted a stream of gliders.¹²⁰ The bounty on discovering a pattern underscores the shift from an analytical to empirical endeavor and the increasingly vital role of graphical computer simulation.¹²¹

The response to Gardner’s column was thunderous. Letters poured in from across the globe. Life was such a hit among computer enthusiasts that “for many weeks afterward, business firms and research laboratories were almost shut down while Life enthusiasts experimented with Life forms on their computer screens.”¹²² Gardner’s article “stirred such interest among computer scientists” that Scientific American followed up in February with a second piece. A cellular automaton graced the cover.

Amplified by Scientific American’s reach, Life helped knit together the splayed threads of cellular automata practitioners. When Alvy Ray Smith, who had recently completed a PhD on cellular automata, read the first article, he realized Gardner didn’t know much about the subject. So he called Gardner, who then spent a day with Smith at New York University in preparation for the second piece.¹²³ Ulam contacted Conway, who realized that “Ulam was the real founder.”¹²⁴ In the coming years, Life would continue to play this role, catalyzing the crystallization of communities of practice by recruiting practitioners and providing a shared contact language.¹²⁵ With Gardner and Conway’s blessing, a system analyst named Robert Wainwright began Lifeline, a quarterly newsletter for Life enthusiasts. Wainwright had helped Gardner validate the prize submission with the aid of a mainframe computer. But Wainwright’s computer simulation was nothing compared to what he saw when he visited MIT, at the end of 1971, and saw, for the first time, Life patterns rapidly unfolding on a screen.¹²⁶ Interactive computer graphics, as Codd had found, offered a thrilling edge. Wainwright, by contrast, submitted his Life simulations to an IBM mainframe, and received the output the next day.¹²⁷

[picture]

A glider gun (at top) emits a downward stream of gliders. (Empty circles indicate cells that will die this generation.)

Life was a phenomenon that sparked a vibrant community of practice that persists to this day.¹²⁸ The rules are extremely simple, and thus portable. Unlike the twenty-nine-state machine, whose exposition occupies an entire book, a full description of Life’s rules fits in a letter, a magazine article, or even a postcard. One can play it with pennies, Poker chips, or computers. It’s easy to program. And unlike a proof, Life feels alive. It’s unpredictable and vivacious; patterns dance, tease, and cavort. As it spread, Life disseminated the cellular automata simulation tradition.

[picture]

Scientific American cellular automaton cover. Hand-made space-time illustration of a 1D cellular automaton by Alvy Ray Smith.

Automata have long functioned as existential conversation pieces, and Life was no exception. The glider offered a tantalizing analogy. It appeared to move, but the apparent motion was a perceptual illusion. Squint, and the glider’s amble was really just cells blinking on and off—the granular machinations of Life’s substrate. “Analogies with life processes are impossible to resist,” wrote Gardner.

If a primordial broth of amino acids is large enough, and there is sufficient time, self-replicating, moving automata may result from complex transition rules built into the structure of matter and the laws of nature. There is even the possibility that space-time itself is granular, composed of discrete units, and that the universe, as [Edward] Fredkin and others have suggested, is a cellular automaton run by an enormous computer. If so, what we call motion may only be simulated motion.¹²⁹

Life was an amusement but also a philosophical provocation. And as a mathematical recreation par excellence, it bridged scientific and popular cultures. But could cellular automata be more than a recreation? Might they contribute to philosophy, biology, or mathematics? Were they science?

§§§

Computer-simulated cellular automata opened the door to expanded applications. These vivacious simulated worlds, depending on your orientation, represented nothing at all—abstract mathematical forms awaiting names—or everything at once: the fundamental stuff of the universe.

Under Burks’s aegis, the University of Michigan had become a hot spot of cellular automata research. In tandem with a general cybernetic turn (especially in biology),¹³⁰ Burks and his colleagues used cellular automata to model a huge variety of “natural systems”: vibrating membranes, heat flows, diffusion processes, “information processing by heart tissue,” and neural nets. He speculated that they might one day be used to study evolution. Cellular automata, it seemed, conformed to the information processing patterns of the natural world. Seen through the lens of the cellular automaton analogy, the natural world was a spatial information processing system.¹³¹

Burks and Holland’s student Tommaso Toffoli completed a dissertation on cellular automata in 1977 that proposed cellular automata as models for the basic laws of physics—the universal fundament as a computer.¹³² According to Toffoli, “the importance of cellular automata lies in their connection with the physical world”:133

Von Neumann himself devised cellular automata to make a reductionistic point about the plausibility of life being possible in a world with very simple primitives. But even von Neumann, who was a quantum physicist, neglected completely the connections with physics—that a cellular automata could be a model of fundamental physics.¹³⁴

With a new-found orientation toward cellular automata as objects of empirical rather than purely analytic inquiry, the stage was set for new types of models.¹³⁵ With computer simulation, cellular automata were used to represent crystal growth, spiral galaxies, snowflakes, sand dunes, tumors, infection, ecosystems, migration, chemotaxis, bacterial colonies, atmospheric pollution, gases, alloy formation, material phase transitions, cooperation, artificial societies, economics, and urban development.¹³⁶

Core aspects of von Neumann’s research program remained. Turing had dissolved the boundary between mind, matter, and information, and this dissolution persisted. Nature became an information processing machine. Von Neumann had adopted the bottom-up cosmology of McCulloch and Pitts in which a teeming mass of identical elements gave rise to complex information processing and mind. In adopting Ulam’s infinite lattice formalism for his proof—itself a by-product of early computer simulation—von Neumann had (inadvertently) all but assured that people would one day simulate cellular automata on computers. Even if many researchers had moved on from strictly biological questions, the program’s intellectual commitments persisted, coloring their models and worldviews.

Even as the cellular automata cosmos expanded, it remained fragmented. An archipelago of communities gathered around particular applications, like physics, biology, or Life. These collective efforts carried within them the seeds of further legitimation and popularization, as well as the notion of an emergent cosmos.

IV. Legitimation and Popularization

If cellular automata could represent so much of our universe, then perhaps these models, taken together, might tell us something about the universe’s fundamental properties. In the early 1980s, Stephen Wolfram glimpsed, in the swirling eddies and complex forms of these miniature worlds, not the representation of a single phenomenon—an organism, a sand dune, or a city—but something more abstract: the natural world as complex system. As fruit flies are laboratory models for studying genetics and games like Chess proved indispensable to artificial intelligence, cellular automata might be laboratory models for studying the universe as a complex system.

The natural world was unruly. Scientists had long neglected phenomena that disobeyed the classical laws of physics and economics, but the scientific establishment had become, in recent decades, increasingly receptive to investigating such scruffiness. Within this budding research paradigm, computer simulations played a crucial role. Computers happily iterated their way through the nonlinear population undulations of an ecosystem, the chaotic drip of a leaky faucet, or a fractal’s psychedelic tendrils. One could soar across a fractal landscape and zoom into pinpoints of interest, simulate countless ecosystems and then collate the results, or graph as much of the Lorenz attractor, with its lovely butterfly-shaped lobes, as one wished.¹³⁷

Just as a microscope presented strange new landscapes and organisms to the naked eye, computers helped probe nature’s chaos. They underwrote the study of dynamical systems by powering novel visualization and simulation techniques. They were both sample and microscope.¹³⁸ It was this emerging research paradigm that Wolfram addressed:

It is common in nature to find systems whose overall behaviour is extremely complex, yet whose fundamental component parts are each very simple. The complexity is generated by the cooperative effect or many simple identical components. Much has been discovered about the nature or the components in physical and biological systems; little is known about the mechanisms by which these components act together to give the overall complexity observed. What is now needed is a general mathematical theory to describe the nature and generation of complexity.¹³⁹

He approached this research community with an offering: a new kind of model for studying dynamical systems. It produced complex behaviors, but because its construction was so simple, it was “amenable to a more detailed and complete analysis.” And so Wolfram hitched cellular automata to the rising star of chaos theory. In doing so, he leveraged a core idea of the cellular automata paradigm: that complex phenomena, such as brains or self-reproduction, arise from simple interacting units. Cellular automata had by now been used to model a great many phenomena, but Wolfram’s aim was “to abstract from a study of cellular automata general features of ‘self-organizing’ behavior and perhaps to devise universal laws analogous to the laws of thermodynamics.”¹⁴⁰

To advance this project, he stripped cellular automata down to their simplest possible form: one-dimensional space and two states. This introduced new affordances. The evolution of an automaton could be plotted down the page in a space-time diagram.¹⁴¹ Each successive row was a snapshot in time, giving the experimenter an overview of a system across space and time.

[picture]

One-dimensional space-time cellular automaton diagram.

This one-dimensional distillation offered another powerful lever, as there were only so many cellular automata rule sets one could construct. Consequently, the laws of physics of these miniature universes could be compactly represented with a single number. Whereas Conway and his collaborators spent the better part of two years inventing and auditioning Life’s rules, hunting for a universe with the right mix of unpredictability, Wolfram had his computer systematically generate rules and plot their space-time diagrams.¹⁴²

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There are sixty-four totalistic cellular automata rules where k = 2, r = 2. Each is shown here with the same random starting configuration.

Examples of Wolfram’s four qualitative classes.

Wolfram had, in effect, defined collections of cellular automata universes. He proceeded to make an empirical survey and found that they evolved in one of four ways. (“My approach is to work like a naturalist,” he later wrote.¹⁴³) Some settled into a stable “spatially homogeneous state.” Others cycled through “periodic structures,” returning to the same small set of patterns over and over. Others looked random, yielding “chaotic aperiodic behaviour.” The fourth class was the most interesting: the specimen exhibited “complicated localized structures, some propagating”¹⁴⁴ (like a Life glider). He linked the four categories to behaviors recognized by researchers studying dynamical systems.¹⁴⁵

Cellular automata had long been studied as models of computation, but Wolfram speculated that the fourth class, and only the fourth class, could behave as a universal computer. (This would later be proved true.)¹⁴⁶ If cellular automata were analogs of the universe as a dynamical system and cellular automata were Turing complete, then this suggested that the universe itself could be considered a kind of universal computer. While some had intuited that the universe might be “a cellular automaton run by an enormous computer,”¹⁴⁷ Wolfram now provided mathematically compelling evidence that nature itself, considered through the lens of dynamical systems theory, was Turing complete—a machine capable of computing anything.¹⁴⁸

Turing, McCulloch and Pitts, and von Neumann had dissolved the boundaries separating mind, matter, and information; Wolfram pushed the dissolution further, suggesting that the very substrate of reality was equivalent, at least in some sense, to the computation of information. Perhaps physics was, deep down, at a fine granular level, an informational process. Reality might very well be a computer—or run on one. It might even be a cellular automaton. It was as if the entire network of analogies that sustained the edifice of computing, cellular automata, physics, biology, and dynamical systems quivered and threatened to collapse. Cellular automata simulated anything and everything.

By linking cellular automata to the study of dynamical systems and then tying it all back to computation, Wolfram elevated the contact language of cellular automata while bridging its far-flung communities. This seeded a network forum through which practitioners—whose foci spanned physics, biology, mathematics, computation, dynamical systems theory, and recreation—could exchange legitimacy, see themselves as part of a larger shared pursuit, and fashion new social networks and intellectual frameworks.¹⁴⁹ Consequently, Wolfram became a kind of network celebrity.¹⁵⁰

By the standards of academia, Wolfram’s work was a smash hit. In early 1983, Los Alamos National Laboratory hosted the first ever workshop for the many tribes of cellular automata. Life, the first blockbuster cellular automaton, served as a shared touchstone, but it was Wolfram’s work that interwove the splayed threads of scientific practice. The entire spectrum of cellular automata applications was represented: as models of self-reproduction, biology, physics, and chemistry; as models of complexity, chaos, and emergence; as a model of computation; and as computer architecture.¹⁵¹ Physicists, mathematicians, computer scientists, and biologists were in attendance, many of them leaders in the rapidly crystallizing field of complex self-organizing systems.¹⁵²

Many of these scientists set up shop down the road from Los Alamos National Laboratory, the birthplace of the atomic bomb, at the Santa Fe Institute. SFI’s founding in 1984 signaled the budding status of cross-disciplinary research into the “sciences of complexity,”¹⁵³ a catch-all for chaos, nonlinearity, self-organization, and emergence—the “surface complexity arising out of deep simplicity.”¹⁵⁴ In the quiet desert of New Mexico, physicists, economists, biologists, and computer scientists—including several Nobel laureates—inquired into everything from market economics and immunology to the origin of life. They made extensive use of computer simulations, which provided “a kind of theoretical laboratory for experiments.”¹⁵⁵

For complexity scientists, cellular automata became a standard technique. Gathering respectability from this association, the cellular automata tradition also received a boost from a quickly growing literature, as well as new tools. But special hardware, like a plug-in module for personal computers or a supercomputer, were unnecessary.¹⁵⁶ Personal computers like the Apple II, IBM PC, and Macintosh would do just fine. Hobbyists could write programs that simulated cellular automata with BASIC quite easily, use spreadsheet software, or use off-the-shelf programs like CA Lab (1989, for PC) or CASim (1989, for Mac). These proliferating tools were accompanied by a budding literature, from academic journals like Physica D to popular magazines like Scientific American and Discover.¹⁵⁷ Whether you played in the major league or little league, there was, by the end of the 1980s, a cellular automata book, article, or tool for you.

§§§

In 1980, NASA hosted a summer study that contemplated using self-replicating robots for space exploration, and von Neumann’s vision took flight once again. Self-replicating robots and lunar factories would do the grunt work of colonizing planets and exploring space.¹⁵⁸

[picture]

Self-replicating robots from a 1982 NASA publication.

To evolve these programs, Holland imagined specialized computer hardware inspired by cellular automata.

The 1980s saw revived interest in other aspects of self-reproducing automata as well. Burks’s student John Holland finally gained traction in his decades-long pursuit of computerized evolution, forging the field of genetic algorithms, and their student Christopher Langton successfully championed the discipline of artificial life. Cellular automata, complexity science, and artificial life were, after all, closely related fields with entwined histories. Holland helped bring the cosmology of “complex adaptive systems” into focus and played a key role in architecting SFI’s intellectual agenda.¹⁵⁹

In the late 1950s, Holland “began to wonder if you could breed programs the way people would say, breed good horses and breed good corn.”¹⁶⁰ This aim descended from von Neumann’s original research program, as did the special parallel computer Holland imagined, whose organization mirrored the cellular lattice. Multiple automata, or programs, would move around a cellular space with “a kind of ‘billiard ball physics,’” interact, and reproduce in their “‘environment.’” Such “self-duplicating systems” should lead to an “autocatalytic” process of artificial evolution—an “adaptive system” that bred optimal programs.¹⁶¹

Holland published Adaptation in Natural and Artificial Systems in 1975. It was a sleeper hit, selling hundreds of copies a year for the first five years. And then, alongside the escalating enthusiasm for complexity science and artificial life, interest exploded.¹⁶² This was part of a broader trend in the 1980s of revived interest in bottom-up approaches, like neural nets and situated intelligence, as the dominant artificial intelligence paradigm foundered.¹⁶³ Other factors help explain why it took so long for genetic algorithms to catch on. For one, it took Holland time to shed the cellular lattice,¹⁶⁴ an unnecessary encumbrance given that programs could be scored, selected, mutated, and bred without it.¹⁶⁵

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Schematic landscape of evolving creatures from Holland’s Adaptation in Natural and Artificial Systems.

Langton championed a revival of biological simulation that met great success in the 1980s under the banner of artificial life. Naturally, it was Conway’s Life that sparked his interest and Burks who pointed him in the right direction.¹⁶⁶ Langton’s early work is visually evocative, conjuring simple organisms like cells, worms, and coral or the meanderings of an ant.¹⁶⁷

Langton’s timing was impeccable, and his work found a ready audience. He presented at the first cellular automata workshop and published in its associated special journal issues.¹⁶⁸ He took a position at Los Alamos National Laboratory, which hosted the first “Artificial Life” conference in 1987, which Langton organized with sponsorship from SFI and Apple Computer.¹⁶⁹ As with the first cellular automata workshop, this convening knit together many previously isolated communities.¹⁷⁰

Langton’s work on the “edge of chaos”¹⁷¹ exemplifies how affiliated research lineages were recombining. Building on Wolfram’s work, Langton pondered what kinds of universes might be capable of supporting life. Classifying cellular automata universes, Wolfram had identified some that exhibited propagating patterns, speculating that these might be Turing complete. Arguing that life is predicated on a material substrate that affords computation—“the transmission, storage, and modification of information”—Langton asked what kinds of cellular automata support computation and, in turn, life. He found that the answer lay in systems at the “edge of chaos,” which he compared to the phase transition between “the solid and fluid phases of matter.”¹⁷² Life, computation, and chaos were kindred phenomena.

[picture]

Langton’s self-reproducing loops. Each loop has a sheath encasing the information that directs its activity. The debt to Codd’s work is apparent.

One-dimensional cellular automata that go from quiet (left), to the “‘edge of chaos’” (middle), and dense noise (right).

Through a series of empirical experiments, Langton found a kind of Goldilocks zone where “large spatial and temporal correlations” make possible the transmission, storage, and transformation of information. Computations are “special instances of the kinds of processes that occur in a physical system in the vicinity of a solid/liquid or a liquid/vapor transition.”

Perhaps the most exciting implication is the possibility that life had its origin in the vicinity of a phase transition, and that evolution reflects the process by which life has gained local control over a successively greater number of environmental parameters affecting its ability to maintain itself at a critical balance point between order and chaos.¹⁷³

Conways’ Life fascinates in large part because it is such a system. The phase transition was what Conway, in his search for Life’s rules, had sought: a Goldilocks zone poised between explosive growth and collapse, where dynamic configurations persist across time and space. In Conway’s words, they found this “extremely well-balanced” system by finding an equilibrium between “birth rules” and “death rules.”¹⁷⁴ Even if the edge of chaos had nothing to do with life or computation, this phase transition was clearly key to understanding cellular automata’s appeal—from scientists at Los Alamos and SFI to the hobbyists mesmerized by Life’s twinkling forms.

§§§

While long-isolated islands of the cellular automata tradition were consolidating into a network, some remained cut off or forgotten entirely. It’s worth reflecting, for a moment, on why, and to what effect. Visiting these remote isles, however, requires us to travel back in time.¹⁷⁵

In 1953, von Neumann invited the Norwegian-Italian theoretical biologist Nils Barricelli to come to the Princeton Institute for Advanced Study (IAS).¹⁷⁶ The IAS computer had recently come online, and Barricelli used it to simulate evolutionary processes. Notably, he explored the idea of symbiogenesis, whereby organisms combine into more complex wholes. (The mitochondria in our cells, which have their own DNA, are now understood as the result of such a merger—a heterodoxy successfully championed in 1967 by Lynn Margulis.)¹⁷⁷

Barricelli simulated one-dimensional worlds populated by numbers. Each row represented a snapshot in time, allowing a world’s evolution to be traced by following the downward cascade of digits.¹⁷⁸ As von Neumann had done, Barricelli represented an organism in terms of many elementary units: a set of interacting numbers. As a result, new assemblages were able to emerge: collections of numbers could interact and thus constitute a larger-scale being—as the symbiogenesis theory postulated. These number worlds exhibited evolutionary behaviors analogous to speciation, parasites, predation, symbiosis, and symbiogenesis.¹⁷⁹

[picture]

“Crossbreeding in numeric symbioorganisms,” from Barricelli’s precocious evolutionary simulations.

Future practitioners reinvented many aspects of his approach. Besides anticipating Wolfram’s one-dimensional space-time diagrams,¹⁸⁰ Barricelli made other precocious moves. Like Ulam and Conway, he set aside the complex machinery of a universal constructor and focused on evolutionary dynamics. In a related set of experiments from the early 1960s, Barricelli used artificial selection to evolve computer programs that competed with one another in “game” “performance tests.”¹⁸¹ By contrast, it took Holland a decade to ditch the two-dimensional cellular lattice, whose representational affordances may have spurred his imagination but also held him back. Barricelli thus anticipated the empirical approach to automata studies, genetic algorithms, 1D cellular automata, and space-time diagrams.

Why did Barricelli’s work remain relatively unknown? While a personality conflict with von Neumann has been proposed,¹⁸² other factors stand out. For one, he did not benefit from a welcoming intellectual climate, as Langton and Holland would in the 1980s. A commitment to symbiogenesis, a relatively unknown theory that complicates the hegemony of neo-Darwinism, couldn’t have helped. Moreover, Barricelli, like Ulam, failed to hitch his research to well-established scientific practices, which is precisely how Wolfram put cellular automata on the map. Barricelli’s work also highlights the importance of computer graphics. The relatively inexpensive microcomputers that Wolfram and Langton used, like the Apple II, allowed them to easily make pictures; Barricelli’s figures, however, required painstaking work. Using one of the few computers in existence, moreover, limited the reach of Barricelli’s research program. Life, on the other hand, was simple enough to be played with pennies or Go stones, but was published in 1970 when computers were less of a rarity.¹⁸³

§§§

By the 1980s, personal computers were everywhere, and simulations such as Life were poised to colonize them. Scientific American, which had laid the groundwork for decades, kept pace and introduced a new department “concerned with the pleasures of computation.” Computer Recreations, founded in autumn 1983, replaced Gardener’s recently shuttered Mathematical Games column.¹⁸⁴ “Computer Games” would have been an inappropriate title—that conjured visions of popular amusements, not the “vast territory between . . . VisiCalc and Space Invaders,” the province betwixt the computer as “an engine of business, a capitalist tool” and “a medium for entertainments so frivolous that they must be hidden from view, like comic books,” that the new department would survey. Videogames “may well be among the highest expressions of the programmer’s art,” but neither Space Invaders (1978) nor VisiCalc (1979) “engages very deeply the question of what a computer is and what it can do.”¹⁸⁵

What more enjoyable way, then, to probe the mysteries of computation than through cellular automata and, that classic crowd pleaser, Conway’s Life? For this generation of computer enthusiasts, many of whom had access to personal computers, it was just the thing. And a spreadsheet program like VisiCalc, with its two-dimensional matrix of cells and formulas, was perfectly suited to the job. The inaugural article explained how to harness the now-commonplace tools of computing for the timeless pleasures of cellular automata, which became a mainstay of Computer Recreations throughout the 1980s.

Scientific American devoted its September 1984 issue to computer software, with articles by leading computer scientists. Wolfram’s piece argued that “computation offers a new means of describing and investigating scientific and mathematical systems. Simulation by computer may be the only way to predict how certain complicated systems evolve.” Nature could be studied as an algorithm: “Scientific laws give algorithms, or procedures, for determining how systems behave,” so “executing a computer program is much like performing an experiment.” Visual apparatus received top billing, with a color cellular automaton snowflake occupying almost a full page and an illustrated primer on chaos theory. “Physical systems are viewed as computational systems, processing information much the way computers do. New aspects of natural phenomena have been made accessible to investigation. A new paradigm has been born.”¹⁸⁶

In August 1984, Discover ran a three-page piece on cellular automata entitled “Life and Death on a Computer Screen.” Like Stewart Brand’s 1972 Rolling Stone piece on Spacewar!, the article offered a short code fragment. But whereas Brand included only pseudocode, the “Do-It-Yourself Automaton” sidebar gave a complete twelve-line program written in BASIC, the lingua franca of hobbyist programming.¹⁸⁷ By the mid-1980s, households across America could read a magazine article, type in code, and run it. Cellular automata made beautiful pictures, inexpensive computers could run them, and the programming was easy.

Daniel Dennett, a philosopher with a popular readership, opined that “every philosophy student should be held responsible for an intimate acquaintance with the Game of Life . . . an essential tool in every thought-experimenter’s kit.” Life offered valuable lessons in ontology. Is the motion of a glider—patterns of blinking dots—real or apparent?¹⁸⁸ For composer Brian Eno, Life was a fascinating “shock-to-the-intuition”¹⁸⁹—“the most important new idea of the time”: simple rules could yield complex beauty.¹⁹⁰

The popularization of cellular automata in the 1980s accompanied an intellectual shift that traveled under multiple banners—emergence, connectionism, bottom-up, complex adaptive systems, self-organizing systems—and in many scientific disciplines.¹⁹¹ The philosophy of emergence is a sober companion to cellular automata, but outlandish cosmologies lurk at the margins. While some, like Wolfram, argued that cellular automata offered a novel approach for modeling physics,¹⁹² others went further, propounding the idea that the universe itself—our world—is in fact a cellular automaton. What we perceive as objects and activity—from household pets to subatomic particles—are the apparent results of a cosmic computer. Our universe, as some argue, may itself be a simulation.¹⁹³

Gazing into a swirling symphony of turbulent matter, creeping beings, and writhing forms, cellular automata practitioners saw universes at once familiar and strange. What had felt infinite now seemed a trifle. Apparently solid objects dissolved into shimmering illusions. A thing that seemed to move might in fact be an emergent epiphenomenon of some more basic reality. Perhaps we ourselves, like gliders, are nothing more than fleeting spatiotemporal patterns; trembling arabesques sophisticated enough to perceive a world of things, including ourselves—as well as the illusion that underpins this apparent reality.

The history of cellular automata—and computing writ large—is animated by powerful analogies: between mind and machine, data and device, organism and automaton, information and entropy, computation and physics. The belief that we live in a simulation pointedly illustrates the hazards of analogical collapse. Losing track of the as if, which animates an analogy, means mistaking a machine for a mind or reality for a computer. Such ideas trade on simulation’s seductive potencies and resonate, in no small part, because of how they throw into sharp relief, and inadvertently lampoon, the metaphysics of computing.¹⁹⁴

Fractals, which filtered into mainstream culture alongside cellular automata, also afforded a glimpse of infinity in a teacup. The Mandelbrot set—the most iconic fractal of them all—adorned Scientific American’s cover in August 1985. “A computer microscope zooms in for a look at the most complex object in mathematics,” proclaimed the article, which helped popularize the Mandelbrot set,¹⁹⁵ and gave enough detail for an amateur programmer to reproduce them at home.¹⁹⁶

Chaos theory wasn’t far behind. In 1987, Computer Recreations ran a piece on the “Strange Attractions of Chaos,” and science journalist James Gleick published Chaos, which became a bestseller and Pulitzer Prize finalist.¹⁹⁷ Rudy Rucker, a mathematician and science-fiction author, teamed up with Autodesk to produce CA Lab (1989), a cellular automata package for personal computers.¹⁹⁸ A year later Rucker and Autodesk, in collaboration with Gleick, published Chaos: The Software (1990).¹⁹⁹ “Now you can explore CHAOS in a hands-on, visual way!,” “Explore strange attractors,” “Discover your favorite Mandelbrot regions,” “Make your own fractal landscapes,” and “Generate never-before-seen chaos patterns in color and sound” promised an advertisement in Scientific American.²⁰⁰ Rucker hoped these products would inaugurate “an ongoing Science Series” at Autodesk.²⁰¹ As a science writer, science-fiction author, and mathematician, he was an adept cultural translator. If computer simulation, chaos theory, and cellular automata were to go mainstream, Rucker might well be the midwife. Both programs were reviewed in the Whole Earth Review, a forerunner to Wired magazine.²⁰²

The first “Artificial Life” workshop, in 1987, convened diverse communities and illustrated the subject’s broad appeal. A. K. Dewdney—the long-standing author of Scientific American’s Computer Recreations column—promoted the event to his readers, organized the workshop demo sessions, and created the prize ribbons and certificates for the “artificial 4H-Show.” Richard Dawkins, a biologist and popular science writer, presented a paper on his Blind Watchmaker program (available for purchase).²⁰³ Gleick covered the event for the New York Times, which is where he first met Rucker. As a cyberpunk author who wrote books about intelligent robots, it was natural, Rucker reflected, for him to be there too.²⁰⁴ “As ideas from Artificial Life infiltrate the culture,” wrote Mitchel Resnick, a faculty member of MIT’s Media Laboratory and one of Seymour Papert’s students, “it is important to develop ways to share these ideas with children.” He presented a paper on using Lego and Logo as an “Artificial Life Toolkit” that children could use to “build artificial ‘animals,’” and noted that Logo itself had emerged out of cybernetic critters, and a desire to have children make them.²⁰⁵

Kevin Kelly, the future founding editor of Wired, camped out at Bandelier National Monument, alongside ancient Pueblo structures, “rustling grass, drifting stars, and hooting owls,” to attend “Artificial Life”—his first ever science conference—and provide coverage for Whole Earth Review. Kelly took the photographs that accompanied an article by Langton, and artificial life became a running theme in Whole Earth Review, alongside coverage of other simulations, such as Biosphere 2 and SimCity, that also made their way into popular culture.²⁰⁶ By the early 1990s, popular science books, science fiction, and articles on artificial life, emergence, and complex adaptive systems had proliferated and multiplied into the mainstream²⁰⁷—from Steven Levy’s Artificial Life (1992) to Steven Spielberg’s Jurassic Park (1993), a film starring computer-generated dinosaurs and a chaos theorist who grapples with the harrowing consequences of synthesizing life. But what about software—the artificial life simulations themselves? Would these become popular practices?

Rucker’s dream didn’t come true—at least not in the way he expected. No additional science simulations from Autodesk were forthcoming. It would be another company, Maxis, that brought cellular automata, complex systems, artificial life, and computer simulation to a truly popular audience with titles such as SimCity (1989), SimEarth (1990), SimAnt (1991), and SimLife (1992). If the Santa Fe Institute represented the acceptance of cellular automata, computer simulation, artificial life, and complex adaptive systems within the halls of academia, then Maxis represented the culmination of a decades-long popularization of those same scientific and philosophical ideas. The writers of Mathematical Games and Computer Recreations intermingled with academics to produce columns for an educated lay audience, but Maxis went a step further, translating such material into broadly accessible products. Journalists eagerly connected the dots, interweaving academic and pop culture efforts, as did the practitioners themselves.²⁰⁸

As the Santa Fe Institute made inroads into an “emerging general science of complexity”²⁰⁹ they cultivated a relationship with Maxis. For Will Wright, SimCity’s creator, the association helped him imagine himself as a science popularizer, and exposed him to leading-edge research in a variety of fields. As SFI recognized, Maxis brought their research to new audiences. When Maxis invested in an enterprise simulation business, SFI saw an opportunity to develop their own donor network. For Maxis, the affiliation burnished their reputation as a purveyor of more than popular amusements, which helped them attract venture capital and establish a serious simulation business. Maxis and SFI helped define the terms by which the other was interpreted by particular communities, to the advantage of both.²¹⁰

This exchange of legitimacy and ideas was no accident, nor was the formation of new social networks. In 1988, their first full year of operation, SFI proclaimed that “the study of complexity demands knowledge and techniques that cut across disciplines.”²¹¹ The computer, that potent analogical machine, played a crucial role. The right analogies allowed simulationists to bridge intellectual frameworks and communities of practice.²¹² A cellular automaton could, for instance, represent an evolving spiral galaxy, human social system, or ant colony. SFI’s interweaving of intellectual disciplines was also predicated on an explicit strategy of cultivating “mutually supportive research networks.”²¹³ So how did gray-haired Nobel laureates—veterans of World War II’s interdisciplinary research culture—and Wright, a bright-eyed college dropout, find common ground? They all spoke the flexible contact languages of complexity science, computer simulation, and cellular automata. What’s more, Maxis, SFI, and others in their orbit all used the same, now widespread, tools: personal computers with sophisticated graphical displays. When Maxis arrived in 1989, it did so practically on cue, linking the scientific vanguard with popular culture by providing simulation playthings to well-primed audiences.

5

A Children’s Construction Set

Despite working at Hewlett-Packard, where he designed calculator chips, Steve Wozniak didn’t “know what companies use computers for; I only know what I like to use them for, and it’s games.” He’d been interested in circuits and games since childhood. In sixth grade he had built a Tic-Tac-Toe playing machine out of “transistors, resistors, diodes, and power supplies.”¹ Now, designing the successor to the Apple Computer, which he and Steve Jobs began selling in 1976, Wozniak set his sights on games. At the time, hobbyist microcomputers, like the Altair, were kits. Fully assembled, the Altair was a box with some switches and lights. After programming it—assuming you had done everything right—the lights would blink on and off. But the Apple Computer 1 had a video display; you could hook it up to your television, where it could display scrolling text. His new machine, the Apple II, would be even better.

“A lot of the features of the Apple II went in because I had designed Breakout for Atari. I had designed it in hardware. I wanted to write it in software now.”² The Apple II would have color graphics, sound, and a paddle—because that’s what you needed to play games. Videogames didn’t have color yet, but Wozniak had seen a “hypnotizing” demo on the Atari factory floor of a color-changing dot that bounced back and forth. It reminded him of “a psychedelic light show at a concert” and inspired him to devise a way to get color video out of his computer. It “was designed to be a game machine as well as a computer” because Wozniak believed that this was key “to get people to start buying these machines.”³

Wozniak knew, as did other computer enthusiasts, that the key to games wasn’t just graphics but a programming language called BASIC. He had learned it in high school, on a dial-up time-sharing terminal. His “Game BASIC,”⁴ as he called it, would plug Apple II users into a vibrant hobbyist software scene. One of the bibles of the Homebrew Computer Club he frequented was 101 BASIC Computer Games, and Wozniak wanted his own computer so he could type in those games.⁵ After he got it working, he showed Jobs how he

could change the colors of things, change the shape of the paddle, and change the speed of the ball with an easy BASIC command. He and I looked at each other—we were both kind of shaking, because we knew that the world of games was never going to be the same. Now [games] were software. Until then, there weren’t software games in the arcades.⁶

Arcade games used dedicated circuits to run game logic and generate video signals, not programmable microprocessors.⁷ Wozniak knew this well; he had once built his own Pong clone out of “28 little $1 chips,” and he had designed Breakout for Atari using this approach. But now “that a fifth grader could program in BASIC and make games like Breakout? This was going to be a new world; we saw it right then.⁸” Demonstrating Breakout, written completely in BASIC, to the Homebrew Computer Club, was “the most satisfying day of my life.”⁹

The Apple II, released in 1977, was an immediate hit. Combined with the floppy disk drive Wozniak designed, Apple II enthusiasts could easily create and share software. Anybody with enough money could now have access to a programmable computer with interactive graphics. BASIC was built in, unlike the Altair, whose owners clamored for it. A tidal wave of software followed: games like Pinball Construction Set and tools like VisiCalc, the first computer spreadsheet. The Apple II unleashed a new generation of software makers and proffered a distinct vision of computing: sensorially rich, playful, expressive, and for everyone.¹⁰

§§§

Will Wright’s first computer was an Apple II, which is where he learned programming (with BASIC) and encountered a flourishing game scene. One title that captivated him, and pointed the way toward SimCity, was Pinball Construction Set, the 1982 hit that illustrated how a computer game didn’t have to be a game at all but could be a toy—in this case, a construction set underwritten by a graphical user interface (GUI). It pointed, as a result, to the future of computing. Launched in 1984, Apple’s Macintosh was a harbinger of this change and the platform where SimCity first met a receptive audience.

But the GUI was more than the new face of computing; it represented a particular orientation toward computers and the world more generally. Well suited toward playful, educative, and creative purposes, the GUI dovetailed with SimCity’s design. The inventors of the GUI hadn’t set out to give computers an accessibility makeover; that was a side effect of their true aims, which lay in transforming the computer into a medium for learning by doing, making, and playing.

Anxieties about responsible stewardship of computer technology and equitable access to tools and knowledge motivated researchers and advocates in the 1960s and 1970s toward efforts that might democratize computing. This gave rise not just to new technologies like BASIC, Logo, and Smalltalk, but influential new cultures of computing. BASIC, for example, helped promote software folk cultures largely organized around games and, perhaps most important, the idea that the computer was a democratic, creative, and playful medium.¹¹

Looking to expand the circle of who might use computers, these practitioners looked to liberal arts undergraduates (at their own elite institution), gifted and privileged high schoolers, and young children—juvenile versions of themselves, in other words.¹² The researchers working with children brought epistemic, cognitive, and learning considerations to their work and celebrated visual, tangible, and playful modalities like graphics, music, movement, and games. Multimedia saturated and easy to use, today’s ubiquitous computers were prefigured by these efforts.

While the GUI heralded a new way to compute, it also marked a return to early twentieth-century modalities and theories of mind. The special emphasis computer pioneers gave to graphics and cognition suggests the fingerprints of engineers like Vannevar Bush, who valued thinking across graphical, tangible, and symbolic modalities. For Bush, multimodal thinking wasn’t just for experts but could help beginners master new kinds of expertise.¹³ By looking to graphics, games, and construction sets, the cultivators of these new computing cultures adopted approaches that expanded the appeal and accessibility of computing.

This chapter, then, is about how the computer became a construction set for children. Wozniak imagined fifth graders making computer games. How did he come to picture this? And how did such things come to pass?

Graphics

Computer graphics appeared alongside the first modern computers.¹⁴ From their inception in the late 1940s, interactive computer graphics were used for diagnostics, demos, games, and design tools. These familiar applications, while apparently precocious, can also be seen as the offspring of a mature engineering culture. While it would take decades for the new medium of interactive computer graphics to blossom, its affordances were swiftly exploited by engineers well versed in graphical thinking and simulation.

MIT’s Whirlwind, for instance, initially employed graphics as a diagnostic. Desiring a way to see which, if any, vacuum tubes had failed, Whirlwind engineers turned to visualization. Using a cathode ray tube as a display—the kind of vacuum tube employed in televisions and also, at the time, for computer memory—they wrote a program called “Waves of One,” which drew “spots” of light corresponding to storage tubes. Failures could now be seen. But to know precisely which tube had failed, Whirlwind’s technical director spent the afternoon devising a “light gun”—point at the spot you were interested in and the computer would report the corresponding tube’s address.¹⁵ (Like the display, the light gun was familiar technology; it had been employed in arcade games for some time.)¹⁶

This confluence of visualization, pointing, and interactive computing unleashed a tidal wave of creativity. The light gun could be used to “erase” spots of light, yielding the first bitmap graphics editor. Whirlwind’s programmer wrote a “Bouncing Ball” program—the first computer animation—which evolved into the first animated computer game: try to get the ball to fall through a hole in the floor by turning knobs to adjust the bounce frequency. A master’s student wrote a graphics program to help him design an antenna array. Later, when the Whirlwind project reoriented toward air defense, it displayed interactive maps for tracking and intercepting airplanes.¹⁷

[picture]

Early computer graphics on Whirlwind. Left: Using the “light gun” to erase “spots” and draw “MIT.” Right: “Bouncing Ball” game.

The charisma of interactive graphics was immediately apparent. MIT’s public relations department got involved from the start, and a 1951 CBS television piece followed suit. In it, Whirlwind welcomes the show’s host with graphics (“HELLO MR. MURROW”), animates a simulated rocket, and plays the tune “Mary Had a Little Lamb.”¹⁸

That Whirlwind afforded tightly knit interaction between humans and computers should come as no surprise. Whirlwind had, after all, begun life as a flight simulator (within MIT’s Servomechanism Laboratory). Real-time performance, as a result, was of paramount importance, distinguishing Whirlwind and its descendants with a responsiveness that would prove crucial to the history of interactive graphics.¹⁹

Inspired by Whirlwind’s tantalizing interactivity, engineers reproduced its unique architecture. Wanting a computer that would also feel exciting and encourage drawing, games, and creativity, the designers of the TX-0, a transistorized adaptation of Whirlwind, also included interactive graphics and sound capabilities.²⁰ An exuberant display of creative technology followed, ranging from hypnotic animations to toys, games, and design tools.

Mouse in the Maze (1959), likely the first software toy to feature a virtual character, was a computerized remake of Claude Shannon’s “Theseus” (1952), in which an electromechanical mouse searched a maze with movable partitions for what Life Magazine called the “bait.” In the TX-0 version, the mouse sought out cheese or martinis (which made the mouse tipsy), had limited energy, and made snappy comments if the goal was unreachable.²¹ Shannon, a master toymaker with a keen interest in games, deftly passed among academic, applied, and popular realms. His charismatic and irreverent creations were well suited for exploring the playful possibilities of interactive graphics.

The earliest two- and three-dimensional computer-aided design (CAD) software was also developed during the 1950s.²² This effort was catalyzed by the MIT Servo Lab’s investigation of numerically controlled machine tools. To help generate the control instructions, researchers created software tools for nonprogrammers. The addition of interactive computer graphics set the stage for Sketchpad.²³

[picture]

Sketchpad figures from Sutherland’s dissertation.

Ivan Sutherland’s seminal Sketchpad (1963) showed how drafting, and graphics more generally, might be reimagined with interactive computing. By enacting graphically specified constraints, Sketchpad could help users draw perfect circles, keep lines parallel, and even simulate a truss under load. Complex drawings could be easily built up out of “instances” derived from a “master” drawing. One could work with a drawing “as if through a magnifying glass” via a “window-into-the-page.”²⁴ Ivan Sutherland’s older brother William completed his dissertation in 1966 and created a system for programming by drawing pictures.²⁵

It was Shannon who had elaborated Bush’s graphical diagramming language for the differential analyzer²⁶ and oversaw the Sutherlands’ dissertation projects. Shannon had encouraged Ivan Sutherland to “try graphics”²⁷ and look beyond “conventional drafting tools” and toward the new capabilities of computation.²⁸

These experiments highlighted how computers might transform long-standing graphical traditions. Engineers had long used graphics to think through problems, communicate, and make specifications, but this was a manual process. Although the differential analyzer automatically drew output curves, the input curves were traced by hand. And somebody had to read a differential analyzer connection diagram and then manually configure the gears and shafts. Now, however, the graphics were fully absorbed into an automated process. The computer not only assisted the act of drawing; it brought these drawings to life through the magic of simulation. Lines could be kept parallel. Draw the walls of a maze, and a simulated mouse would find its way. A picture could tell the computer what to do, and the computer could show you what it was doing. Interactive computing linked together graphic, symbolic, and machine representations as never before.²⁹

As practitioners were well aware, interactive computer graphics fostered a new kind of “Man-Machine Graphical Communication.”³⁰ Some, like the cybernetically inclined J. C. R. Licklider, theorized this as “Man-Computer Symbiosis.”³¹ Pointing and pantomime play a vital role in communication, helping us to focus on and create joint attentional scenes. Now machines could enter the conversation.³² But as the two-player game Spacewar! (1962) made clear, this was more than dialog. Flying spaceships and bouncing balls were more akin to dance, sports, or games. Interactive graphics offered a tantalizing sense of embodiment—a relic of the disappearing culture of analog—plus the immersive enticements of imaginative worlds. And these worlds, it was increasingly clear, might appeal to more than engineers. Reproduced in endless variations, Spacewar! achieved folkloric status, becoming a touchstone reference for how computing might appeal to the masses.³³

Folklore

Wozniak wanted BASIC for his colorful warbling machine. Games would sell the Apple II, and he wanted to write those games in BASIC. But why? What was special about a programming language? The answer, it turns out, has everything to do with an early effort to put computers in the hands of nonspecialists. Where, after all, did all those BASIC games come from?

Around 1956, Thomas Kurtz and John Kemeny, two mathematics professors at Dartmouth, took up programming on an early IBM computer. The introduction of FORTRAN in 1957 made a strong impression on them, as its compiler translated mathematical expressions into machine instructions. While the programs didn’t run as fast, they were easier to write. A couple of years later, when Dartmouth got its own computer, Kemeny, Kurtz, and their students began crafting their own high-level programming languages.³⁴

BASIC was motivated by a social quandary. At the time, Dartmouth was an elite all-male liberal arts university, and most students did not major in science or engineering. These students, Kemeny and Kurtz knew, were likely to become “the decision makers of business and government” that oversaw society’s use of this potent new technology. But how, they wondered, could “sensible decisions about computing and its use be made by persons essentially ignorant of it?” Worried about a divide between “two societies,” they decided to teach liberal arts majors about computers. Lectures would be senseless; one must, as they had, learn to program.³⁵

To accomplish this, they needed a “friendly” system that would be “easy to learn and use.” It could not, under any circumstances, use punched cards; the nontechnical students wouldn’t have accepted it. With punched cards, programs were composed and then perforated onto cards, which were then dropped off, like dry cleaning, for processing. (Interactive computers were an exorbitant rarity.) To solve this problem, Kemeny and Kurtz adopted a nascent technique called time-sharing. An operating system would rapidly switch a computer’s attention among multiple users connected via inexpensive terminals, giving interaction a conversational back-and-forth quality. According to Kurtz, “The design of BASIC and of the operating system” are “inextricably meshed,” enabling users to seamlessly switch between editing and running programs (like the future Apple II).³⁶

Existing languages were too complex, or ill suited to nontechnical majors, so they decided to develop their own. Making a simple language for a “casual programmer” required substantial implementation complexity. While BASIC programs didn’t run fast, they were easy to write. That was the trade-off. To ensure widespread exposure, they incorporated programming into the math courses most students took. And they invited students to work on self-directed projects that interested them, believing that this would motivate learning.

The computer would be democratized, not cloistered, the same way that libraries make knowledge broadly accessible. The same student ID that granted library access also granted computer access; no special permission was needed. As in a library, students had privacy: the license to pursue knowledge and activities without anyone looking over their shoulder. And since Dartmouth, unlike MIT, didn’t have any lucrative government contracts, there would be no conflict over computer time between students and researchers.³⁷

Sputnik’s launch in 1957 had spurred a Cold War push for science education. Using more National Science Foundation grants, Kurtz and Kemeny embarked on “missionary work,” putting terminals in high schools and colleges across New Hampshire.³⁸ Hewlett-Packard and Digital Equipment Corporation (DEC) put BASIC on their commercial time-sharing systems. A creative outpouring of BASIC programs followed—including many games. In 1973, DEC published 101 BASIC Computer Games.³⁹ Privileged students like Bill Gates (b. 1955) and Steve Wozniak (b. 1950) learned programming in high school with BASIC on dial-up time-sharing systems. Like Spacewar!, BASIC became so popular that it inspired numerous re-creations that reproduced the original in kaleidoscopic variation—like the one Wozniak later made for the Apple II.⁴⁰

BASIC was promoted by educators and enthusiasts like Bob Albrecht, who since the early 1960s had been teaching programming to gifted high school students. After encountering BASIC, Albrecht vowed to never teach FORTRAN again. He noted that “it was easier to teach kids how to program in BASIC if the subject matter was a game.” Albrecht brought expertise, funding, and connections from the corporate computer world to the countercultural scene. After moving to San Francisco, he cofounded the Portola Institute, an educational nonprofit that incubated the Whole Earth Catalog and People’s Computer Company (PCC).⁴¹

Founded by Albrecht in 1972, the PCC offered storefront computer access and published a newspaper. Echoing Kurt and Kemeny’s conundrum but in countercultural form, the first issue’s cover declared that computers are “USED TO CONTROL PEOPLE INSTEAD OF TO FREE THEM.” It was time for a “PEOPLE’S COMPUTER COMPANY.” Albrecht and the PCC published books like My Computer Likes Me When I Speak in BASIC (1972), which sold over 250,000 copies, and What to Do after You Hit Return, or P.C.C.’s First Book of Computer Games (1975). Dr. Dobbs Journal, a popular programming publication that carried on for almost forty years, spun out of PCC. And it was Albrecht who brought the Altair 8800 computer to the first Homebrew Computer Club meeting.⁴²

Kurtz and Kemeny had hoped to inspire a new relationship between society and computers. By the late 1970s, it was clear that they had succeeded. BASIC was more than a programming language. It undergirded vibrant communities who used it for casual programming, a medium of creative expression, a learning tool, and plaything.⁴³ It underwrote a sprawling “folklore”⁴⁴ of games and software. It was for these reasons that Altair’s users clamored for a version of BASIC that Altair’s manufacturer struggled to provide. (They contracted two young programmers, Bill Gates and Paul Allen, to develop it, giving Microsoft its first product.) BASIC fostered communities of “computing citizens”⁴⁵ alongside a liberal arts orientation toward computers that stressed open access, self-directed activity, and creative expression. Computers were tools for students and hobbyists—not just academic, military, and industry elites.⁴⁶

Children

Who and what were computers for? By the late 1960s, there was no simple answer. BASIC illustrated how a novel orientation could guide technical interventions that brought computers to new communities, transforming the shape and history of computing. All this blossomed from a simple question: What if computers were for liberal arts majors?

What, then, if computers were for children?

In the mid-1960s, an educational technology group at BBN, a consulting firm with close ties to MIT, began experimenting with programming languages for children. Their language would need to be less mathematical than BASIC, as children don’t know algebra. “The notion,” according to Wallace Feurzeig, the group’s founder, “was that computers were not just for doing science or math or technical things; they could be used for language, for music, for all kinds of things.”⁴⁷ Since language was envisioned as a key application, and Feurzeig, along with his collaborators, were close to MIT’s artificial intelligence (AI) research community, they settled on a variant of Lisp, dubbing it Logo, after the Greek word logos, which means “word” and “reason.”⁴⁸

Seymour Papert, a recent arrival at MIT and codirector of its Artificial Intelligence Laboratory, consulted on the project, shaping the language’s functional specifications. Papert had recently arrived from Europe, where he had worked with renowned psychologist Jean Piaget. In 1953 Papert left South Africa, his birthplace, for a postdoctoral fellowship in England, fearful that authorities wouldn’t permit him to leave on account of his antiapartheid activities. Finding Cambridge insufficiently politically radical, he shifted to Paris, where he met Piaget, who needed a mathematician to help with a project involving children and numbers. Although Piaget is best known for his work with children, Papert writes that⁴⁹

his real interest was epistemology—the theory of knowledge—which, like physics before Newton, was considered a branch of philosophy until Piaget came along and made it a science in its own right. . . . Piaget is best-known for demonstrating that, so far from being empty vessels to be filled with knowledge (as traditional pedagogical theory has it), children are actually active builders of knowledge—little scientists who are constantly creating and testing their own theories of the world.⁵⁰

While studying “how children become thinkers”⁵¹ in the Swiss Alps with Piaget, Papert sought computer access to pursue his interest in artificial intelligence. He found it back in England, on the Pilot ACE (a machine designed by Alan Turing). At a conference, he met Marvin Minsky, who recruited him to MIT in 1963, where they would soon codirect the Artificial Intelligence Laboratory. (Warren McCulloch helped Minsky secure the appointment.)⁵²

The emerging field of AI, as well as Piaget’s work with children, shared a common goal: explaining how the mind works. This wasn’t always fashionable. American psychology had come to be dominated by an approach called behaviorism, advanced by the work of scholars like Ivan Pavlov and B. F. Skinner, in which minds were black-boxed. Only the inputs and outputs—the outwardly visible behavior of an animal (or person)—were seen as worth studying. But the 1950s saw the emergence of a new field (or fields), cognitive science, that attempted to open up this black box and study the “representational and computational capacities of the human mind and their structural and functional realization in the human brain.”⁵³ Cognitive scientists took inspiration from scholars like Jean Piaget and A. R. Luria (a Lev Vygotsky protégé) who took a more holistic stance toward brain, mind, and culture. It was a multifaceted effort encompassing philosophy, linguistics, anthropology, neuroscience, computer science, and psychology.⁵⁴

Computers and children offered distinct avenues for studying the mind. The developing minds of children offered a glimpse into the nature of thought and how people learn to think. As concrete embodiments of thought, computers were potent objects for contemplating cognition. Computers are, after all, simulations of minds. The work of Bush, Turing, and John von Neumann illustrated how particular philosophies of mind led to, and are baked into, modern computing.⁵⁵ Artificial intelligence researchers continued this project, simulating minds with computers. Papert described “one version of”

what people actually do when they “do AI.” They select a piece of human mental activity, say, playing chess or seeing a cat; then they write a computer program that will do something similar; and finally they discuss, sometimes at very great length, whether the computer program “really” does what the human did. I had been engaged in a lot of this kind of activity and knew it had stimulated me to exciting and productive insights into human thinking. True, I did not often really think that the AI program was successful in fully imitating a person; but even when the differences were more prominent than the similarities, the discussion of the machine still produced valuable insights into how people think—and into how they do not think.⁵⁶

Reflecting on how computers had shaped his own thinking and how captivating he found them, Papert flashed on an “‘obvious’” idea:

What computers had offered me was exactly what they should offer children! They should serve children as instruments to work with and to think with, as the means to carry out projects, the source of concepts to think new ideas. . . . Could access to computers allow children something like the kind of intellectual boost I felt I had gained from access to computers at MIT?⁵⁷

He recalled a “playful speculation” that he and Piaget had engaged in about

what would happen if children could play at building little artificial minds. I had been saying that the essence of AI was to make theoretical psychology concrete. So (since concreteness is supposedly what children thrive on) in principle perhaps some elementary form of it could become a children’s construction set. If psychologists could benefit from making concrete models of the mind, why shouldn’t children, whose need was even greater, also benefit?

Piaget liked the image of taking one of his favorite aphorisms—“to understand is to invent”—into a new domain.⁵⁸

The children, in effect, would become cognitive scientists and join MIT’s hacker culture of “learning by doing.”⁵⁹ But computing, as constructed, favored certain kinds of people and minds. For one thing, children didn’t program computers. Even BASIC, which arose around the same time and whose target audience was liberal arts students, was taught at the college level in conjunction with mathematics. Young children didn’t know algebra. New modes of programming would be needed. Papert believed, as the developers of BASIC had already found, that making a more friendly environment “required lots of surplus computer power.”⁶⁰ This was a huge departure from prior educational computing efforts, which basically amounted to fancy flash cards.⁶¹ The computer was programming the child. But what if the child programmed the computer?

As Papert and Sherry Turkle would later argue, computing was defined by the hard analytical culture of math and logic, but it had a softer, more pluralistic side.⁶² Experienced programmers knew that there were many ways to be right and wrong and make incremental progress toward a goal. They stood things up quickly and knew that debugging—diagnosing and fixing issues in their own work—was part of the journey. This was unlike how children are typically taught math, where answers are either right or wrong.⁶³

Like Kurtz, Kemeny, Albrecht, and others, Papert saw liberalizing computers as a political act. It was

no longer acceptable that blind social forces be allowed to assign stations in life through differences in access to learning. . . . In this spirit, Logo was fueled from the beginning by a Robin Hood vision of stealing programming from the technologically privileged (what I would in those early days in the 1960s have called the military-industrial complex) and giving it to children.⁶⁴

The Logo team wanted to work with young children but decided to start with junior high students. Using Logo, kids played games, made games, and wrote sentence and poetry generators. Recognizing that walking and drawing were activities that younger kids could engage with and reason about, they introduced a programmable “turtle”—a pint-sized robot—that could move around, sense its environment, and draw on paper.⁶⁵ Children liked to ride it, direct its movements, and play with it. Moreover, the robot helped children link their own embodied experience of movement to geometry and mathematics. To make a straight line, you walk straight; to make a circle, you step forward while slowly turning.⁶⁶

Papert argued that learning is best done through embodied experiences and cultural practices relevant to the learner, a point he illustrated with his childhood fascination with automobiles and how an intimacy with gears helped him assimilate mathematical ideas.⁶⁷ The idea echoes Bush’s story of the high school–educated mechanic who learned basic calculus by working on the differential analyzer, getting it “under his skin.”⁶⁸ Graphics, bodies, and mathematics were combined again, this time with children, computers, and robots.⁶⁹ Logo called attention to a dialectic between an ascendant analytical culture of computing and an older analog one. While digital computing tended toward abstraction and invisibility, the older graphical, embodied, and empirical traditions persisted. Children would help bring this approach to the fore.⁷⁰

The crucible of field testing unleashed an extraordinary output of creativity from both researchers and children. “Twenty things to do with a computer,” a 1971 paper by Papert and Cynthia Solomon, summarized how Logo was being used: art, artificial intelligence, playing and recreating Spacewar!, geometric patterns, animation, music, playing with randomness, manipulating blocks with robots, light shows, concrete poetry, impish quizzes, physics and control systems, and puppetry. It was an iterative, seat-of-the-pants learning process for children and researchers alike.⁷¹

Logo coincided with a revival of progressive educational ideas in the late 1960s that had lost currency in the wake of Sputnik. This found expression in the politically charged work of educators like Doreen Gehry Nelson, with her city building method, and the design of new open classroom schools. Papert agreed “with Dewey, Montessori, and Piaget that children learn by doing and by thinking about what they do.”⁷² Educators like John Dewey and Caroline Pratt had emphasized the child’s context, interests, and creativity over rigid curricula. By the early 1980s, Logo was filtering into public schools on, in many cases, Apple II computers.⁷³

Papert came to call his approach constructionism, a wordplay on Piaget’s theory of constructivism, which held that knowledge isn’t transmitted to a learner, but constructed by them. Papert’s constructionism refers to the “construction set” nature of both knowledge and its supporting physical activities:

Construction that takes place “in the head” often happens especially felicitously when it is supported by construction of a more public sort “in the world”—a sand castle or a cake, a Lego house or a corporation, a computer program, a poem, or a theory of the universe. Part of what I mean by “in the world” is that the product can be shown, discussed, examined, probed, and admired. It is out there.⁷⁴

[picture]

Child using Radia Perlman’s preschool “Button Box” to draw with Logo’s turtle robot.

Constructionism held that children learn best when seeking knowledge for their self-motivated projects and that they should be scaffolded “morally, psychologically, materially, and intellectually in their efforts.”⁷⁵ The best way to learn French is not in a foreign-language classroom, but to live—or even better, grow up—in France. In this vein, Papert argued that microworlds offered a chance for people to live in “Mathland”—or any number of lands. (Children, of course, could build their own microworlds.) Computers, through their incredible power to simulate, could conjure whole worlds—tangible representations that suited a child’s interests, culture, and embodied experience.⁷⁶

By uniting the towering prestige and ethos of MIT (“learning by doing”⁷⁷) with Piaget (“to understand is to invent”), Papert brokered an exchange of legitimacy, ideas, and identities between engineers, educators, cognitive scientists, and children. He became the emblematic celebrity of the Logo network and a charismatic disseminator of ideas that would profoundly shape the future of computing.⁷⁸

Media

It wasn’t until Apple introduced the Macintosh in 1984 that the work of Xerox’s Palo Alto Research Center (PARC), most notably the graphical user interface, began to reach a popular audience. (Actually, Bill Budge’s Pinball Construction Set beat Apple to the punch—a point we’ll return to.) Seeing that computers posed a potential threat to their lucrative copying business, Xerox established PARC, in 1970, as a hedge. Increased congressional oversight of the Defense Department, in the wake of the Vietnam War, upended funding for free-spirited computer research, helping PARC recruit top talent from across the country.⁷⁹

“Much of the philosophy on which our work is based was inspired by the ideas of Seymour Papert and his group at MIT,”⁸⁰ wrote Alan Kay, who played an influential role in PARC’s computing efforts. Attending an early Logo field test at a school with Feurzeig, Papert, and Solomon, Kay was “amazed” to see children writing computer programs.⁸¹ Their art, animations, music, and artificial intelligences helped him see computers in a new way: as a dynamic medium. Computers weren’t

[picture]

Kay’s illustrations of children playing Spacewar! on the proposed DynaBook.

a personal dynamic vehicle, as in Engelbart’s metaphor opposed to the IBM “railroads,” but something much more profound: a personal dynamic medium. With a vehicle one could wait until high school and give “drivers ed,” but if it was a medium, it had to extend into the world of childhood.⁸²

Kay described his intentions in a 1972 report, “A Personal Computer for Children of All Ages.” Whereas Kurtz and Kemeny looked to the library for inspiration, Kay looked to books, which “allow centuries of human knowledge to be encapsulated and transmitted to everybody.”⁸³ Kay “remembered Aldus Manutius who 40 years after the printing press put the book into its modern dimensions by making it fit into saddlebags.”⁸⁴ Intentions and ideas shape media, not just technology.

Following Marshall McLuhan’s study of the transformative power of the book and his analysis of virtually every technology as media, Kay called his proposed device the “DynaBook.” Smallness stood for ubiquity and accessibility, suitable for adults and children alike. “Dynamic simulation”⁸⁵ would enable it to be “active . . . rather than passive,” “like the child.” It would have the “attention grabbing powers of TV” but be under the child’s control, not that of corporations. It would be like “a piano . . . a tool, a toy, a medium of expression, a source of unending pleasure and delight.”⁸⁶ Users would be able to buy content like encyclopedias and pulp fiction. Like Logo’s creators, Kay envisioned children playing Spacewar! and, motivated by their enthusiasm and a desire to modify the game, learning about programming, physics, and more.

In conceptualizing the computer as a creative medium, Kay followed the Logo team’s lead. Papert emphasized concreteness—building and thinking with things out there, in the world—and Kay envisioned a system that would make extensive use of images and the familiar “linguistic concepts” of objects. As one of Ivan Sutherland’s doctoral students, Kay was well versed in computer graphics.⁸⁷

[picture]

Young Alto users.

PARC was large enough to harbor within itself research groups with diverse expertise and objectives, enabling them to work on problems bridging physics, software, and hardware. Kay’s charisma, wide-ranging interests, and synthetic creativity were well suited to this environment, rife with cross-pollination and collaboration. Chuck Thacker and Butler Lampson’s efforts to build a personal computer with high-quality graphics led to the Alto, the machine that Kay’s Learning Research Group (LRG) adopted as their “interim Dynabook” hardware.⁸⁸

By combining multiple technical innovations, the Alto reinterpreted the affordances of computer graphics, establishing conventions still in use today. First was the bitmap display, sized like an 8.5 × 11 piece of paper. Simulating paper with pixels allowed graphics and text—any visual representation, really—to be seamlessly combined.⁸⁹ Second was the computer mouse, a recently developed pointing device that allowed users to draw, point, and grab. Piaget had noted that children progressed through developmental stages he called kinesthetic, visual, and symbolic—modalities the Alto fluently combined.⁹⁰ One of the first images it displayed was an animated Cookie Monster, from Sesame Street.⁹¹

Like a sketchbook or musical instrument, the Dynabook should afford a seamless flow of activity.⁹² It should feel highly responsive, allowing a “flitting-about” of attention and activity.⁹³ This concords with our vision system—the saccades (quick movements) of our eyes enable us to take in large scenes, like a landscape or poster—not to mention the mind’s rambling focus. Concerned about the Alto’s limited display size, Kay proposed multiple overlapping windows, like the “overlapping documents on a desktop,” which could easily be brought to the top. This idea elaborated on the multiple views offered by earlier systems, like Sketchpad, and was underwritten by bitmap graphics.⁹⁴ Windows amplified the sense of unencumbered flow; eyes and hands could scamper across multiple simulated pages. This was unlike conventional computer interfaces, where it was easy to feel stuck or lost in different modes.⁹⁵ Looking, pointing, and grabbing were primeval interactions congruent with the preverbal communication of young children.

A dynamic medium needs a programming language. Kay considered naming it “Simulation LOGO,” or “SLOGO,” but eventually settled on “Smalltalk,” an intentionally innocuous name—it was for children, after all. Synthesizing a variety of influences, like Simula, an event-based proto-object-oriented language designed for simulation, Kay envisioned

a more biological scheme of protected universal cells interacting only through messages that could mimic any desired behavior. . . . each Smalltalk object is a recursion of the entire possibilities of the computer. Thus its semantics are a bit like having thousands and thousands of computers all hooked together by a very fast network.⁹⁶

Smalltalk was more than a programming language; it was also an operating system and user interface. Like BASIC, whose design was also enmeshed with an operating system, these interdependencies underwrote the seamless experience that enabled Smalltalk to feel like a pencil, book, or flute rather than a clumsy machine. Smalltalk’s motley crew of objects fielded the messages that radiated from a user’s mercurial activities, giving the system its signature feeling of “aliveness.”⁹⁷

Given their apprehension of computing as an expressive medium, it is unsurprising that a parade of creative tools followed. There were mouse-based word processors, including the first WYSIWYG galley editor, written in Smalltalk. Different object types could present their own user interfaces, enabling multimedia documents. There was a music system, TWANG, whose notation was designed for children, with real-time voice synthesis and a graphical timbre editor, and OPUS, which transcribed performances into musical scores. There was the animation tool, Shazam, and a simulation system, Simpula, that children used to model amusement parks, schools, and stores. There was an object-oriented version of Logo, as well as an early attempt at programming with icons.⁹⁸

By the summer of 1973, Smalltalk was ready for field-testing with children. Lacking the necessary expertise, Kay’s group recruited Adele Goldberg, a graduate student specializing in educational technology, from Stanford, where she had been teaching Logo alongside Papert’s ideas.⁹⁹

In 1977, Kay and Goldberg published “Personal Dynamic Media,” which described progress toward the Dynabook vision. The enumeration of children’s expressive, media-rich, self-directed projects echoed Papert and Solomon’s “Twenty Things to Do with a Computer.” Both articles deliver a manifesto on learning packaged in a giddy and exuberant account of the delightful wonders created by the children (and researchers). They were

exploring the use of this system as a programming and problem solving tool; as an interactive memory for the storage and manipulation of data; as a text editor; and as a medium for expression through drawing, painting, animating pictures, and composing and generating music.¹⁰⁰

It was for “reference materials, poems, letters, recipes, records, drawings, animations, musical scores, waveforms, dynamic simulations, and anything else you would like to remember and change.” Kids “are used to finger-paints, water colors, color television, real musical instruments, and records” and required computational forms that were equally responsive, sensorially rich, and flexible. Using Smalltalk, the children made drawings and paintings, created simulations, edited fonts, wrote prose, made music, and made and customized their own tools by writing in Smalltalk. Most important,

the kids love it! The interactive nature of the dialogue, the fact that they are in control, the feeling that they are doing real things rather than playing with toys or working out “assigned” problems, the pictorial and auditory nature of their results, all contribute to a tremendous sense of accomplishment to their experience. Their attention spans are measured in hours rather than minutes.¹⁰¹

The computer’s “ability to simulate the details of any descriptive model means that the computer, viewed as a medium itself, can be all other media”—“a metamedium.”¹⁰² The high-resolution bitmap display and object system allowed diverse media—text, image, animation, sound waves—to coexist in a unified visual and computational field. The “central notion” was simulation: “simulation of visual or auditory media,”¹⁰³ like music and sound, as well as agent-based systems like an amusement park, school, or hospital.¹⁰⁴

This groundbreaking work, as Kay later reflected, was actually the synthesis of earlier research from the 1960s. The “big shift that consolidated these ideas into a powerful theory and long-lived examples came because the LRG focus was on children,” a move indebted to the Logo group’s emphasis on children, education, and field testing.¹⁰⁵ The approach had unintended and far-reaching side effects. Like sidewalk curb ramps—which benefit everyone, not just people with disabilities—designing a computer for children vastly expanded the potential reach of computing.¹⁰⁶

Despite the widespread adoption in the 1990s of both object-oriented programming (OOP) and the graphical user interface (GUI), Kay considers the interim Dynabook a failure. Since “the content of personal computing was interactive tools, the content of this new kind of authoring literacy should be the creation of interactive tools by the children.” But Kay’s group struggled to teach object-oriented programming. Yes, some kids did pick it up, but only a minority got anywhere meaningful. Moreover, the children came from Palo Alto schools, a sample that skewed toward privilege. For Kay, the problem was too many nonobvious ideas. He identified a painful parallel with literacy, which is familiarity with “a literature that renders ideas.”¹⁰⁷

By pointing to the importance of a literary canon, Kay hints at the horizons that bounded their vision. A canon is a collective cultural enterprise. But Kay and Papert were animated by a cognitive rather than sociocultural outlook. Taken to its limit, cognitivism is prey to the fallacy of the solitary autodidact.¹⁰⁸ Smalltalk, however, inadvertently demonstrated the power of sociocultural learning. Bruce Horn, one of the precocious teens recruited to test Smalltalk, would go on to join the LRG staff, where he contributed to Smalltalk. He later joined Apple and helped design the original Macintosh.¹⁰⁹ By describing kids like Horn as a “‘hacker phenomenon,’”¹¹⁰ Kay inadvertently points to another impediment: their designs targeted an idealized “technically precocious boy”¹¹¹ with deep roots in MIT’s hacker culture. BASIC, by contrast, epitomized a more democratic vision of computing—situated outside America’s elite computing institutions—and the power of community to lift up and cultivate new practitioners.¹¹²

Kay and his colleagues would go on to further their vision of computing for children in research labs at Atari, Apple, Disney, and elsewhere. This work ultimately pointed the way toward Scratch, the monumentally successful children’s programming environment. Scratch’s preeminence, however, is inseparable from a deep engagement with diverse communities of learners in terms that interest them—as BASIC once did.¹¹³

In the end, it wasn’t Xerox, but Apple, and then Microsoft, that would bring the graphical user interface to the masses. Jef Raskin, who started the Macintosh project at Apple, encouraged Steve Jobs to visit PARC in 1979 to gather inspiration. Finally, writes Kay, “people who could really do something about the ideas finally got to see them.” Apple gradually hired away many PARC staffers and adapted and refined their ideas. They kept the curb ramps (the GUI) but discarded the technically precocious hacker bits (OOP). While underpowered by PARC standards, the Macintosh worked on low-cost hardware and disseminated the GUI approach to users and third-party developers—bringing more people than ever before into the world of computing as an expressive medium.¹¹⁴

Pinball

Growing up in the 1960s, Bill Budge enjoyed construction toys like Fischer Technik, Lego, and Tinkertoys, which he credits for inspiring Pinball Construction Set. He learned to program in high school, where he used a dial-up BASIC system to write Tic-Tac-Toe, Checkers, and Go. He studied computer science at Berkeley, where his classmate (a future member of Apple’s Macintosh team), turned him on to the recently released Apple II. Budge got one, and loved playing Wozniak’s BASIC version of Breakout. (He manually typed in the code, which was included in the Apple II Technical Manual.)¹¹⁵

Wozniak was right. Games were key to a successful personal computer, and BASIC, an easy-to-learn programming language popular with hobbyists, was the key to games. Budge became so good at Breakout that he modified it, making up new rules and increasing its difficulty. He then reprogrammed it in assembly, giving it high-resolution graphics. This led him down the path of commercializing derivatives of popular games like Pong and Asteroids, first trading a collection of games to Apple for a $700 printer and then partnering with the California Pacific Computer company, which packaged and distributed his games in plastic bags to stores.¹¹⁶

Budge’s early success with creating Apple II games, mostly clones, brought him acclaim among Apple II enthusiasts and eventually landed him a job as a graphics engineer on the ill-fated Apple III. Finding this work boring, Budge transferred to another star-crossed project: the Lisa, Apple’s first attempt at building a computer in the image of Xerox PARC. Budge had heard about Xerox’s GUI, but it wasn’t until he encountered its derivatives at Apple that he absorbed the import of the interface paradigm that would revolutionize computing.¹¹⁷

Budge also absorbed his colleagues’ mania for pinball, which inspired him to create a sophisticated pinball game for the Apple II. Like Budge’s earlier games, it too was a clone. He used tracing paper to copy the layout of the Firepower pinball machine, creating a game whose implementation required solving tricky graphics and physics problems. “I wanted to see if I could build it as a technical exercise,” says Budge. “Making it fun to play was kind of secondary.”¹¹⁸ Directly apprenticed to Apple engineers, Budge now possessed the programming expertise to pull it off. He quit his job at Apple and, with his sister, formed BudgeCo to market his new game as Raster Blaster.¹¹⁹

Budge was mainly motivated by the technical challenges of making games, which explains his enthusiasm for cloning, rather than designing, games. Realizing “that creating tools for others to make games was a way for me to indulge my interest in programming without having to make games,”¹²⁰ Budge decided to try something different. Armed with Raster Blaster’s pinball simulation and the GUI conventions he encountered at Apple, Budge “saw that it would be a small step to do a construction set. This was the kind of program I liked, since there was no game to write. But it was a lot of work, since I had to implement file saving, a mini sound editor and a mini paint program.”¹²¹

[picture]

Firepower (1980, left) and Raster Blaster (1981, right).

Left: Smalltalk paint program. Right: Bill Atkinson’s MacPaint (1984), released with the Apple Macintosh.

This effort elaborated on the development tools he had created for Raster Blaster, in particular its background editor. The magnifier tool, which resembled the GUI he had seen at Apple, allowed him to zoom into the background and easily edit individual pixels. This was the seed from which Pinball Construction Set sprouted; a developer tool blossomed into a player’s construction set and toy.¹²²

[picture]

BudgeCo advertisements for Pinball Construction Set.

Pinball Construction Set, which BudgeCo published in 1982, transformed pinball table design, their advertisement claimed, from hard work into fun. The toy-like packaging intentionally evokes the construction sets of Budge’s youth.¹²³ Construction sets like Lego had long cast design and engineering as child’s play. Now, with the help of the GUI, Budge brought this tradition to the medium of videogames. BudgeCo called it “The First Software Toy.” By putting accessible game development tools in the hands of players, Pinball Construction Set inspired a wave of digital construction sets, like SimCity, that inspired, guided, and amplified a player’s imaginative impulses in a particular domain, and then brought the resulting creations to life.¹²⁴

Players place parts via a drag-and-drop interface, paint the background, shape the table’s contours with a polygon tool, and wire game triggers to sound effects and point awards. Designs can be exported to a floppy disk, allowing others to play them—even if they don’t own Pinball. The resulting pinball machines, which can be quite complex, are fluidly simulated, even with multiple balls in play.

There is an inherent tension between granting players creative agency and the limits of what a programmer and designer can anticipate. A toy should gracefully respond to unanticipated play that pushes boundaries and wreaks havoc. (Toys and tools invite this sort of behavior). Unwilling to compromise on the player’s sense of creative agency, Budge’s solution was to gracefully accommodate edge cases like malformed polygons while also cautioning against them in the manual. The manual also encouraged boundary-pushing behavior, explaining, for example, how objects can be hidden by painting out their pixels, thus transforming a potential bug into a feature. Wright would later describe this design posture as accommodating failure states and making them enjoyable.¹²⁵

[picture]

Pinball Construction Set (1982).

Lego, like many other construction toys, includes step-by-step instructions and arrives in packaging that illustrates potential creations. To scaffold new designers and satisfy impatient players, Pinball offers sample tables and always greets players with a suggestive and partially complete table. Like a Lego set’s packaging and manual, these prime player imagination and guide play. Pinball’s examples, which range from bare bones to complex, are themselves editable, thus functioning as starting points for tinkering—like Wozniak’s BASIC Breakout. Such a design move is also right out of the Xerox PARC GUI design playbook, which notes that “creating something out of nothing is a difficult task . . . it is easier to modify an existing document or program than to write it originally.”¹²⁶

[picture]

Left: Bill Budge with Tinkertoys and Macintosh. Right: Original BudgeCo packaging for Pinball Construction Set.

Pinball coincided with a nascent software industry that transformed software from folk culture to commodity. Wozniak’s emphasis on programmability, as well as the Apple II disk drive, accelerated the production and exchange of mass-market software. It was a bumpy transition, as Bill Gates’s 1976 open letter to computer hobbyists asking them to “pay up” to ensure the existence of “quality software” suggested. (At issue was the BASIC that Gates and Allen, his partner at “Micro-Soft,” had created for the Altair.) Trip Hawkins, a marketing director at Apple, recognized the commercial potential when he left and founded Electronic Arts (EA) in 1982. For Hawkins, people like Bill Atkinson, one of the chief architects of the Macintosh, were artists. Thus, what had once between considered menial technical labor mostly performed by women became the province of talented, mostly male, virtuosos. EA games weren’t sold in plastic bags, but came in handsome album-like enclosures that featured their creators. (This shift marked a broader movement underway in the game industry, from amateurs to professionals, and foreshadowed the coming hypermasculinization of videogames.) As in the music industry, producers become important. EA hired Norman Seef—famous for his portraits of rock stars, film directors, and other cultural superstars—for their “Can a Computer Make You Cry?” advertising campaign that prominently featured developers like Budge. Hawkins eventually prevailed on Budge to allow EA to publish Pinball, which had seen only modest sales with BudgeCo. With EA’s marketing muscle behind it and ports for the Atari 800 and Commodore 64, Pinball became a hit, selling over 300,000 copies.¹²⁷

[picture]

Pinball Construction Set, Electronic Arts gatefold album, front, back, and interior triptych (1983). Note the hand motif and its recurrence throughout this chapter.

While Budge and his sister had used primary colors and simple shapes to market Pinball, EA’s album-like packaging was more aggressive: “Power. Pure, sheer and unadulterated. A nearly telepathic link between you and the machine.” In the software, a gloved hand served as the player’s cursor. EA’s packaging elevated and multiplied this hand, highlighting the exquisite manipulability it symbolized and mediated. These hands—depicted as belonging to both the player and Budge the auteur—reach into a lattice world, underscoring direct manipulation,¹²⁸ human-computer intimacy, and power over a digital world—from game logic to the laws of physics. Play is described here as an unfettered power fantasy, not a toy-like construction set. The emphasis on manipulability and intimacy points to the design history of the GUI, echoing Licklider’s “Man-Computer Symbiosis,” and the long journey from Whirlwind to Apple. As a BudgeCo advertisement noted, Pinball was the first time a broad audience interacted with a GUI.

§§§

An enduring legacy of Pinball Construction Set is that by pushing genre boundaries and achieving commercial and critical success, it gave license to developers such as Will Wright to confidently create software toys, experiences that weren’t games and couldn’t be won or lost, as well as construction sets, toys that scaffolded creative play. Pinball Construction Set harbored the transformative idea that a title inviting open-ended construction play could commercially thrive amid the landscape of computer games. Players didn’t need explicit goals. They just needed to be gently scaffolded with the right templates and tools.

The GUI was perfectly suited to Pinball Construction Set, as the GUI itself was designed for children, the manipulation of concrete visual matter, media authorship, self-directed activity, and learning by doing. While the modern GUI coalesced at PARC, its animating ideas have a long and complex history. New ideologies and methods—such as a turn toward children, often performed in close collaboration with women researchers—allowed creativity, learning, and universal accessibility to come to the fore. This history, moreover, reflects a deep entanglement with play that complicates the distinction between tools, toys, and simulations.¹²⁹

As BASIC, Logo, and Smalltalk illustrate, computing’s transformation from military system to popular media was animated by new visions of computing. This vision, however, did not arrive in mainstream culture as many of its champions had hoped. While certain long-standing themes, such as learning by doing, multimodal representation, and play, underwrite the appeal and accessibility of today’s apps, games, and tools, other themes are less evident. Kay’s group envisioned computing as a sophisticated medium that everyone—even children—would use to create their own tools. Scratch notwithstanding, this activity was mainly taken up by technically precocious young men like Bill Budge (b. 1954) and Will Wright (b. 1960).

Wozniak’s populist Apple II, with its built-in BASIC interpreter, propagated an influential vision of computing as an expressive medium. For a young college dropout named Will Wright, it was a tool for controlling his robots. Enchanted with its ability to play games and run simulations like Conway’s Game of Life, Wright went on to make games and toys of his own. Titles like SimCity, which took inspiration from Pinball Construction Set. What Budge did with pinball, Wright would do for cities.

III SimCity’s Architects

6

Designing SimCity

Gazing down from the air, shimmering waterways, crammed cities, pastoral towns, and sprawling roads offer themselves up in overview. The logic of the city is revealed—or so it seems. The airplane, wonder of the mechanical age, offers a bird’s-eye view. Writing between world wars, the Swiss-French architect Le Corbusier, dean of modern architecture and city planning, fell for the airplane, marvel of the era, and the new landscape vision it offered.

Airplanes had delivered death to sleeping towns during the war, but now the airplane delivered a new vision that “indicts the city.” The “airplane eye reveals a spectacle of collapse.” Its “eagle eye” offered “proof, recorded on the photographic plate, of the rightness of our desire to alter methods of architecture and town-planning.” The airplane instilled a “modern conscience”: “Cities, with their misery, must be torn down. They must be largely destroyed and fresh cities built.” Looking down from this new vantage offered a sense of objectivity, clarity, and—importantly—emotional distance. The aerial vista inspired the wholesale redesign, destruction, and rational rebuilding of cities.¹ It was from such a remove that Robert Moses masterminded the redevelopment of vast swaths of New York City and the wholesale destruction of vibrant neighborhoods. The street-level view, in its complex humanity, didn’t enter Moses’s vision.² Jay Forrester’s urban dynamics model, similarly, was a top-down modernist vision aligned with the logic of urban renewal. Neighborhoods could be torn down and replaced with sleeker visions. This was the view from above, from cruising altitude. From up here, the city became a toy, a military target, a system to reconfigure.³

As Will Wright flew over the landscape of Raid on Bungeling Bay, a shoot-‘em-up videogame, he also found himself in a reverie of urban design. In Raid, players piloted a helicopter, raining destruction, but Wright, who had designed the game and its intricate landscape, found himself enchanted by the airplane eye; he couldn’t stop building Raid’s landscape, tinkering with its islands, buildings, and roads. Wanting to bring this world to life, he researched cities and elaborated his program. Gradually, layer upon layer, the program began to become something else.

What Wright eventually created was neither videogame nor serious simulation. It was a strange object that somehow synthesized a wide range of influences. These included videogames, Godzilla, and science fiction, as well as cellular automata, system dynamics, and urban geography. Stranger still was that people liked it. But strangest of all was that SimCity was enthusiastically received by people who played videogames and those who did not; by scientists at the Santa Fe Institute and architecture professors who taught urban planning. Blue-chip firms like Chevron, Wall Street venture capitalists, Newsweek, the New York Times, and Computer Gaming World all lauded SimCity. To understand how and why this happened, and prepare the way for a close analysis of SimCity’s design, we turn now to how SimCity was made and the people who made it.

The Simulation Native

Will Wright (b. 1960) loved to make things. In 1981, he and a friend souped up his Mazda RX-7, expanding the fuel tank and adding a roll cage, for a cross-country rally race. But he was especially interested in robots and space travel. Wright had moved to Manhattan in 1980 to study the commercialization of space at the New School, where he repurposed machine parts, such as landing-gear hydraulics (from scrapped military aircraft), into a 100-pound robot arm. A robot arm, however, is nothing without some kind of control system. Computers could control robots, so Wright bought an Apple II (from a racing partner) and then set out to hook it up to his creation. The Apple II, however, was too bulky to sit next to the robot, so Wright built a radio modem and cobbled everything together with walkie-talkies. Now they could sit at a comfortable distance and communicate. But how would the computer direct the robot? Some kind of intelligence was needed. Wright would need to study programming as well as artificial intelligence.⁴

That was fine. Wright was a self-starting learner. Since the age of sixteen, he had bounced among universities—Louisiana State, Louisiana Tech, the New School—taking up subjects that interested him: architecture, aviation, economics, mechanical engineering, military history, and the commercialization of space.⁵ Programming didn’t interest him; punched cards and printouts were tedious.⁶ They lacked the immediacy of cars and robots. More important, programming hadn’t been connected to his interests. As a self-directed learner, formal education didn’t suit him. He wandered the halls of higher learning, following his wide-ranging intellectual appetites, but he never obtained a degree.⁷

Where did he fit in? He had thrived in a Montessori school, where children learn complex ideas through play and problem solving. One of Maria Montessori’s precepts was decentering the teacher. Children followed their own interests and learned through direct interaction with a specially designed environment. Ideally, they would proceed as if the teacher didn’t exist at all.⁸

Wright’s parents were supportive. He had grown up making models—ships, cars, planes, World War II dioramas—out of plastic kits, balsa wood, Meccano, and stock aluminum. Once he and a friend almost burned down his room with homemade chlorine-based gunpowder (made with pool chemicals). At ten, he made a balsa-wood miniature of the Star Trek Enterprise flight deck, winning a prize at a convention. In the age of Sputnik, it wasn’t surprising that Wright’s fantasies were filled with rockets and space colonies, and he dreamed of becoming an astronaut. It didn’t hurt that his dad, an engineer, had founded a plastics manufacturing company and that Wright’s dad, along with his father, had both graduated from Georgia Tech with engineering degrees. Tragically, when he was nine years old, his father died of leukemia, and so his mother, who dabbled in magic and acting, moved the family from Atlanta back to her hometown of Baton Rouge.⁹

Wright’s hobbies and fascinations kept him busy. The cybernetic vision of the 1950s still pulsed through popular culture, pumping a steady beat of cyborgs, robots, and intelligent machines into Wright’s imagination. He began building robots as a teenager—strange mechanical contraptions made of scavenged parts, like the syringes he repurposed into a hydraulic robot hand.¹⁰

As a teenager, he dove into the intricate wargames filtering into American culture: titles like PanzerBlitz (1970), Global War (1975), and Sniper! (1973).¹¹ RAND researchers had introduced hexagonal game boards to military wargaming, and many popular wargames followed suit. The hexagons’ journey from military to mainstream was emblematic of a broader migration. Systems thinking, simulation, and game theory—all cultivated for military purposes—were percolating into the mainstream. By the 1970s, when Wright was a teenager, a sizable American market for wargaming had come into its own, along with vibrant, if sparse, player communities. (Dungeons & Dragons, first published in 1974, is the best-known product of this ferment.) Hobbyist wargames, unlike those used by the military, emphasized playability over verisimilitude.¹² Nonetheless, arcane rules had to be mastered and their minutiae legalistically debated as victory conditions were frequently ambiguous and open-ended. This was all part of the fun, as was the pandemonium that ensued from an unlucky roll of the die.¹³ Such failures weren’t the red ink of a bad grade, but a badge of engagement and learning.¹⁴

As a simulation native, Will Wright knew the joy and craft of model making, and knew simulation as a medium of representation. Examples abounded at the game store, model shop, and science-fiction convention. Simulation was stagecraft and machine, history and magic. Any material—even a syringe—could be creatively repurposed and become a model of something else.

§§§

Wright was perfect for the Apple II, and the Apple II was perfect for his robots. Just as Steve Wozniak had hoped, the built-in BASIC interpreter invited multitudes into crafting software.¹⁵ Wright had no problem teaching himself BASIC and learning about artificial intelligence.¹⁶ Punch card programming wasn’t for him, but the immediacy of BASIC on a personal computer was magic.¹⁷ Besides, he was programming for his muse, not some professor whose course assignment was divorced from his own fascinations.

Wright quickly discovered the Apple II could do much more than animate robots. By the early 1980s, Wozniak’s machine played host to a buzzing computer game scene, and Wright found himself in a mind-bending world of software toys, games, and digital microworlds. No need to recruit a partner for wargaming and spend hours teaching them the rules; he could go to the computer store and buy a piece of software that transformed his Apple II into a ready playmate.¹⁸

Fascinating microworlds awaited exploration. There was Flight Simulator (1983), a low-resolution wireframe affair whose rich rule set and undirected play conjured a world open to his imaginative hijinks. Wright probed the world’s boundaries, flying straight up or deliberately crashing. Pinball Construction Set (1982) “blew me away,” says Wright, a committed model builder. He could design his own pinball tables and play them.¹⁹ This was learning through experimentation, failure, and play.

A voracious reader, Wright stalked bookstores and libraries. His insatiable curiosity brought him to John Conway’s Game of Life, which he programmed in BASIC. Life’s entrancing patterns inspired him to become a better programmer and ponder how elegant rules gave rise to complexity. Wanting his simulation to run faster, Wright taught himself Pascal and then machine language, improving his simulation’s performance ten-fold each time he rewrote it.²⁰ As he mastered the Apple II, he pictured its hardware and operating system as a bustling yet familiar city.²¹

Through books, magazines, and the computer store, Wright entered a teeming universe of microworlds and computing communities. Each offered a body of work to learn from. In artificial intelligence and robotics, for example, Wright discovered Terry Winograd’s seminal work: a simulated robot arm that manipulated blocks in response to natural language. Wright did not have to conceive, from the ground up, of how computers might represent the world through simulation. The cellular automata formalism, for instance, came via Life. As Wright became fluent in the canon of computer simulation, he began to simulate his own robots, deepening his programming proficiency. Computers were many things to many people, but for Wright, the computer was a tool for modeling, experimentation, and play.²² He also learned about something else: computer game development as a profession.

Back in Baton Rouge, he had fallen in love with Joell Jones, an artist and activist eleven years his senior, which brought him to Oakland, California, where she lived. He was still living on the educational trust fund his father had left him, even after dropping out of college. Maybe he could create and sell a game. There was one problem: the world of computer games was rapidly maturing. A solo developer could still produce a hit, but seasoned developers like Nasir Gebelli and Bill Budge were recognized masters with massive head starts. How would a young upstart ever catch up?²³

Raid on Bungeling Bay

One answer lay in the ceaseless leapfrogging of computer technologies. In 1982, Commodore released a new home computer, the Commodore 64 (C64). Its custom hardware enabled more sophisticated graphics and sound than the Apple II (1977), which, by the fast-changing standards of the computer industry, was declining into senescence. The C64 was destined to become the leading home personal computer, and Wright immediately bought one. It was a newly leveled playing field.²⁴

Wright was alive to the creative implications of this new platform. The VIC-II graphics chip allowed programmers to define custom character sets and produce smoothly scrolling worlds. Under the hood, individual letterforms were actually bitmap images, and programmers could replace them with custom graphics. “X” might become part of a tree, and “2” part of a road. With this technique, complex graphical scenes that scrolled and animated could be assembled from custom characters. Taking up this affordance, Wright built a game in which the player piloted a helicopter across a vast landscape.²⁵

His game would be called “Carrier Patrol,”²⁶ and it was a combat mission. A sprawling archipelago filled with forests, roads, and bridges awaited the player’s helicopter, which hurled missiles and bombs at boats, tanks, and planes guarding six factories. The concept synthesized two very different game genres: the fast-paced arcade shooter and the strategic wargame. A complex supply line underpinned the player’s opponent, whose boats harvested resources and transported them to land, where tanks brought them to factories that manufactured and repaired its arsenal. The clockwork world reflected a range of simulation influences, from videogames and wargames to cellular automata, and the intricately crafted mechanical models, like orreries (clockwork solar system models), that had long fascinated Wright, who wanted unpredictable complexity to emerge from precisely crafted rules.²⁷

[picture]

Raid on Bungeling Bay (1984), Wright’s first commercial game.

A coherent fictional world was paramount. Rather than ratcheting up difficulty across artificial game levels, the enemy escalated its response as its factories were destroyed. The game’s climax came with the construction of a battleship that would seek out and destroy the player’s home base, an aircraft carrier. (Throughout the game, the player must balance attacking the enemy with defending her own carrier.) Wright later described this continuously escalating difficulty as a spring that pushes back harder the more you press into it. Intricate simulation logic, not game levels, yielded both naturalism and a dramatic arc.²⁸

As is customary in game development, he made special tools. The scrolling world was a mosaic whose islands, waterways, forests, roads, and buildings were stitched together from custom C64 characters. Wright wrote two programs to help him design the world: a character editor called Chedit, for designing the custom character graphics, and a world editor called Wedit, for composing those characters into the game map. He also made a third tool: a custom hardware interface that linked his older Apple II to the new machine. This allowed him to leverage the Apple II’s mature development tools, speeding up development.²⁹

§§§

Wright shopped around his game, driving to a few Bay Area game publishers. Eventually he partnered with Brøderbund, whose founders and staff provided invaluable mentorship and support.³⁰ Despite its pacifist leanings, Brøderbund did publish violent titles, but within a narrative framework where the Bungeling robot empire, not humans, was the antagonist.³¹ Published in 1984, the same year Wright and Jones married, Raid on Bungeling Bay’s popularity was masked by lackluster sales (around 30,000 units in the United States) due to software piracy.³² But a Famicom port sold around 800,000 units.³³ Nintendo’s cartridge-based systems made piracy much more difficult, and Brøderbund had a lucrative royalty arrangement with Nintendo, which also distributed Raid, in Japan, as an arcade machine.³⁴ Wright’s game development gambit had paid off.

Reviewers applauded Wright’s modeling prowess. Raid was “amazingly detailed,” wrote one review, its “beautifully rendered” landscape a delight to fly over. “Smoke pours from the stacks atop the factories, helicraft blades whirl, heat-seeking missiles are propelled on shafts of flame, and radar installations explode like fireworks.”³⁵ The “detail of the landscape below is extraordinary, complete with small buildings, roads, radar stations, ports, and drydocks.”³⁶ Even the Children’s Television Workshop computer magazine liked Raid. But what if, they wondered, the detailed animate landscape were put to a less violent use?³⁷ Indeed, what if the intricate world wasn’t for war but something else?

Raid heralded Wright’s emerging design sensibility. It exhibited his flair for intricate simulation and naturalistic detail. Verisimilitude would trump the conventions of game design, like levels, but without sacrificing playability. Whenever possible, he would opt for naturalistic representation. Although Raid delivered the fast-paced thrills of a videogame, it also offered the strategic depth of a wargame. This admixture of strategy and action—and their different time scales—would become a hallmark of Wright’s work. Neither genre nor game convention would constrain his bricolage, which also incorporated, in this case, the technical affordances of a new computer platform. Genre-bending bricolage, technical artistry, naturalistic simulation, and strategic depth—Wright’s style was coming into focus.

Looking back, Wright observed that a lot of simulation detail was wasted on players, as it was barely legible. Enemy agents ferried resources across the map, but players couldn’t see it. And how could they? Raid didn’t represent them to the player.³⁸ Wright had made a clockwork world, but hadn’t surfaced some of its most important dynamics. In the future, he would partner with developers who assumed more responsibility for the user interface.

With his career as a game developer now off the ground, Wright proceeded toward his next project. It would, of course, be about robots, and software would be the medium of representation. The working title was “Probot,” for “probe” and “robot.” Using a joystick, the player drove a robot arm mounted on a four-wheeled platform through an abstract Escher-like landscape riddled with platforms, passageways, and ramps. Rivulets of water coursed through, emerging from three different sources, each of which occasionally spawned a different shape: a cube, a sphere, a pyramid. The robot arm, controlled by holding down the joystick button, allowed the player to collect and organize the shapes while trading with three-legged alien robots doing the same. This new game combined Wright’s interest in cars, robot arms, and artificial intelligence.³⁹ He dove in but somehow found himself returning to Raid, flying over its miniature world.

City Planner

Raid’s airplane eye invited a reverie of urban design. While players were busy bombing Raid’s landscape into submission, Wright found that he couldn’t stop building that landscape. Of course he loved to build; he knew that. But the game had shipped. What was he doing? It was almost as if Wedit, the game’s world editing tool, was more fun than Raid.⁴⁰ Like Budge, Wright had become entranced with his own development tools. Wright began to elaborate Wedit, tinkering with it and adding features.⁴¹ For someone who loved to build models, development tools were more interesting than any game.

To help him produce Raid’s landscape, Wright had written code that automatically laid roads. Now, however, he wanted to direct the placement of roads himself and have the computer worry about the details. Building a road was like matching dominoes. He had to select the right characters for each tile, so the graphics for the whole road linked up and appeared continuous. Wright added logic that automated this process, so as he added to a road Wedit automatically picked out the right characters. This is a satisfying experience, as players of SimCity can attest.⁴²

He wanted traffic on the roads. But where were the simulated people going? They needed buildings. Wright wanted buildings to appear semiautomatically, so he added a menu and zoning tool. But automatic building selection required a model of urban growth and decay. This nascent world was calling out for simulation rules that modeled urban growth, not the Bungeling’s war machine. Reflecting on his motivation, Wright realized he “was fascinated by bringing a city to life.”⁴³

The microworld fed his autodidactic impulses, motivating him to learn “about urban planning theories, urban dynamics and simulation.” He plowed his learnings back into his simulator, which “became a kind of test-bed.” “What was a very dry subject in books became very fascinating when I had this guinea-pig city that I could do these experiments on.”⁴⁴

Luckily, his friend Bruce Joffe was a city planner and lived across the street. Now, thanks to Wright’s interest in cities, they shared a new enthusiasm. In addition to playing Go and talking about programming, the Apple II, and Gödel, Escher, Bach, they chatted about maps and cities. Joffe encouraged Wright and recommended books. Joffe, in fact, had studied programming and taken Jay Forrester’s class on system dynamics at MIT while pursuing master’s degrees in architecture and city planning. (His interest in urban planning had been kindled while working for the Johnson administration’s Office of Economic Opportunity, an agency created in response to the urban crises of the 1960s.⁴⁵) Joffe catalyzed the development of Wright’s nascent city editing and simulation program.⁴⁶

A land value model informed the automatic building placement and eventually became the heart of the city’s spatial simulation. Simulated trips between buildings generated animated traffic. Walter Christaller’s central place theory provided Wright with a framework for modeling the relationship between economics and geography. In the early 1930s, Christaller asked after laws to explain the “sizes, number, and distribution of towns” and postulated that the “crystallization of mass around a nucleus is, in inorganic as well as organic nature, an elementary form of order of things.”⁴⁷ Drawing on ideas from this literature, Wright embellished his model. He added internal and external markets. The city’s center of gravity exerted a gentle tug on development and land value. Commercial development should gravitate toward the center, since it served the internal market; industrial development would prosper at the periphery, since it served the external market; and residential would thrive somewhere in between.⁴⁸

A search for modeling techniques led him to the work of Jay Forrester and his Urban Dynamics. Forrester’s thoughtfully documented techniques were easily adopted by Wright. System dynamics was, according to Wright, “a way to look at a system and divide it into, basically, stocks and flows. Stocks are quantities, like population, and flows are rates, like the death rate, the birth rate, immigration. You can model almost anything just using those two features.”⁴⁹ But system dynamics had no map; Wright compared it to a spreadsheet. It was just a dry set of numbers and formulas.⁵⁰ To remedy this shortcoming, he modeled geography with ideas drawn from central place theory⁵¹ and the cellular automata formalism, which dovetailed perfectly with the Commodore 64’s character map and could model “propagation, network flow, proximity, and so forth.”⁵² For Wright, like other practitioners of cellular automata, the appeal of systems such as “Go and Conway’s Life or cellular automata in general” lay in how they captured “some underlying aspect . . . of reality and complex systems.”⁵³ The combination of system dynamics and cellular automata gave rise to exciting new behaviors. In addition to this fruitful hybridization, Wright layered cellular automata so they could “interact on the third dimension.” For instance, “pollution can impact the land value layer.”⁵⁴ Like a sculptor who creates by aggregating heterogeneous materials, Wright created a complex simulation assemblage by combining techniques and subsystems into a new representation.

All this “led layer upon layer to a whole city.”⁵⁵ At some point Wright dubbed the program “City Planner.”⁵⁶

§§§

City Planner was, for Wright, a private pastime.⁵⁷ He never thought it would appeal to anyone but himself and maybe “a few architects and city planner types.”⁵⁸ But the enjoyment he derived from his simulation toy, which brought the arcana of urban systems to life, made him wonder if others might enjoy it too.⁵⁹

Bill Budge’s Pinball Construction Set showed that people would pay for a software toy and that construction sets were a legitimate form of computer play—provided they were easy to use. He himself loved to make things, and this apparently was an experience that others enjoyed too. Rather than make a serious city planning tool—or whatever it was City Planner might become—he resolved to make a software toy. Like Budge, Wright would spread the joy of design.⁶⁰

Looking to Pinball Construction Set, Wright adopted the framing of a software toy, the idea of a construction set, and the specific user interface design conventions that made all this possible.⁶¹ He decided “to design a fancy GUI on the Commodore. . . . I thought it was so cool at the time.”⁶² In truth, it was an immature effort. There was no mouse support, the graphics were abstract, and the graphical user interface, despite Budge’s example, was hampered by the C64 hardware and Wright’s inexperience with these new interface design conventions. At the time, for Wright, the “GUI was the row of icons.”⁶³ Nonetheless, a seed had been planted. Later, when City Planner was transplanted onto the Macintosh, its interface, along with the feeling of manipulability, would flourish.⁶⁴

Wright also gathered inspiration from an episode in The Cyberiad, a story cycle by Stanisław Lem in which two benevolent constructor-benefactors, Trurl and Klapaucius, roam the cosmos while inventing marvelous machines.⁶⁵ In “The Seventh Sally,” Trurl encounters a deposed king exiled by his subjects to a barren asteroid. The king implores Trurl to restore him to his throne. Knowing that this would inflict suffering on his subjects, Trurl instead builds a simulated kingdom to rule over: a “microminiaturized society” in a portable box, “very like a child’s toy.” “There were plenty of towns, rivers, mountains, forests, and brooks, a sky with clouds, armies full of derring-do, citadels, castles, and ladies’ chambers.” At first the king is insulted by this diminutive world, but he soon realizes that peering through its “thick glass lid made everything inside seem large.” The emotions were the same. Unseen by his new subjects, the king intercedes by “manipulating the control knobs.” Klapaucius, however, argues that Trurl’s simulation is indefensible; the simulated subjects, the Microminians, still suffer the king’s tyranny. How are you sure, he asks Trurl, that “the parades, tortures, and beheadings are merely an illusion?”⁶⁶ Trurl protests that “it’s only a model.” Klapaucius disagrees:

“And are not we as well, if you examine us physically, mechanistically, statistically, and meticulously, nothing but the minuscule capering of electron clouds? Positive and negative charges arranged in space? And is our existence not the result of subatomic collisions and the interplay of particles, though we ourselves perceive those molecular cartwheels as fear, longing, or meditation? And when you daydream, what transpires within your brain but the binary algebra of connecting and disconnecting circuits, the continual meandering of electrons?”⁶⁷

Lem’s story gave Wright a few helpful concepts. First, there was the idea of a miniature society rolled into a box. As in the story, Wright’s player would have an ambiguous role, not quite a mayor or a king but something like a god—the box’s denizens would be unaware of the player’s presence.⁶⁸

The story also helped Wright crystallize another aspect of City Planner’s interactivity: players would fiddle with the controls while it was running. This departed from many simulation traditions that enacted a kind of scientific detachment: don’t mess up the experiment. Cellular automata and system dynamics modelers typically designed their clockwork worlds, wound them up, and then watched them go. Even Pinball Construction Set separated editing from playing. You designed the table and, when you were ready, you pushed play. To make changes, you had to stop playing. Early versions of City Planner functioned similarly. You’d pause, edit the map, and then push play. Eventually Wright merged these modes, collapsing the distinction between playing and editing. (To accomplish this technical feat, and enable the C64 to keep up with his simulation’s mounting complexity, Wright implemented something akin to a multitasking operating system.)⁶⁹

Finally, the story delivered an important lesson about empathy. Although the Microminians were tiny simulated beings, they nonetheless provoked life-sized emotion and empathy. Returning to the asteroid, Lem’s protagonists are relieved—and stunned!—to find that the simulated society has overflowed the box (!), banished the king, and enveloped the asteroid in “countless signs of intelligent life.”⁷⁰ The story helped Wright see that he was “connecting at some level, empathizing with these little simulated data points.”⁷¹ Perhaps that explained some of his attachment to City Planner.

§§§

Early play tests surprised Wright. Players did empathize with City Planner’s inhabitants—but as madcap tyrants. Initially cool to Wright’s toy, players perked up when they discovered the bulldozer tool. Although it was there to fix mistakes, players would invariably “blow up a building with it by accident. And then they would laugh. And then they would go and attack the city with the bulldozer.”⁷² “They’d start running it all up and down the downtown area with this maniacal laugh. They just loved it.”⁷³ While most contemporary games, he reasoned, “were about destruction,” Wright intellectualized the bedlam he was witnessing as a form of learning. The violence “really intrigued me, because it was like someone coming across an ant pile and poking it with a stick to see what happens.”⁷⁴ For Wright, this destruction wasn’t about violence, but “exploring the dynamics of the system. . . . They see how alive and fragile the system is. It builds the illusion in the player’s head that the simulation is real.”⁷⁵ As in Lem’s story, the prospect of suffering attuned players to the reality of this simulated world.

Paradoxically, it was the unreality of the simulated denizens that underwrote both the violence and the emotional connection. City Planner’s inhabitants were mere data points—unimportant subjects to torture and trifle with, but their powerlessness and (imagined?) suffering also provoked empathy and care. Like the thick glass lid of Trurl’s toy-like world, which both othered and magnified its inhabitants, Wright’s simulation provoked play’s paradoxical capacity to both buffer and connect.⁷⁶

[picture]

Wright’s City Planner prototype is virtually identical to SimCity for Commodore 64, shown here.

Having learned that destruction was easy, and building was hard, players became hooked on rebuilding their cities.⁷⁷ Wright also took a lesson from the destruction and “finally decided, ‘Well, I might as well really let them get it out of their systems, I’ll add some disasters to the game.’ And that’s what gave me the idea for the disaster menu.”⁷⁸

Around 1985, after about six months of working on City Planner, Wright returned to Brøderbund and “they agreed to pick it up, and we had a contract for it and everything.” Brøderbund and Wright, however, had very different ideas about the finished product. Brøderbund expected a game with a win/lose condition.⁷⁹ In response, Wright added scenarios (a wargaming convention) and experimented with an election. Periodically, if your numbers sank too low, you would be booted from office. (The residue of this design is evident in SimCity’s purely informational approval rating.)⁸⁰

This didn’t work for Wright. He envisioned something “much more open-ended, more of a toy,”⁸¹ which “felt more like Legos to me than a standard game.”⁸² Players should be able to explore failure states without worrying about being ejected from office.⁸³ Whereas one can fail at a game, Wright was fascinated by failure. Who says that failure is bad? Or that it isn’t enjoyable? And who, after all, gets to define failure and success? In the toy he was making, players decided for themselves. Cities, after all, reflect the values of their designers.⁸⁴ Besides, for Wright, “most effective learning is failure-based . . . 90% of winning is failing.” Failure is “usually interesting,” as it helps you “understand why” things went wrong. “You’re elaborating that model, you’re building the system in your head with every failure.” Wright wanted his players to be fascinated by, delight in, and learn from failure.⁸⁵

After about a year of development, the arrangement with Brøderbund ground to a halt. Although Wright was pleased with the design, Brøderbund didn’t think it was finished—they awaited City Planner’s transformation into a game. According to Wright, they “didn’t see how they could possibly sell it. And I just left it there, and they left it there, and that was that.”⁸⁶ As Brøderbund’s interest waned, so did Wright’s.⁸⁷ City Planner was a kind of failure. Luckily, it was a fascinating failure, the kind you might learn from. Fortunately, since Brøderbund had never paid for any of its development, Wright had no financial obligation to Brøderbund, and the rights, at least in theory, remained with him. But to develop it into its finished form and reach players, the project needed a software publisher willing to take an enormous creative risk, a publisher whose aesthetic sensibility was sensitive to the creative promise of both Wright and his unusual city construction set. A publisher possessed of the business savvy to shepherd City Planner to completion without ruining it. Disheartened, Wright put City Planner aside, where “it just sat on the shelf for several years.”⁸⁸

The Entrepreneur

Jeff Braun (b. 1955), the son of a grocery store clerk and elementary school art teacher, grew up in Los Angeles. From a young age, he was interested in technology: “if it buzzed, beeped, flashed, whirred, had a button—I’m in.” At age six, a family friend who worked at the aerospace company TRW took him to see the room-sized computers that calculated NASA’s moonshot trajectories. He took home printouts and kept them in a drawer for many years. His mother, an artist and elementary schoolteacher, took the family to museums. He loved the California Museum of Science and Industry, whose playful exhibits—“a touch of Aristotle and a dash of Barnum”—were designed by the likes of Charles and Ray Eames, where he played against an infallible electronic Tic-Tac-Toe machine. As a teenager, he fell for Claes Oldenburg’s giant Ice Bag sculpture at the Los Angeles County Museum of Modern Art. Braun loved how it moved and groaned, exuding emotion and life. He saved an exhibit poster, which hung in the Maxis office for many years. “I always kept that poster because I thought that it’s really about art and technology.⁸⁹

As a student of Pitzer College, a private liberal arts institution, he interacted with computers for the first time. Spacewar! and Eliza made a huge impression. “I thought it was too cool. We were going through outer space and we were talking to our psychiatrist.” He discovered Philip K. Dick, whose stories of hidden realities underlaying our own he found profoundly affecting.⁹⁰ Braun took courses in a wide variety of fields, but, like Wright, lacked the patience to learn punch card computer programming: “I don’t have the patience to sit and knit a blanket.” Braun moved to Lake Tahoe to ski, eventually graduating from Lake Tahoe Community College.⁹¹

Around 1981, Braun moved to San Francisco and found work at a company that repaired pinball machines and videogames. As he stood in a giant warehouse of broken coin-op machines, video gaming’s commercial possibilities dawned on him. “I knew all the games and I played all the games. . . . You had to test everything. . . . That sort of built my first little introduction to ‘This is gonna be a huge damn industry.’” He loved it all—from arcade videogames and pinball to computer text adventure games like Zork (1981). According to Braun, because coin-op amusements were a cash business, organized crime was involved: “Eventually I got hired away by the Mafia and then the Hells Angels.”

Braun’s first entrepreneurial effort was in barcoding, helping organizations like San Francisco MUNI, AC Transit, and the US Army modernize their inventory control. Then, in 1985, Commodore released the Amiga 1000 personal computer. Excited by its multimedia capabilities, Braun decided he was “really into fonts” and hired the programmer Edward Kilham, whom he met at an Amiga enthusiast meeting, and together they designed a product called Calligrapher that enabled the Amiga to compete with the costly video-titling hardware that television broadcasters used.

About a year later, Braun sold his font company, Interactive Softworks, to a friend. “I took that money and decided I was going to do videogames, but I had no clue what that meant or what it would be.” Reflecting on his experience with games and multimedia, he had decided “that games were going to be the next big thing,” but lacked a plan as to what shape his new games venture would take.⁹²

Through Kilham, Braun met and befriended A. J. Redmer, a Bay Area native whose social network and know-how played a crucial role in getting Braun’s new enterprise off the ground. Braun asked Redmer, who had extensive game industry contacts, how he should go about getting into the business and meet game programmers. Redmer’s answer was beer, pizza, and parties for playing computer games like M.U.L.E. and Maze War—the kind of events Braun and Redmer enjoyed together. Just as Braun had met Calligrapher’s programmer at an Amiga enthusiast meet-up, Braun sought out game developers in the same way—by joining an established community. He started attending the parties thrown by his friend Chris Doner. Wright’s friend and neighbor, a high school student named Mick Foley, dragged him to some of these parties, where he met Braun.⁹³ Braun recalls asking Wright, shy and reticent, what he was working on:

“Well, you’re not gonna like mine. It’s really awful.”

“Why wouldn’t I like it?”

“No one likes it.”

“Why doesn’t anyone like it?”

“Well, it’s not a game.”

“Really? What do you do?”

“Well, you build a city.”

Intrigued, Braun visited Wright’s home to see his demos. Wright and Jones’s newborn daughter, to whom Wright would later dedicate SimCity, lay in a bassinet. The first demo Braun saw was Probot, the game prototype Wright had made before turning in earnest to City Planner. Braun wasn’t interested. Then Wright showed him City Planner, which he described as a paint program for a city.⁹⁴ The simulation detail was astonishing: “I nearly [fell] on the floor in shock. He shows me San Francisco having an earthquake, the monster running around, boats going toot-toot-toot, traffic . . . there’s fires, and there’s a little helicopter going around.”⁹⁵ Moreover, it was a technical marvel: this microminiaturized society unfolded within the meager circuitry of the C64.96

Unlike Brøderbund, which wasn’t interested in Wright’s genre-defiant city building program, Braun wanted in. He had no trouble with its liminality. “So it’s something else. . . . I didn’t have any issues with not being a game or being a game. I wasn’t trying to draw a semantic [distinction]. . . . Like I care.”⁹⁷

Because of Wright’s relationship with Brøderbund, Braun wanted to ensure the rights for the project were clear. This should have been the case since Wright had invested only his own money into the project. Braun sent Wright to Don Daglow, a Brøderbund executive, along with a release letter for him to sign acknowledging Brøderbund didn’t own City Planner.⁹⁸ To be safe, Braun instructed Wright not to tell Daglow that anyone else was interested in the project. Wright acquired the release, setting the stage for a partnership. Braun’s goal was to run a game publishing house, and the business arrangement they struck was that Braun, operating as a publisher, would license Wright’s project and pay for its conversion to Macintosh, Amiga, and PC.⁹⁹

SimCity

Wright had transformed city planning, system dynamics, and cellular automata into his own private plaything; now he, in partnership with Braun and a small team, would transform it into a plaything for others. This meant support for new platforms and an improved user interface, better art, a manual, and packaging as well as manufacturing, distribution, and sales. Players needed to apprehend what kind of thing City Planner was, understand what was happening when they played it, feel a clear sense of agency within its world, and empathize with its inhabitants the way Wright did. And they needed a better name. Braun didn’t like “City Planner,” so he renamed the project “Micropolis,” which was better, but still awkward.¹⁰⁰

A growing development effort was installed in Braun’s two-bedroom Moraga condominium. The second bedroom became the office, where Braun’s cherished Ice Bag poster hung, and the living room and kitchen became workspaces. Wright preferred his own home, where he could work late into the night without disturbance, rewriting Micropolis’s handcrafted C64 assembly into cross-platform C.¹⁰¹ Setting out from the condo midday, the development team could hike out of a valley and through an undulating hillscape and return in time for a bowl of soup from the Chinese restaurant just before it closed.¹⁰²

Whereas Raid was a solo effort, Micropolis would take a team. Creative authority emanated from Wright. Braun built a team around him, describing its organization as a “wagon wheel” with Wright as the hub. If Wright needed help with something or didn’t want to do something, then Braun would plug someone in—a spoke—and see if it worked. He describes his role as an impresario: “My focus was really about trying to extract the best. I knew Will was a genius. . . . I knew we had to get him out in the best way possible.” Braun didn’t want to leave his mark on Micropolis; it was Wright’s project. “I don’t think I have a single suggestion that’s mine in the game—intentionally.”¹⁰³ Although Wright typically receives the lion’s share of acclaim, he credits his collaborators for their important and often unacknowledged creative contributions and has described his role as a “traffic cop” to an unfolding creative synthesis.¹⁰⁴

[picture]

Developers at Braun’s condo/workspace (ca. 1988). From left: Jeff Braun, Richard Bagle, David Caggiano, Leda Zudowski, Michael Bremer, Will Wright. Braun rented another condo for the Amiga team.

Wright was responsible for the simulation, but others handled the user interface and platform conversions. Development focused on a Macintosh version, which later became the starting point for Amiga and PC ports. Mac front-end programming was done by Robert Strobel, who is also credited in the manual for design contributions (probably for the user interface). To make the art, Braun again tapped his network, and hired Don Bayless, a Kansas-based art therapist he had met through Calligrapher, who collaborated closely with Wright by email.¹⁰⁵ Designing letterforms that combine to form words was not unlike designing tessellating character graphics that composed two-dimensional landscapes.¹⁰⁶ Steve Hales, an experienced game programmer and sound designer, made the sound effects and did the sound programming. A handful of others contributed, for example, designing the scenario maps.¹⁰⁷

Growing into the Macintosh platform’s affordances, the city became more vivid and manipulable. One of Wright’s accomplishments had been getting his complex simulation to run on the C64’s now meager 1Mhz 8-bit CPU. But on newer platforms, with their high-resolution graphics and beefy 7Mhz 16-bit CPUs, more was possible. While the simulation core stayed relatively unchanged, it was refined and elaborated alongside the overall experience design.¹⁰⁸

Micropolis’s clunky user interface gave way to a lovely Macintosh-style one. The Mac was more than a beefed-up personal computer. Its operating system offered programmers a toolbox of ready-made GUI primitives, and Apple provided design guidelines as well as exemplar programs like MacPaint (1984) that the Micropolis developers studied and learned from. As in MacPaint, the tool palette sat on the left. Using the mouse, players selected tools and painted on the city “canvas.”¹⁰⁹ As far as the user interface was concerned, MacPaint was a more suitable exemplar than Pinball Construction Set.

Through these developments, it became easier to see and understand Wright’s microworld. Whereas the C64 version required players to navigate among various nested modes to see important information, the Mac version let players juxtapose and flit between views. Demand for residential, commercial, and industrial zones—the guts of the simulation Wright had adapted from Forrester—was now shown in the main window. The graphics became less abstract. On the C64, zone types were indicated with only a letter (R, C, or I). On the Mac, high-resolution art enabled a more naturalistic display. Belching smokestacks suggested pollution, and residential buildings now looked like housing. Architectural features, landscaping, and blight made wealth and population density plainly visible, surfacing more of the underlying simulation.¹¹⁰ Messages guided play and scaffolded interpretation with granular feedback and occasional advice: “Brownouts, build another Power Plant,” “A Monster has been sighted!!,” or “Citizens upset. The tax rate is too high.” Scenarios such as a monster attack in Tokyo, high-crime Detroit, and gridlock in Bern offered explicit play scripts.

The map got bigger and the world more detailed. Waterways disappeared, but fire stations and police departments were added, along with railroads, trains, and new simulation layers that modeled fire departments, police, and crime. A stadium and nuclear power plant were added. You could name your city. Disasters could happen automatically, and you no longer had to manually bulldoze if you wanted to build on a forest or waterfront. It looked and felt more like a living city yet retained a toy-like schematic clarity.

§§§

Braun brought a demo to Los Angeles, hoping to enlist his childhood friend Michael Bremer in the fledgling effort. Bremer, an aspiring screenwriter, had been working on a science-fiction story about artificial intelligences who hung out online in a place called “SimCity.” Bremer suggested the title to Braun, who still wasn’t satisfied with the name “Micropolis,” which, in any event, was also the name of a hard-drive manufacturer. The name “SimCity” was exactly what Braun had been looking for. It was a place that Sims lived. The name also suggested a brand with more Sim titles, which Wright and Braun both appreciated. For Bremer, the name was perfect not just because of its pun-tential, but because it meant that it was no longer Micropolis, an anonymous city, but a specific city, one filled with people that you presided over as mayor. Bremer was hired to write the manual.¹¹¹

Braun knew that his new company’s name would be on the box and asked his dad, a word game aficionado, to help him come up with it. He specified that it needed to be meaningless, have six or fewer letters, a small number of syllables, and be in the same vein as Atari. “Maxis” was the name he came back with, which he recommended because having an “x” in the middle was cool, it had mother and sister in it (“ma,” “sis”), and it spelled “six a.m.” backward. Braun loved it.¹¹²

Another childhood friend, Richard Bagel, handled the package design. Bagel hired Kurt West for the illustration, which depicted a retro-Bakelite-radio city control panel. Of the box, Braun says, “the idea was to cause questions more than answers. Like, what is this Bakelite radio thing with this monster coming out of the screen? What’s going on here?”¹¹³ Envisioning SimCity as having intellectual substance and educational value, Wright hired Cliff Ellis, a UC Berkeley PhD student, to write a short essay on the history of cities and city planning for the manual. (Ellis learned of the opportunity through the Berkeley Department of City and Regional Planning, where he studied the history of urban form.)¹¹⁴

[picture]

From left: Jeff Braun, Daniel Goldman, Will Wright, Michael Bremer, Michael Paterson, David Caggiano, and Tim Johnson, in the parking lot of the first Maxis office (ca. 1989). Bremer holds an outsized SimCity box image.

The project eventually went over Braun’s budget, which is how Wright came in as co-owner of Maxis. Recognizing that the project was special and that software development was unpredictable, Braun didn’t impose a development schedule. But after about a year and a half he ran out of money. Wright nervously invested a little over $40,000 (most of his cash) into Maxis in exchange for a 15 percent stake (Braun had already invested a little over $100,000; Wright would also earn royalties from SimCity).¹¹⁵

As SimCity approached completion, Braun sought a publishing partner. He returned to Brøderbund, but Gary Carlston, one of Brøderbund’s founders, again rejected a publishing deal—he thought that SimCity was still too exotic. The Carlstons, however, were seeking to develop their company in anticipation of going public and offered Braun a favorable consignment deal as part of an experimental new program.¹¹⁶ Sensing that SimCity would be big and not wanting to cut his teeth as a publisher on it, Braun first shipped Sky Chase (1988), a two-player dogfighting game, under this arrangement.¹¹⁷

SimCity was launched in February 1989 for the Macintosh,¹¹⁸ followed by ports for Amiga, Commodore 64, and then PC.¹¹⁹ There was a hitch with completing the C64 version: Wright had lost its human-readable source code. All that remained was the compiled program binary, a long, cryptic sequence of numbers. In order to emblazon “SimCity” on its title screen they resorted to editing the compiled binary with a bitmap editor. It is for this reason that the C64 release is historically important. Like an insect trapped in amber, it depicts Wright’s City Planner before it blossomed into SimCity.¹²⁰

§§§

So why didn’t Wright go along with Brøderbund, and turn City Planner into a game? In hindsight, the answer seems obvious: Wright’s signature irreverence toward game design norms. But why, at this early junction, did he do it? At one level, SimCity realized a proclivity for self-directed creativity and learning that his family had long encouraged and supported. And Raid’s success, plus Pinball Construction Set’s example, surely bolstered his self-confidence. But something very specific happened during City Planner’s development that oriented Wright’s approach to SimCity and beyond. If Wright has an essential design sensibility, then perhaps this is it. In a rare piece of writing published soon after SimCity’s launch, Wright explained “why SimCity ended up with such loosely structured goals.” First, he wanted to share his own experience of “interactive discovery”—of learning through play—with SimCity’s players. This, after all, is what had drawn him to the project in the first place. Second, and perhaps more important, he wished to honor player creativity: “We left it up to the player to decide what the desirable outcome was. A city reflects the values of its designers.”¹²¹ (The full sweep of this perspective can be seen, for instance, in The Sims’s inclusive approach to sexuality, which stemmed from a desire, Wright explained, for players to be able to “construct whatever family they came from or could imagine or wanted to play with.”)¹²²

[picture]

SimCity (1989) for Macintosh.

Perhaps unsurprisingly, the process of creating SimCity and exploring the potential of interactive discovery led Wright to see himself as a kind of science popularizer. Moving forward, he would use the medium of computing, the magic of interactivity, and the potency of play to bring esoteric topics like urban dynamics, Earth science, ant colonies, and complex systems to popular audiences.¹²³

7

Maxis at the Crossroads

By the late 1980s, general-purpose computers assumed many guises. They managed airline reservations and battle spaces; vivified games in arcade cabinets and consoles; juiced PCs in homes, schools, and offices that played games, reckoned spreadsheets, mediated graphics, edited photographs, typeset documents, processed words, and much else; they were automatic teller machines and, in homes across France, portals to online information services. Industry, academic, and government elites no longer held a monopoly. New specializations such as game design and computer-human interaction emerged, crystallizing through and within a bewildering array of publications, conferences, and professional associations, and their attendant commentators and critics.

A diverse mass of eyeballs turned its compound gaze to SimCity and considered this strange new offering. There were many roads to SimCity, and each approach—whether entertainment, education, business simulation, city planning, or politics—brought new citizens, not to mention channels for promotion and distribution. Reviews from manifold communities appeared, each greeting SimCity on their own terms.

InfoWorld, a business computing weekly, thought SimCity, and “system simulation software” more generally, might cultivate critical thinking skills among managers, and praised it as “a most addictive morsel of brain-engaging artistry.” Future Maxis titles, the article hoped, would “simulate business decision making.”¹

In the gaming sphere, SimCity claimed top honors,² including Computer Gaming World’s “Best Game of the Year,” where it quickly ascended the charts.³ This community was finely attuned to SimCity’s strategic depth, technical sophistication, and naturalistic fidelity. It was a game, a toy, a realistic model, a strategic challenge, and, most important, “extremely addictive.”⁴ A common, if questionable, observation was that SimCity had reconciled “serious simulation” with entertainment.⁵ It was so “realistic,” one review claimed, that city planners were taking it up.⁶

Indeed, architecture and urban planning professors at Stanford, Brown, and other universities were interested in, or planned to use, SimCity in their courses.⁷ A GIS consultant felt that SimCity could familiarize people with using computers for city management.⁸ Portland’s mayor wrote a letter to Maxis: he wished running a city was this easy.⁹ The Providence Journal orchestrated a playoff for the city’s Democratic mayoral primary.¹⁰

Traditional media outlets like the New York Times emphasized SimCity’s sophistication, accessibility, and the unexpected pleasure of urban planning.¹¹ Newsweek’s erudite review echoed Le Corbusier, noting that “flying low over a good-size city is enough to make the heart sink.” SimCity offered a Robert Moses–like power to take action—albeit within an enchanting “simulacrum,” a “living, changing, growing-or-dying model of the complex urban organism.” The article voiced what would become a recurring critique: SimCity harbored certain biases. As one San Francisco urban planner and fan noted, “There’s no NIMBY [not in my backyard] syndrome.” Will Wright commented that SimCity’s in-built assumptions inspired debate among “average people, not planners” about policy and planning.¹²

Soon after placing SimCity in Macworld’s “Game Hall of Fame,”¹³ Steven Levy, a technology journalist steeped in the history of computing, published “Exploring the Nature of Simulation versus Reality.” Yes, SimCity was being taken up by city planners and in college courses, but was this “a valid practice, or are these folks tricked by an illusion?” The question had political import, as simulations veined with biases, Levy noted, “run on government computers to help determine national policy.” His real interest in simulation was ontological: “Not what they profess to address . . . but the separate realities they generate.” Levy, who would soon publish a book on artificial life, looked to simulated bird-like objects called boids. These, he claimed, didn’t merely imitate flocking birds, but “are actually flocking.” Levy’s primary interest in SimCity was this: if a mesmerizing cellular automaton represented the universe as a complex system to a scientist, SimCity played the same part but for a popular audience; it was as close as he could get to the mysteries of simulation on his Macintosh.¹⁴

In linking SimCity to the history and philosophy of computer simulation, was Levy out on a limb? If anything, this piece—alongside the other reviews—pointed toward the many simulation traditions Wright had borrowed from and the diverse communities Maxis was linking up with. The Whole Earth Review covered SimCity as a diverting simulation, but the context—situated between an interview with the Santa Fe Institute’s president and books on demography, economics, and politics—outlined a more nuanced engagement.¹⁵ “Cybernetics spawned this magazine,” wrote Kevin Kelly, who explained that computer simulation allows complex adaptive systems, like life, to be studied.¹⁶ As we shall see, a shared interest in cybernetics, games, simulations, artificial life, complexity science, and education would help Maxis establish links to far-flung communities of practice. Kelly, who would soon cofound Wired, emerged as a chief exponent of the 1990s techno-utopian culture that Maxis inhabited—artificial life enthusiasm, dot-com bubble, and all.¹⁷

§§§

What was SimCity? Was it a tool university professors could use to teach urban planning? Or was it a child’s plaything? A tool for city management or a philosophical provocation? A toy for communing with complex systems? A strategy game? A business management training tool? Maybe it was just a fun way to learn about cities—a subject that appealed to children and adults alike. SimCity had it all. An aura of realism and import. The “holding power”¹⁸ of a game. The seductive fantasy of control. A shimmer of science, a gleam of complex systems and cellular automata, and a wink of philosophy. It was realistic yet playful; complex yet simple; educational yet amusing. With SimCity, you could play something with the appeal of a computer game that nonetheless retained a whiff of serious simulation, city planning, and self-organizing systems. It didn’t feel like an abstract cerebral simulation but a vivid make-believe world. SimCity emanated an aura of sophistication but went down more easily than Pac-Man.

Maxis leaned into this ambiguity; SimCity’s packaging didn’t exactly clarify matters. The box was whimsical, but the manual included an essay, “History of Cities and City Planning.” Maxis’s documentation soon trumpeted SimCity as a “new type of entertainment/education software, called SYSTEM SIMULATIONS,”¹⁹ which Maxis soon reframed as toys²⁰ and then software toys (which, they noted, could be a kind of system simulation). “Toys, by definition, are more flexible and open-ended than games.”²¹ In attempting to pin down what exactly they were making, Maxis compounded SimCity’s interpretive fluidity, encouraging its uptake in wildly divergent contexts. In a remarkable reversal, the delicate topic of SimCity’s genre classification, fraught ever since Wright’s contract with Brøderbund, became a windfall.

Diverse receptions buttressed one another. City planners and professors found SimCity’s verisimilitude compelling, allowing game players and journalists in turn to qualify the simulation as realistic. Educators could then point to SimCity as an example of something that was both educational and fun. SimCity’s amorphous liminality enabled it to be taken up by heterogeneous communities that egged one another on. In any event, the history of computing suggests that the boundaries between serious tools and playthings had always been porous.²²

As a result, you could read about SimCity in the New York Times or Computer Gaming World and buy it at the mall. Autodesk’s CA Lab (1989), by contrast, was not marketable through the same channels. Although Spacewar!, Conway’s Game of Life, and Jay Forrester’s simulations had also rippled into mass-media outlets, only SimCity could be bought and played.²³

§§§

SimCity’s polyvalence helped Maxis cultivate ties with diverse communities: venture capital, science, education, business, public policy, and videogames. But what was Maxis? What community did it belong to? Should it make games or business simulations? Educational products or toys? SimCity’s reviews foreshadowed the crossroads Maxis would face.

Wright’s creative process compounded the difficulties. “No one in their right mind would make a game about city planning for everyone else,” says Jeff Braun. Wright made City Planner “for himself,” but Braun had seen in it “something that others will enjoy,” and in the case of SimCity, at least, “what Will wanted lined up with the customers in a really nice way.”²⁴ This wouldn’t always be the case. Beyond chasing unproven themes, Wright’s innovative approach often required extensive research and development runways—and ample room for risky experiments.

This chapter unspools the story of Maxis in three acts.

Act 1, “The SimCity Network,” examines how Wright and Maxis brokered ties with diverse computing communities, from complexity scientists at the Santa Fe Institute (SFI) to Nintendo’s Shigeru Miyamoto, the father of Mario. The SimCity “network forum”²⁵ amplified the power and reach of these communities, underwriting Maxis’s growth.

In act 2, “The Studio Strategy,” Maxis barrels towards a dot-com initial public offering (IPO) while straining to reconcile the diverse actors that comprise the SimCity network. Wright struggles to attract support for his dream project, a dollhouse simulator that eventually becomes The Sims.

In act 3, “Unraveling,” the tensions of the SimCity network, combined with the pressures of a fast-changing game market, finally prove ruinous, prompting Maxis’s sale to Electronic Arts. The Sims, this chapter contends, could only have gestated in Maxis’s permissive atmosphere, which afforded Wright both the runway and resources to nurture this ambitious and improbable hit. While the SimCity network provided the social, intellectual, and technological conditions for The Sims, a more disciplined corporate patron was necessary to bring it to market.

Act 1: The SimCity Network

SimCity was a hit, selling just over 80,000 units domestically in the first year, and earned $3 million in revenue.²⁶ As a result, Braun increased Wright’s share of Maxis to 30 percent.²⁷ Graphics expansion packs—ancient and future cities—and minor improvements followed. Electronics Boutique, in its 1990 Christmas catalog, gave as much space to SimCity and its expansions as to Tetris and its small coterie of variants.²⁸ Sales were good, but there were hitches. Not only did Toho, Godzilla’s copyright holder, come after Maxis, but the IRS scrutinized its books. Braun, who could barely read a balance sheet, was in over his head. He responded by bringing in managers for product development, accounting, and marketing. The company was top heavy but could now grow more comfortably.²⁹

Braun and Wright divided responsibilities: “I’d run the business side of things and he’d run the programming side of things. And I never really told him what to do and he never really told me what to do.”³⁰

Wright followed his muse. As he had throughout his life, he studied new fields and built models. An admirer of Carl Sagan, the preeminent science popularizer, Wright now saw himself as a synthesizer who popularized esoteric subjects through the medium of interactive computing.³¹ Each “game was almost an excuse to do years of research on a subject that I like.”³² Wright’s next enthusiasm was the Gaia hypothesis, which inspired SimEarth. Gaia theory germinated from NASA’s attempt to find life on other planets by looking for telltale biotic signatures in the atmosphere. The theory was developed by James Lovelock, who had pioneered life-detecting instruments for NASA in the 1960s, in collaboration with Lynn Margulis, a trailblazing microbiologist. Gaia synthesized the view from above (atmosphere) with the one from below (microorganisms), and conceptualized Earth as a self-regulating system in which life maintains and generates the conditions for its own existence. (Author William Golding suggested the name “Gaia,” the Greek goddess of Earth; Margulis held her nose.)³³

Lovelock was no stranger to toy simulation models. His proudest invention was Daisyworld, a “parable about Gaia” in which fluctuating populations of dark- and light-colored daisies stabilize planetary temperature. So when Wright met Lovelock and invited him to collaborate on SimEarth, Lovelock enthusiastically agreed.³⁴ It didn’t hurt that they both loved simulations and spoke the same cybernetically inflected language. Like Wright, Lovelock’s work crossed cultural and disciplinary boundaries. While Gaia theory appeared in respected scientific journals, it was popularized through Stewart Brand’s CoEvolution Quarterly (a Whole Earth Catalog successor) and became associated with New Age spirituality (a casualty of its goddess moniker).³⁵ It was Brand, an adherent of Homo Ludens and ardent conservationist, who thought “play is the means” to “Earth Consciousness,”³⁶ who introduced Wright to Lovelock. (Wright met Brand, an SFI trustee well versed in cybernetics and computer games, through Kelly.)³⁷ Lovelock penned an introductory essay for SimEarth’s manual, and Maxis, without being asked, contributed royalties back to Lovelock’s charity, helping to sustain his work.³⁸ Maxis closed the loop between science and popular culture.

The Gaia connection brought Maxis in step with other efforts to bring cybernetically inspired whole-systems thinking to new communities. The Global Business Network (GBN), a consultancy cofounded by Brand, brought Wright into their consulting network. GBN melded ideas and approaches from cybernetics, complex systems, and the counterculture and saw in Wright’s Gaia simulation a tool for understanding the world.³⁹ While GBN brought future-visioning scenarios and adaptive systems thinking to multinational corporations and military and intelligence leaders, the launch of Biosphere 2 in 1991 captivated mainstream and countercultural media alike with its charismatic simulation of Earth’s ecosystems and the “biospherian” caretakers who eked out an existence from within its glass-enclosed world.⁴⁰ Like SimEarth (1990), Biosphere 2 vivified, and brought to mass audiences, closed-world models of “Spaceship Earth,”⁴¹ and coincided with humanity’s dawning understanding of its self-inflicted climate change predicaments.⁴²

To help complete SimEarth, Maxis hired Fred Haslam, who had impressed Wright with his knowledge of plate tectonics, to help create its geologic, biologic, and sapient models.⁴³

Despite positive reviews in Time and the New York Times, sales were disappointing.⁴⁴ Haslam’s demonstration on PBS’s Computer Chronicles was a total hash, underlining the inaccessibility of SimEarth’s software and concept—especially as compared to SimCity.⁴⁵ And while SimEarth’s sophisticated simulation is unmatched by Wright’s other titles, it wasn’t a construction set—a quality Braun and Wright would later become highly attuned to.⁴⁶ SimCity’s critical and commercial success, after all, was only partly due to the deep research and model making Wright loved.

Braun hadn’t thought SimEarth would sell but wanted to support Wright as a creator and invested in a quality manual, packaging, and marketing—including two strategy guides. Braun’s goal was “to protect him, in a sense, and create a structure where he can do his thing. It really was not about me and what I thought would be successful. It was about Will and getting his creative thing out.”⁴⁷ SimCity demonstrated that Wright’s offbeat projects could be quite lucrative. As a result, Maxis incubated boldly experimental works that also threatened the financial viability of the entire enterprise.

In marketing SimEarth as “The Living Planet” and SimCity as “Alive!,”⁴⁸ Maxis tapped deeply rooted resonances between life, computing, intelligence, and other complex systems that had long pervaded the history of computing. With SimAnt (1991), the theme of computerized life came to the fore. The title was inspired by the work of E. O. Wilson, whose discovery that ants communicate via pheromones—such as the chemical trails they leave for one another in the environment—revolutionized myrmecology.⁴⁹ Wilson neglected to read Wright’s letter inviting his collaboration but was later charmed by SimAnt’s “sophistication and precision” and thought it would attract more entomology majors.⁵⁰ Echoing SimCity’s division of labor, Wright’s high school friend Justin McCormick handled the front-end programming and Wright focused on the simulation core. The simple elegance of ant intelligence, says Wright, made SimAnt an easy project. They finished in well under a year—a testament to their rapport and round-the-clock efforts: McCormick worked at night and Wright during the day, with twice-daily handoffs, including a breakfast debriefing at an Orinda café. (This helped McCormick spring an Easter egg on a delighted Wright: the wolf spider, whose leg kinematics Wright had studiously devised, could retaliate against the ants with laser beams.)⁵¹

[picture]

SimEarth (1990) and SimAnt (1991).

Like SimEarth, SimAnt was grounded in studious research. But the scale and setting—the backyard of a suburban home—softened the blow of its strange subject. Ants, after all, are familiar because they visit our homes; SimAnt invited players to follow the trail underground, back into ants’ homes. Players returned, as it were, in the form of invading ants. Skillful players could “win” by driving terrified humans from their house and overcoming the red colony.⁵² By decentering the human, SimEarth and SimAnt ushered players into the strange “more-than-human world.”⁵³ SimAnt’s playful and pronounced irreverence, especially toward human life, helped soften the blow of this displacement. Absorbing a lesson from SimEarth, Wright emphasized accessibility, fun, and lightheartedness. Despite—or perhaps because of—these efforts, SimAnt sold to an undesirably narrow demographic of ten- to thirteen-year-olds.⁵⁴

Wright’s most marketable titles, he would later reflect, are economic. In SimCity and The Sims, players earn and spend money—a familiar concept. Planets and ants, by contrast, traffic in nonhuman energies. These biological systems are strange and squishy—harder to read, shape, and relate to.⁵⁵

Michael Bremer, whose title, since naming SimCity and writing its manual, had become “Voice of Maxis,” fondly recalls Braun exhorting him to “get weirder”—a trick that served him well, especially when writing SimAnt’s manual. He wrote documentation as if it was simultaneously an experience as well as customer support. As an aspiring screenwriter turned technical writer, this made perfect sense.⁵⁶ Bremer’s masterful ability to imbue strange and complex subjects with accessibility and whimsy also underscored the tenuous bricolage of Maxis itself; urban planning, Gaia theory, and myrmecology were abstruse topics for most people.

If the mass market found simulated ants to be a strange choice, artificial life researchers did not. Ants were, Wright explained, the field’s unofficial “mascots.” Wilson likened insect colonies to human brains: “Individual brain cells are dumb, but millions together are very bright.”⁵⁷ It was only within the past handful of years that artificial life had gained a kind of mainstream academic respectability, and SimAnt arrived, practically on cue, to a gush of popular enthusiasm for the subject. Coverage wasn’t limited to Scientific American, but outlets such as the New York Times, Whole Earth Review, Wired, and Newsweek, which combined reportage of Christopher Langton and Maxis alike.⁵⁸ In 1992, Levy published his Artificial Life book, and Maxis released SimLife. In 1993, Maxis published Unnatural Selection and El-Fish. Once again, Maxis closed the loop between scientific research and popular culture, if not the mass market.

Maxis demonstrated a prerelease version of SimLife at the 1992 Artificial Life III conference, which one observer described as having the “feel of a science fiction convention, a rock concert, and a scientific conference.”⁵⁹ Researchers from academia and industry hobnobbed with journalists like Levy, Kelly (a SimLife beta tester), and James Gleick (Chaos).⁶⁰ MIT’s Lego–LOGO lab hosted a robot-creature building workshop and contest, causing a good deal of lost sleep. Craig Reynolds spoke about using flocking simulations for Hollywood special effects.⁶¹ Conference sponsorship came from SFI (which published the proceedings as part of its Studies in the Sciences of Complexity series), the Los Alamos National Laboratory Center for Nonlinear Studies, and Apple Computer’s Advanced Technology Group, whose Vivarium research program sought to teach children about complex systems. Vivarium proceeded in collaboration with the MIT Media Lab and the Los Angeles Open School and was advised by world leaders in artificial intelligence, science fiction, biology, and animation.⁶² Of course Maxis was at Artificial Life III.⁶³

§§§

The Maxis delegation included SimLife’s creator, Ken Karakotsios, an Apple hardware engineer whose enthusiasm for artificial life had been kindled by Scientific American. Karakotsios’s CASim (1989), which enabled Macintosh users to simulate cellular automata, served as his entrée to cellular automata society. Inspiration came by way of Rudy Rucker’s cellular automata class. Marooned on San José State’s campus in the aftermath of the 1989 Loma Prieta earthquake, Karakotsios began planning CASim—Rucker’s class used his PC-only CA Lab. Levy, naturally enough, wrote about it in MacWorld. Researchers from SFI got in touch. At a local simulation meetup, Karakotsios met a like-minded enthusiast: Wright. Inspired by the work of SFI, Artificial Life conference proceedings, Richard Dawkins’s writings, and Wright’s encouragement, Karakotsios pitched the idea for what became SimLife to Wright and Braun.⁶⁴

True to Maxis’s vision of itself as a publisher that nurtured talent, it backed Karakotsios’s project. CASim’s reach was limited, but with the support of Maxis, who knew how far a SimLife might go? And the opportunity to contribute to the Sims franchise? Karakotsios was awestruck. Unsure if he’d make “a good game,” Maxis bucked up Karakotsios’s confidence, supporting him with art, additional programming (McCormick), and weekly phone calls with Wright, who offered design advice.⁶⁵ Braun didn’t find SimLife particularly fun, but honored his intention to cultivate talent. Despite lively press interest in SimLife and artificial life more generally, sales were disappointing.⁶⁶ But even if SimLife wasn’t a commercial success, it was enthusiastically received in other arenas.

Langton, the dean of artificial life, enthusiastically reviewed SimLife for the Bulletin of the Santa Fe Institute.⁶⁷ Like Maxis, SFI had also recently begun to make serious investments in artificial life. But SFI’s nontraditional research agenda hobbled its ability to secure support from traditional science funding agencies, prompting them to seek corporate partnerships. They were grateful to Levy’s book for bringing their work to a wider audience⁶⁸ and welcomed Maxis on similar terms. Wright, who was admired within the SFI community, had been a regular visitor since at least 1991, when he attended an intimate Workshop on Adaptive Computation with scientists, many of them key figures in the history of computing and simulation (among them were Arthur Burks, John Holland, Christopher Langton, and Nicholas Metropolis) and investors (typical for SFI).⁶⁹ Wright befriended Holland (a SimCity fan) and visited him in Michigan, where they brainstormed ideas. But while there was chatter about Maxis-SFI software collaborations, these never materialized. Who would they sell products to?⁷⁰ This, perhaps, was beside the point. Maxis brought the gospel of “complex systems”—the contact language of the SFI network—to broad audiences, from classrooms to corporations. SFI consequently celebrated their association with Wright and the SimCity network, which helped them expand their own cultural and institutional reach.⁷¹ Later, in 1994, when SFI celebrated their first decade, they quoted Wright, alongside other stars, toasting them on the back page of their Bulletin:

Some may consider the research done at Santa Fe rather esoteric and therefore not directly applicable to their lives. But we’ve used many of the ideas developed there to introduce millions of kids (and adults) to complex systems through our computer games.⁷²

Maxis–SFI liaisons also illustrate how Maxis kept pace with scientific developments. SimLife was “awfully close to being a useful tool for scientific research,” gushed Langton’s review.⁷³ Artificial life researchers had begun attending to ecology and artificial worlds,⁷⁴ key foci of SimLife. During development, in fact, SFI researchers asked Karakotsios to add features that might benefit them, some of which, like a genetic algorithm editor, he added as Easter eggs. Stephen Wolfram inquired about hooking up SimLife to Mathematica; Karakotsios added a data export feature. After launch, Langton tried to recruit Karakotsios for his embryonic “Swarm” project.⁷⁵ But Karakotsios had other plans. Excited by the prospect of postdocs and schoolkids alike using his work, he began his next Maxis project.⁷⁶

He dreamed of communicating with an alien civilization. But since interstellar communication was unlikely, Karakotsios imagined growing an alien society in silico. He began an elaborate design document and prototype for “Mindshare,” a simulation of jungle-dwelling “primitive-looking people.” Could they begin with a minimal language and develop abstract concepts, make discoveries, invent money, and ultimately become as “rich and alien as anything we expect to find in the broader universe”? (Around the same time, an SFI anthropologist began simulating ancient Anasazi communities with agent-based models.) Karakotsios got the people walking around an axonometric world and greeting one another. He exchanged ideas with Wright, who had also recently begun a project involving simulated people. While Wright encouraged Karakotsios to keep it simple, Mindshare ultimately exceeded his grasp. As Maxis’s priorities shifted, the company lost interest. His confidence exhausted, Karakotsios hit a brick wall and quit.⁷⁷ As a result, SimLife marked Maxis’s final release at the frontier between vanguard science and popular entertainment.

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SimAnt showed Wright an alternate path toward simulating people. The ants’ intelligence was modeled using simulated pheromones: ants made decisions by responding to an environment that they themselves modified. The strategic context for play was domestic: ants could invade a house, inhabited by a simulated person, who did things like mow the lawn, watch TV, fix food, and, of course, freak out about the ants. In writing the artificial intelligence logic for the ants and the person, Wright found that the ants exhibited far more “complex behavior than this guy in the house,” despite the fact that his programming was an intricate “pain in the ass.” The ants, says Wright, “taught me a particular approach”⁷⁸—one that would form the core of a new project.

SimAnt reflected the budding enthusiasm for artificial life in academic and popular cultures and also reiterated a transformation then underway in the cognitive sciences—one that echoed the intellectual turn toward emergence.⁷⁹ What if the profoundest kind of intelligence wasn’t to be found in playing chess, but simply walking around your home, making breakfast, or cooperating with other people in the world? Once upon a time, such social and domestic activities had been overlooked by researchers, most of whom were mathematically inclined men. To those who cherished abstract symbolic reasoning, Chess was a fine measure of intelligence, but perhaps, in the final analysis, embodiment posed the most profound challenge of all. Researchers turned to biological systems for guidance, and models of intelligence became more embodied, bottom-up, and socially situated.⁸⁰ The humble ants led Wright toward a robust simulation of human intelligence. Moreover, the approach paralleled contemporaneous developments in cognitive science and artificial intelligence, where insect models played a comparable role.⁸¹

[picture]

SimAnt (1991) house view.

Wright had just completed SimAnt when the 1991 Oakland Hills wildfire consumed his home. Surveying an alien landscape of melted cars and carbonized houses, he found that ants, deep underground, were the only surviving life-form. And while he and his family narrowly escaped, their physical possessions were incinerated. He hated shopping, but now that his family had to rebuild their home and fill it with stuff, Wright glimpsed his life from a fresh vantage. What was all that stuff actually good for? What needs did it purport to fulfill? What was a life actually made of? Wright turned to psychological theories, such as Maslow’s pyramid of needs, and analyses of how people shop and spend their time.⁸² And he looked to the work of Christopher Alexander, whose A Pattern Language offered an architectural vision in which every element, from the placement of a bench or window to the layout of a city, met fundamental human needs.⁸³

Like ants, people shape and are profoundly shaped by their environment. But how exactly does the material world satisfy desires and shape behavior? A concise answer to this puzzle, Wright realized, offered a key to understanding and also simulating people. The right set of motivations and objects would allow him to simulate people as elegantly as SimAnt simulated ants. Wright called his nascent project “Dollhouse,” evoking a toy world of domestic architecture, people, and their possessions.⁸⁴ Initial prototyping was done in the SimAnt code base, which already contained a person and could depict a house as a 3D architectural-style cutaway. McCormick returned to SimAnt’s code, where he experimented with 3D terrain.⁸⁵ Another early collaborator was Mick Foley, who had, since bringing Wright to the party where he met Braun, graduated from college and taken a job at Maxis. Together, Foley and Wright built a Dollhouse prototype that riffed on the domestic setting and 3D graphical style of SimAnt’s yard view. Foley handled the axonometric graphics code, which could flexibly render a variety of house layouts, while Wright wrote an animation system for marionette-like people, who walked and danced through these spaces.⁸⁶ Just as an ant colony might, through its multitudinous meanderings, eventually infiltrate a bag of cookies, Wright began making his way, one tenuous experiment at a time, toward The Sims.

§§§

In summer 1989, not long after SimCity’s launch, Braun received a call from Nintendo. Shigeru Miyamoto, their star designer, loved SimCity and wanted a version for their eponymous console. When could Braun visit their American headquarters to discuss? The next day, he was on a plane to Redmond, Washington, where he met Nintendo’s famously hard-nosed Howard Lincoln and his boss, Minoru Arakawa, the president of Nintendo of America and son-in-law of Hiroshi Yamauchi, the third president of Nintendo, a hundred-year-old company originally founded to sell handmade hanafuda playing cards. Under Yamauchi’s iron-fisted but unusually broad-minded leadership, Nintendo had experimented with offbeat businesses, like selling instant rice and operating love hotels, eventually striking success in novelty toys and videogames to surpass Toyota as Japan’s most successful company. Lincoln laid out the terms of a nonnegotiable offer. Nintendo would handle the adaptation and pay Maxis a royalty of one dollar per cartridge. Sales, they estimated, would top a million units. If Braun signed now, Lincoln explained, sliding over a contract, then he would also slide over a check—a signing bonus for $1 million. Braun took the deal.⁸⁷

In October, Nintendo flew Wright and Braun to Kyoto where they were welcomed as family and treated like royalty. Both were surprised by Nintendo’s hospital-white interior, where Wright spent a week-long design intensive with Miyamoto. Charming and hospitable, Miyamoto took them people watching, pointed out tanuki statues, and to visit his beloved Fushimi Inari shrine. Although he had two young children, Miyamoto graciously took them out every night. This included a visit to his home and an outing to play pachinko, where Wright learned why old men played for items like cooking oil: “selling” these circumvented gambling prohibitions.⁸⁸

Miyamoto, in Wright’s view, retained the playfulness of a child, and was a master of emergence: the basic mechanics of Mario, like a ricocheting turtle shell, recombined into an exuberant complexity. As a sign of respect, his fans called him “Dr.” Miyamoto, an honorific he and his staff—which were clearly taken not just with SimCity but also its creator—now bestowed on Wright. Nintendo knew the value of a good character and had clearly met one in Wright, whose unsettlingly wide-ranging expertise and taste for the absurd inspired the professorial yet whimsical “Dr. Wright,” a manga-style homage Wright was honored to accept.⁸⁹

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Miyamoto, Wright, and Braun in Kyoto (1989).

Wright and Miyamoto formed a lasting friendship, but the forces that drew these playful spirits together were larger than either of them. Nintendo’s lifeblood was deeply experimental, and Miyamoto, like Wright, gathered inspiration from well beyond the cultural mainstream of computerized entertainment. If any videogame maker were to seek out SimCity, Nintendo was it. Having saturated the traditional videogame market (eight- to thirteen-year-old boys), Nintendo desired, in Yamauchi’s words, “to go beyond entertainment,”⁹⁰ and sought titles, like Tetris and SimCity, that might entice new demographics. While dreaming up ideas for Nintendo’s forthcoming console, Miyamoto imagined a game where players created their own world; when he discovered SimCity, he sent for its makers and sought a deal allowing him to put his own stamp on it.⁹¹

[picture]

“Now here’s an interesting fact,” says Dr. Wright. “According to my statistics, over half of all mayors regret the decision to sack their cities, but fortunately for their peace of mind the guilt lasts only 2.6 seconds on average.”

Contemplating programming as a form of entertainment, Miyamoto also had his team study children’s programming languages.⁹² And receptive to a pitch from MIT’s Seymour Papert, Marvin Minsky, and Nicholas Negroponte, Nintendo donated millions toward researching the educational potential of videogames.⁹³ If MIT were to design a Nintendo game, said Papert, it would look to Tetris and SimCity for ideas.⁹⁴

If anything, Nintendo’s adaptation (and Papert’s comment) underscores SimCity’s interpretive versatility. Like Maxis, Nintendo leaned into SimCity’s contradictions, distilling them into the figure of Dr. Wright, who embodied the merger of sophisticated science with topsy-turvy popular culture, a paradox that emerged from SimCity and Wright himself. SimCity, which evocatively straddled multiple worlds, facilitated exchange between those worlds. But even as Maxis was welcomed into the very different communities of videogames and high science, an altogether different world beckoned—one that would have profound implications for Maxis’s future.

§§§

Maxis was also approached by companies seeking bespoke simulations. This was the business simulation market InfoWorld had envisioned. Maxis turned them down, but the invitations kept coming. In 1991, after around two years of requests, Maxis began saying yes and subcontracted development to Delta Logic, an upstart outfit in Monterey. The arrangement, at least in theory, would allow Maxis to take on business simulations without distracting its core development team. Chevron’s SimRefinery was the first project.⁹⁵ Maybe Maxis was also a business simulation maker?

All along, Braun had wanted to be a publisher and support fledgling developers. That was how the original deal with Wright, after all, had been structured. His dream, in fact, was to take Maxis public, like Brøderbund. But Brøderbund was handling Maxis’s sales and distribution and taking a 20 percent cut. And for what? Braun wasn’t getting the marketing and sales support he wanted, and its cut, in 1992, was $2 million of the $10 million revenue generated by SimCity and SimEarth. But divorcing Brøderbund and transforming Maxis into a full-fledged publisher that handled its own sales, marketing, and distribution was a gutsy move. Careful preparation was necessary. Seeking a “net underneath the tightrope” and an investor that would help take them public, Braun began talks with venture capitalists (VCs) and brought Maxis’s eccentric lawyer, Bob Derber, in-house.⁹⁶

Braun had been scouted by VCs before, but they hadn’t been interested in Maxis. Games are a fickle business. You might have a hit, but then revenue drops off a cliff. “They said we were like vegetables—we go bad.”⁹⁷ But SimCity’s critical and commercial reception suggested that Maxis might be different. Warburg, Pincus (WP), a New York–based private equity firm, certainly saw things that way. It would never have invested in a game company; the spectacular crash of the videogame industry in the early 1980s served as a cautionary tale. (Legend had it that Atari stealthily buried truckloads of unsold E.T. cartridges in a New Mexico landfill. All true.)⁹⁸

It was Esther Dyson—“Silicon Valley’s answer to a Hollywood agent,”⁹⁹ polyglot daughter of physicist Freeman Dyson, former Wall Street analyst, Silicon Valley journalist and venture capitalist, GBN member, and SFI trustee—who introduced Braun to Bill Janeway at PC Forum, her annual salon for Silicon Valley insiders. Janeway had recently joined WP to expand its high-technology venture portfolio. While Braun and Wright both worried about losing their independence to shortsighted investors, Janeway, with his partner Henry Kressel, won them over.¹⁰⁰ Kressel had worked for RCA and helped pioneer the laser diode, which impressed Wright, who once described Janeway to me as the button-down financier.¹⁰¹

Janeway, in fact, had an abiding interest in computer simulation. Son of a renowned economic pundit and political adviser, Janeway had earned an economics PhD from Cambridge supervised by a John Maynard Keynes acolyte. His dissertation topic, the 1929 bubble and its aftermath, would prove relevant to his Wall Street career, which began during an epochal moment of deregulation in the 1970s, when modern financial capitalism was born. As traditional econometric models proved vacuous in the face of the economic discontinuities that resulted from the 1973 energy crisis, Janeway looked elsewhere and connected with the MIT System Dynamics National Modeling Project. The system dynamics approach was diametrically opposed, in Janeway’s view, to the neoclassical economic orthodoxy that had bastardized Keynes’s ideas. For Janeway the economic theoretician—and scholar of politics, government, and history—Forrester’s methodology of theory over empiricism was a feature, not a bug.¹⁰² “The computer,” he wrote in a 1977 report, “is a tool for the theoretician.”¹⁰³ The encounter sparked Janeway’s interest in computers as “simulation engines,”¹⁰⁴ which he divided into two categories: “prediction” (like econometrics) and “explanation” (like system dynamics).¹⁰⁵

Maxis, as Janeway saw it, wasn’t a game company but a simulation firm making explanation models. As the computer market moved from hardware to software, Janeway and Kressel looked for a software play, and Wright’s radiant talent attracted their attention.¹⁰⁶ Interest was there: Maxis had already inked a deal with Chevron for SimRefinery, and WP’s investment would help them move deeper into the “serious education market,” as Dyson, who had attended at least two SFI workshops with Wright, explained in Forbes.¹⁰⁷ To that end, WP established a high-octane science advisory board—with strong ties to Xerox PARC, SFI, Interval Research, and Intel—to plug Maxis into the broader scientific and industry simulation communities.¹⁰⁸ Janeway’s interest in and orientation toward simulation, as well as a shared history in system dynamics, facilitated contact between Maxis and WP—and beyond.

As Kressel saw it, Wright had developed a “totally new genre of entertainment” for a market that would surely grow alongside the proliferation of inexpensive computers.¹⁰⁹ A solid-state physicist whose extraordinary career had touched practically every aspect of the postwar computer boom—silicon transistors, integrated circuits, computer-aided design, lasers, solar cells, and fiber optics—Kressel intimately understood the appeal of software. His extraordinary rise from orphaned Holocaust refugee to vice president at RCA Laboratories was underwritten by education: Yeshiva College, Harvard (MS), and the University of Pennsylvania (PhD, MBA). For him, Maxis’s value lay in its enriching, creative, and strategic games, which appealed to kids and adults alike.¹¹⁰ But enterprise simulation, as he saw it, was tantamount to business consulting—a very different proposition from selling packaged software.¹¹¹

In June 1992, WP invested $10 million for a 30 percent stake and a seat on Maxis’s newly established board of directors.¹¹² For WP, it was a small investment, but an opportunity to explore a new market space.¹¹³ Braun was excited not just by the prospect of taking Maxis public, but by Janeway’s sweeping vision of simulation as far bigger than games.¹¹⁴ The new board encouraged Braun’s scheme to break away from Brøderbund,¹¹⁵ and WP encouraged Maxis to improve its cash flow by tapping new markets such as education and enterprise. WP also pushed for more professional management: Braun should get out of operations and focus on strategy.¹¹⁶ To that end, WP helped Maxis recruit a sales director, Sam Poole, from Disney Software. (Marketing had been limited to customer service, sporadic newsletters, and some direct mail and magazine ads.) Poole, who felt micromanaged at Disney, hoped to spread his wings at a small company with good cash flow. He saw an uphill challenge: despite rising sales, the game industry was growing so fast that Maxis’s overall market share was declining. A seasoned executive with an MBA and experience in videogames, mass-market entertainment, and enterprise (Procter & Gamble, Polaroid), Poole was a dazzling hire whose resumé alone helped position Maxis for an initial public offering.¹¹⁷

As part of this transformation, Maxis absorbed Delta Logic’s staff as the Maxis Business Simulations division. In addition to SimRefinery, they developed SimEnvironment for toxic waste site management (for an EPA contractor); SimSite to plan military base closures; SimPower and SimEnergy for energy utilities; SimHealth for the American health care policy debate; and TeleSim, a policy advocacy piece for some regional telephone companies. Business simulations, Braun predicted, might be bigger than games.¹¹⁸ From SFI’s perspective, Maxis brought their research to new markets: education, consumer, business, and policy. As SFI cultivated their own donor Business Network, they saw the value of their Maxis affiliation go up.¹¹⁹

Most Maxis business simulations, however, remained stalled in the pitch or prototype phases.¹²⁰ And despite Braun’s belief that simulations might allow “groups to collaboratively decide what would be in their interest,”¹²¹ these projects were either for training or advocacy, not open-ended debate. Simulations surely had that potential, but that would have required a radically different approach, something along the lines of Doreen Gehry Nelson or Richard Duke.¹²² From a cash flow standpoint, the division that developed these business titles made little sense. Maxis’s technology, let alone products, was not easily adapted for the enterprise market; that was a mirage.

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In 1991, Jenny Martin, a photographer turned game artist, joined Maxis as its first full-time artist. Beginning with her friend and fellow Epyx alumna Suzie Greene, who also loved SimCity, Martin began building Maxis’s (mostly female) art department. “It wasn’t easy to find games that women liked,” says Martin, who had also worked at Bally Midway and on Beyond Dark Castle. She felt lucky, at Maxis, to be working on games for everyone. At first, Martin’s team polished and finalized art for ports and in-development titles like SimAnt, which was then on the cusp of release. Starting with SimLife, they were involved from the start. During McCormick and Wright’s initial Dollhouse investigations, Martin photographed dollhouse furniture, and her team did art tests to explore Dollhouse’s visual possibilities. She and Greene encouraged Wright to emphasize people and their personalities.¹²³

Wright preferred to work from home, but when he did come in, he sat with the artists. “Probably because we were fun,” Martin told me. “And I think he’s an artist.” Plus, “his wife was an artist.”¹²⁴ Having outgrown its Moraga offices, Maxis moved into Orinda’s recently redeveloped Theatre Square, where company meetings were held in its iconic Streamline Moderne movie theater.¹²⁵ Everyone at Maxis, Martin recalls, was about the same age and became friends. The “Drinking SIG” gathered at Casa Orinda, a Western-themed watering hole, where El-Fish’s Russian developers would cut loose and drink everyone under the table. Maxis staff picnicked at Lafayette Reservoir, canoed the Russian River, skied Lake Tahoe, camped, fielded a softball team sporting SimSox shirts, and competed in Sea-Monkey husbandry. Braun maintained two on-site pinball machines and, in an attempt to promote healthy living, invited speakers on topics such as fasting and trucked in carrot juice by the case.¹²⁶

Maxis also hired, in 1991, a vice president of marketing, Robin Harper, from a blue-chip advertising agency, where she had worked on strategy and market research for Nintendo’s SimCity. Harper oversaw a slick rebranding effort. According to Braun, now that Maxis was more established, its packaging could afford to be less strange. Harper explains that they kept the ball—“a toy with no goals where you make your own gameplay”—from Maxis’s logo and added playful color to the letters.¹²⁷ Abstracted into a moon-like crescent, the ball’s rim lighting now also intimated the mystique and grandeur of a world.

Maxis cultivated a quirky sense of humor. The llama became the company mascot after Wright put it to a vote—a llama with a jetpack versus a tapeworm—immediately following dinner at the company Christmas party in Tilden Park’s Brazilian Room. If you called Maxis and listened to the list of phone tree options and then waited, you’d get an option to order decomposing meat products. Choose that, and you’d be launched into a Choose Your Own Adventure–style story, complete with high-quality sound effects, in which you serve tainted meat at a party and the guests become ill and threaten to sue. Eventually you could choose to speak to an attorney or flee the country, which would route you to Maxis’s legal department or travel agent—who weren’t so amused.¹²⁸

Wright had never wanted to make another SimCity game, but after the lackluster commercial reception of SimEarth, SimAnt, and SimLife, Braun implored Wright to do a sequel to SimCity, or Maxis would fold.¹²⁹ Wright had been focused on the Dollhouse prototypes for about six months before becoming “dragged into,”¹³⁰ as he put it, SimCity’s sequel. Haslam, SimEarth’s coauthor, had been working on the project for about a year and half, but wasn’t making headway as fast as had been hoped. Development was complicated by a decision to use a 3D perspective, as was becoming common in computer games, as opposed to SimCity’s original overhead view. Wright took over the project, bringing some of Dollhouse with him, including its shell code and graphics programmer, Mick Foley. (This explains why SimCity’s sequel contains code called “DrawHouse” and “DrawYard.”) Martin’s art team had to recreate their assets, yet again, to match the new graphics system and perspective, which was, by all accounts, an improvement.¹³¹ Completing the project required an unfamiliar level of operational discipline; the resulting friction alienated some employees, who then quit.¹³²

[picture]

SimCity 2000 (1993).

The final product, SimCity 2000, was released in 1993 to massive critical acclaim and commercial success, underlining how the SimCity franchise underpinned Maxis’s financial health. Braun fondly describes SimCity 2000 as Maxis’s “crowning achievement.” The rest, he says, “was like a train wreck.”¹³³

Act 2: The Studio Strategy

The computer and game industries were rapidly changing. Looking back, Wright compared the CD-ROM to a meteor hitting Earth—an extinction-level event heralding profound changes. CD-ROM titles emphasized data-heavy multimedia, not the algorithmically rich systems he loved.¹³⁴ Computers became faster and cheaper, and consumers developed a taste for multimedia, 3D graphics, and the Internet. It was like a meteor shower, with the competitive environment transformed over and over. The days of a single developer producing a hit were gone. Budgets ballooned. Development now required teams of experts proficient in various subspecialties of programming, design, art, marketing, sales, and more. As competitors streamed into an already crowded field, the odds of a hit diminished and product shelf-life shrank. Risk escalated.¹³⁵

[picture]

Braun and Wright (1994).

To survive these shocks, Maxis embarked on a “studio-like strategy.” Like a book or music publisher, a diverse stream of products would sustain them.¹³⁶ This required a continuous influx of investment and increasingly scarce talent. Maxis developed, re-released, and published a bewildering array of titles—like Print Artist (1993), Full Tilt! Pinball (1995), and SimGolf (1996)—and partnered with, and purchased, companies whose relevance to Maxis’s core business—whatever that was—remained unclear. Poole, who became general manager in late 1993 and then president and board member, was seen by many as the driving force behind this shovelware approach. He “had no shame,” says Braun (who remained CEO).¹³⁷

As WP advised, Maxis leaned into the education market. To meet teacher interest, they developed guides and enlisted the help of Doreen Nelson.¹³⁸ While there were many educational software developers, according to the New York Times, Maxis was different. “It deals with principles,” Nelson told the Times. Despite being screen-based experiences—nothing like her multiperson embodied simulations—Maxis titles, she believed, promoted abstract reasoning and critical thinking.¹³⁹ Maxis cosponsored a Future Cities design contest, and in 1993 the winners met President Clinton at the White House and presented him with a model of the winning city.¹⁴⁰ SimAnt and SimCity 2000 were exhibited in museums.¹⁴¹ For Braun, play was curiosity-driven learning in a safe environment; Maxis titles afforded “learning by doing” and “interdisciplinary studies.”¹⁴² But by the mid-1990s, Maxis changed tack, and Claire Curtin, who had been hired from Brøderbund in 1992 to tailor SimCity, SimEarth, and SimAnt for the education market, was moved onto kid-oriented projects like SimTown.¹⁴³

Educational resonances deepened Maxis’s academic liaisons, but such flirtations, despite feeding Wright’s curiosity and influencing his work, never directly blossomed into new products. Academic relations tended toward the theoretical, as scholars, critics, and policy wonks began poring over Maxis titles (following the release of SimCity 2000),¹⁴⁴ and the practical, as when computer scientists began attending to computer games as objects of earnest inquiry—all of which traded on the growing sophistication and prestige of (and market for) games. As a result, Wright developed relationships with academics at SFI, Stanford, Northwestern, and elsewhere.¹⁴⁵

Maxis tried to be many things to many people. But who were they? What was their focus? They galloped ahead but floundered about. In the grip of an identity crisis, Maxis stumbled through an increasingly turbulent industry. They would need to focus, but how, and on what? In January 1994, just before the launch of SimHealth, they spun Maxis Business Solutions back out into an independent company. “We were spending more time negotiating contracts with these clients than developing the software,” said Wright.¹⁴⁶ Janeway’s ability to recruit enterprise customers notwithstanding, business simulations were clearly not a viable strategy, and the science advisory board was dissolved. According to Braun, as Maxis abandoned this line of work Janeway seemed to lose interest in Maxis, and his partner, Kressel, became more involved.¹⁴⁷ Although Janeway, who had championed the enterprise simulation business, was busy with other, more promising enterprise software investments, Maxis, which would later prove to be their sole consumer technology investment,¹⁴⁸ nonetheless continued to receive the careful attention WP was known for.¹⁴⁹ Like clockwork, a Janeway staffer phoned Maxis’s CFO, Fred Gerson, every day. Gerson, in turn, hounded Wright for new products.¹⁵⁰

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Where was Wright’s project, his dream of a simulated dollhouse? His batting average, for those keeping score, kept dropping. Sales of SimEarth were worse than those of SimCity, and SimAnt was worse than that. Although SimCity 2000 was on track to outperform the original SimCity, it was a sequel, not a new concept. Wright seemed to be a one-hit wonder. Projects were expensive, and a new Wright title—given the trend—would likely fare worse than SimAnt or even SimLife. Doubt and hostility to Dollhouse was pervasive, from rank-and-file staff to the board of directors.¹⁵¹ In 1993, marketing focus-tested the concept with teenage boys and found that they hated it, which didn’t help matters.¹⁵² Braun describes the skepticism: “A dollhouse? That’s for little girls.” And little girls, of course, don’t buy computer games. Some found Wright’s idea so absurd that they chalked it up to his well-deserved reputation for prankishness, like his “Willvis” Elvis impersonation, or a Magic 8 Ball jukebox he had made that answered any question in the form of an Elvis song.¹⁵³ Even Wright harbored doubts that “a doll house for adults may not be very marketable.”¹⁵⁴ Maybe SimCity was it. Or maybe, just maybe, SimEarth and SimAnt were, like Raid, City Planner, and Probot, merely waypoints on an arduous multiyear creative trek. Even if SimAnt was a commercial failure, who says failure is bad? Failure can be fun; it’s how we learn. In SimAnt, Wright had glimpsed a robust simulation concept for human behavior.

The creatively permissive atmosphere that SimCity had made possible, and which had enabled SimEarth and SimAnt to be made, was thinning out. As Maxis geared up for an IPO, its appetite for risk weakened. According to Joe Scirica, Maxis’s VP of product development, Wright was unable to articulate a “real product plan” for Dollhouse, which hamstrung efforts to secure a meaningful budget.¹⁵⁵ With venture capitalists on board, an IPO on the horizon, and rising development costs, the free-wheeling era of SimEarth was over.

Wright’s muse seemed to be separating from Maxis’s business needs. Or maybe Maxis lacked the patience to keep following him and was squandering its energies. It was hard to tell. The SimCity network, in any event, was beginning to come unglued. Employees openly spoke of “the Will problem.”¹⁵⁶ But perhaps the problem was that nobody believed in him anymore.

§§§

Despite widespread opposition, Wright managed to secure a young Maxis programmer, Jamie Doornbos, for the Dollhouse project in 1995. Doornbos sensed that the minimal staffing might be a cynical ploy to address “the Will problem” by diverting Wright’s attention from matters deemed more important to Maxis’s survival rather than an earnest investment in the project.¹⁵⁷ Regardless, with Doornbos’s help, the project made important headway.

Goaded by the focus test, Wright began calling Dollhouse by other names, such as “Tactical Domestic Simulator,” or “TDS,”¹⁵⁸ and “Project X.” (Wright also had Project Y, which became SimCopter, and Project Z, an abandoned game about the Hindenburg.)¹⁵⁹ Alternate names weren’t just to shield Dollhouse from reproach; they were earnest attempts “to orient people to the right perspective on it,” says Wright.¹⁶⁰ Project X, he explained, would be something like: Groundhog Day, a 1993 comedy in which Bill Murray’s character relives the same day; Little Computer People, an offbeat and irreverent 1985 title from Activision’s “anthropological research” efforts that connected players with the tiny people that inhabited computers via a “house on a disk.”¹⁶¹; and, later, Tamagotchi (1996), a keychain digital pet whose simplicity belied its potent emotional allure.¹⁶²

Player creativity would be central. In 1994, Wright explained that Project X would have a “strong simulation component” while staying “focused on the idea of you [the player] as a designer.” Wright thought in terms of dollhouses and train sets, “media through which people can exercise their design talents.” As hobbies, they invite diverse interests and play styles.¹⁶³ The lively modding scene that had quickly grown around Doom (1993), which encouraged player modification, attested to the possibilities. But Wright wanted to go further and give players sophisticated design tools. He immersed himself in design theory and studied A Pattern Language’s comprehensive architectural grammar.¹⁶⁴

Doornbos wasn’t sure if Project X would be fun, but he loved working for Wright. Every couple of weeks or so, Wright would appear, sometimes brandishing floppy disks with his own prototypes. These included the prior prototype with the marionette people, as well as a program that generated voxel buildings with genetic algorithms. Doornbos was always excited to see Wright, a benevolent father figure, and show him his latest demos. Working together, they created the heart and soul of what became The Sims.¹⁶⁵

Wright crafted a simulation of a person’s needs. A simple prototype showed a person’s hunger, bladder, and other needs evolve over time. If the person peed in their pants or fell asleep, a dialog box popped up. Doornbos incorporated this “motive engine,” a concise set of equations and coefficients, as well as ideas, assets, and code snippets from the marionette prototype, into a new Project X prototype focused on imbuing the people and things of this dollhouse world with behaviors.¹⁶⁶

“SimAntics,” as the behavior language came to be known, went through many iterations, but from the start it had a graphical editor inspired by the diagrams Wright drew to explain how people would do things like go to the bathroom, brush their teeth, and make breakfast. These behavior scripts, which were compiled into a compact bytecode, told people and objects how to do things like select activities, walk around, animate, and, in the case of insensate objects like appliances, break. As in SimAnt, intelligence emerged from the coupling between agent and environment. Objects broadcast advertisements that claimed to service a person’s changing motives. A toilet, for instance, would advertise its ability to relieve the bladder motive.¹⁶⁷ Crucially, the custom language scaffolded the design conversation between Doornbos and Wright, who could discuss its limitations.¹⁶⁸ As a result, SimAntics was highly extensible; new objects could easily plug into Project X’s dollhouse world and describe both why and how a person might use them.

As with SimCity, Wright fused many different design and simulation approaches. A system dynamics–like model represented physiological and psychological quantities (rather than socioeconomic ones). An agent-based model, like SimAnt, linked people to their social and architectural context—their homes, which players would author using a sophisticated and accessible design tool.¹⁶⁹

§§§

Financially, Maxis was a rising star. Buoyed by SimCity 2000, revenue and profit kept climbing, and Maxis made preparations to go public. Not only was SimCity 2000 a hit, but the divorce from Brøderbund seemed to be going well. Maxis kept more of the revenue for itself, built up its own sales and distribution network, and now published games for other companies.¹⁷⁰

On May 25, 1995, Maxis became a publicly traded company.¹⁷¹ The decision to go public “was just sort of the thing to do, and it just felt right,” says Braun. In this, he was not just fulfilling his dream of an IPO—and emulating Brøderbund, The Learning Company,¹⁷² and MECC—but riding a tsunami of capital then surging into Bay Area technology firms. Concomitantly, newly empowered technologists redefined the rules of capital—acquiring more and more of it on increasingly inexpensive terms. Maxis had hitched itself to a speculative bubble. Up the roller coaster they went, alongside companies such as Netscape, whose August 1995 IPO led to a multibillion-dollar valuation—not bad for a company with zero profit.¹⁷³ Analysts were bullish on computer game stocks; sales of CD-ROM titles the previous Christmas had been fantastic. According to one analyst, Maxis was “highly profitable and growing rapidly,” so an IPO made sense.¹⁷⁴

Braun now identifies the decision to take Maxis public, which he made by himself, as “a big mistake.” “It was me. Greed, you know, that was the path back then. I regret it now.”¹⁷⁵ The venture capitalists had advised Braun, the CEO, that now was the time to go public (and restore their liquidity). Braun agreed, but voiced his doubts: Maxis lacked the products to go public and make its numbers. Every management team, Kressel observes, harbors reservations about going public, and Maxis was no different. Gerson, who led the meetings, was optimistic; he wouldn’t have taken the chief financial officer job if Maxis wasn’t ready.¹⁷⁶

In December of that heady year, Newsweek printed “The Net 50.” Netscape’s Marc Andreessen was already famous, but who was up and coming? “Look for Braun to start moving his world of fantastic simulations to the Internet sometime next year.”¹⁷⁷ But Maxis, like Netscape, wasn’t in any shape for an IPO.¹⁷⁸ It was like pumping jet fuel into a model rocket. Something exciting was bound to happen. From the perspective of financial capitalism, the timing was pitch perfect—an explosion of money. From the perspective of running a software business, it augured an explosion of a different sort.

Act 3: Unraveling

According to Wright, being a publicly traded company “drives a lot of dysfunctional decision-making,”¹⁷⁹ and “it was just obvious that we were driven towards short-term thinking.” Products like SimCopter were rushed out the door.¹⁸⁰ Braun describes Maxis as losing its focus. In order to go public, they had tried “to build up as much product as possible” and invested in many ill-advised experiments, but should have “just stuck with what worked.” “I had the golden goose and I’m looking for other golden gooses.”

One symptom of Maxis’s lack of focus was an inability to get behind Project X. According to Braun, he had always believed that it “would be the big one” and regrets not focusing on it.¹⁸¹ But The Sims was a bold and experimental idea that required a huge research investment and a massive amount of labor to produce the sounds, art, animation, and objects it needed. But most damningly, it was met with skepticism and ambivalence within Maxis.

By the mid-1990s, the industry Maxis found itself in was worlds away from the one in which Wright had begun a decade earlier. Retail outlets that sold computer software had rapidly consolidated, which meant that a profusion of titles jostled for dwindling shelf space. In response, retailers stocked sure-fire hits, but Maxis had bet on many nonpremium titles. Retailers weren’t interested, and so Maxis turned to Internet and direct-mail sales—an unproven strategy.

Meanwhile, computers had become more powerful, and games, as a result, became more sophisticated and costly to develop. Wright had chosen the C64 in 1982 because it was a new platform on which he might eke out a competitive advantage. But an unrelenting meteor storm of technologies now crashed down—CD-ROM, more RAM, faster processors, more detailed graphics, networking, sound—and developers struggled to keep pace. Consumers, retailers, and platform holders favored increasingly elaborate games with unprecedented development costs. Not only were the vegetables rotting faster than ever, but they cost more to grow than ever before. As a result, software developers and retailers sought to mitigate risk.¹⁸² Experimental and ambitious titles—exactly the sorts of titles Wright wanted to make—did not seem like a viable business strategy.

Maxis was swimming upstream. Separating from Brøderbund meant becoming a bigger fish that might gobble up smaller ones. It was a risk with a huge potential upside: becoming a dominant player. But they might starve, be swept downstream, or swallowed whole.

[picture]

Wright’s box design for what became The Sims.

Some companies, like Electronic Arts, had figured out how to thrive in this environment. Operational discipline and brand management were key.¹⁸³ EA licensed popular intellectual properties, often from sports, and methodically cranked out titles incrementally different from one another. Licensing wasn’t cheap but provided certainty and stability. Maxis went after an abundance of low-hanging fruit, publishing games about golf, baseball, trucks, and pinball. It made questionable acquisitions and inked a deal with Ringling Bros. to produce a “SimCircus.” It also embarked on costly and ill-advised technological misadventures, like a SimCity with online play and one with 3D graphics, SimCity 3000.184 This required larger teams, longer development cycles, and scarce technological expertise.

To help surmount the spiraling technical challenges, Maxis established, in 1995, a Core Technology Group (CTG) across the bay in San Mateo. Braun tapped Jim Mackraz, a veteran of Amiga’s operating system development team, to direct the effort. CTG’s mission was multifold. Its location would help recruit Silicon Valley talent that was more engineer than game hacker, and it would cultivate tools and expertise that enabled Maxis’s product teams to stay technically competitive. To this end, CTG researched cutting-edge technologies like 3D animation and artificial intelligence.¹⁸⁵

A key focus was “SimWorld,” a modular simulation architecture that would allow Maxis titles to interconnect, the selling of content packs (as in a hobby shop), and eventually a development kit for outside developers—and even players, who would create and share custom content. Players would be able to walk, drive, or fly through their cities. All this would be powered by an object-oriented system that could also simulate the inside of a home, allowing players to zoom in and rearrange and play with components like people and furniture. Project X, Wright’s dollhouse project, would thus interlink with SimCity. As with Jay Forrester’s system dynamics, Doreen Nelson’s classroom cities, and the Santa Fe Institute’s complexity science, SimWorld—Maxis’s most ambitious undertaking—would interlink all disciplines, bridge separate communities, and foster new ones.

To advance SimWorld, CTG built a prototype SimCity in which new building types with novel behaviors could be dynamically loaded.¹⁸⁶ SimCopter (1996), which enabled players to fly through their SimCity 2000 cities, illustrated the potential. Not only could players engage with their creations in new ways, but technical investments could be repurposed: SimCopter’s citizens were rendered using CTG’s 3D character animation system and were simulated with SimAntics, the behavior system Doornbos had created for Project X.¹⁸⁷ The bricolage was signature Wright.

[picture]

Dollhouse would enable players to zoom into their SimCity cities, design architecture, and play with simulated people. “We’re looking at actually having a little family model inside of here.” Wright demonstrated this Dollhouse prototype at Terry Winograd’s Stanford seminar on April 26, 1996.

SimWorld was an audacious stratagem, more operating system than stand-alone title. But a modular system would allow content packs—far more sophisticated than the art packs that simply reskinned SimCity—to be easily developed and sold, steadying the bottom line between major releases. And Maxis would be less dependent on Wright, who hated working on sequels. In truth, operating systems are sophisticated and costly enterprises, and Maxis was now investing in two entwined OS-like initiatives: Project X and SimWorld.¹⁸⁸

§§§

Sometime near the end of 1995, while Maxis’s performance and prospects still looked rosy, the company undertook a secondary stock offering to raise capital, extend its runway, and allow for some liquidation. By all accounts, Morgan Stanley, which handled the offering, did a fantastic job. Janeway and Kressel, however, wanted a better price; Gerson recollects it was over a small amount—under a point. Bubbling hot Silicon Valley valuations couldn’t have helped. WP, which Gerson says was used to having their way regardless of their stake, now owned 43.2 percent of Maxis. Flexing their muscle, WP pressed hard to kill the deal. When Poole pushed back, Janeway grew “angry.”¹⁸⁹ To the perplexity of multiple observers, WP got its way, and Maxis’s board rejected the deal. Braun felt undermined. (Janeway and Kressel declined to comment on Maxis’s secondary offering.) While avarice is no shortcoming for a VC, greed must have turned to fear as Maxis’s stock price crumbled over the next year to a fraction of the spurned price.¹⁹⁰

[picture]

Maxis Inc. share price.

Braun had been warned that games, like vegetables, go bad. Before going public, he crowed that he was “king of the mountain.”¹⁹¹ But now, Braun was king of the vegetables. Perched atop a hill of them, revenue was declining, expenses were ballooning, and Maxis was in the red. Head count had grown by nearly 50 percent over the past year, and Maxis headquarters relocated to plum new offices—the sixth floor of a contemporary glass-wrapped high-rise near downtown Walnut Creek.¹⁹² The vegetables were costlier than ever to grow, fewer sold, and their shelf life was shortening. The whole heap was rotting from underneath. And Maxis pumped out dubious products, worsening matters. Meanwhile, news of SimCity 3000’s development woes depressed Maxis’s stock price.¹⁹³

As Maxis’s finances unraveled, the San Mateo office fell under scrutiny. CTG’s work wasn’t readily taken up by the product teams that actually made money, so Mackraz was given an ultimatum: either work on SimCircus or Wright’s pet project, which, he was told, would be killed. Recalling his own skepticism over an early SimCity prototype, Mackraz chose Project X: “You don’t bet against Da Vinci twice.”¹⁹⁴ From Wright’s perspective, the marriage made perfect sense. Maxis was about to pull the plug on CTG, and he needed staff for Project X. And so, Wright made a play for CTG and its handful of top-notch programmers.¹⁹⁵ CTG, moreover, wasn’t “infected” with the negativity toward Project X that pervaded the home office, says Braun.¹⁹⁶ Wright and Doornbos commuted down to San Mateo, the new hub of the Dollhouse skunkworks. One of CTG’s first projects was upgrading the character animation from sprite-based axonometric to 3D. Sure enough, Maxis cancelled Project X. Undeterred, Mackraz revived the project with a new sobriquet: “Jefferson,” as in “Life, Liberty, and the Pursuit of Happiness”—a new gloss on the dollhouse concept and a rallying cry for the embattled team.¹⁹⁷ With the aid of funds siphoned from some cancelled projects, Martin and Greene, who had left Maxis, were hired to produce 3D characters inspired by well-known sitcom personalities like Edith and Archie.¹⁹⁸

Having labored to create an environment in which Wright would thrive, Braun had inadvertently undone his work by taking Maxis public. According to Braun, “it was that conflict between the stress of this business and the investors that come in from that public process, and Will and his creative thing.”¹⁹⁹ The pressure of being a public company caused things at Maxis to go increasingly haywire. Braun lasted as CEO until August, when Maxis posted a quarterly loss, and was replaced by Poole.²⁰⁰ Poole, who had never clicked with Maxis’s senior management,²⁰¹ scrambled for solutions during his own brief tenure as CEO.²⁰²

Maxis’s financial health had always been underpinned by the SimCity franchise. To help make their numbers, Maxis had, in early 1996, accelerated development of SimCity 3000 (and other titles). When Scirica finally announced that fall what everyone knew—that the long-troubled project, which Wright had kept at arm’s length, wouldn’t make its aggressive ship date—Poole responded by firing him and, without missing a beat, began talks with corporate suitors.²⁰³ The jig was up.

Maxis entertained the possibility of merging with another midsized publisher. Ultimately they decided to sell the firm to a larger company and held talks with Infogrames, Electronic Arts, and Activision. In summer 1997, Maxis was purchased by Electronic Arts (EA) in a stock exchange worth $125 million. The game industry was consolidating, and EA saw the value of Maxis’s talent, its well-regarded SimCity franchise, and its “Sim” brand. Maxis would help EA develop its PC market share, make inroads in online gaming, and lessen its dependence on the cyclical console market (and its imperious platform owners).²⁰⁴ In the run-up to the acquisition, Don Mattrick, head of EA’s North American Studios, visited the San Mateo office with Wright as part of due diligence, for a demo of Jefferson. There were people, architectural features, and a few sample objects. Mattrick, who had a competing project underway in Canada, was enthusiastic.²⁰⁵

According to Luc Barthelet, who became Maxis’s general manager, Maxis had tried to do everything EA was doing but without the requisite “focus or infrastructure.” Too many people were pursuing “their own little projects and didn’t care if they would sell. Those people left.” Maxis’s culture of self-directed creativity was reined in and yoked to commercial outcomes. Product development was rationalized and disciplined.²⁰⁶ No longer at the center, as Wright put it, “of this small public company that was struggling to survive,”²⁰⁷ he finally received the leeway to pursue The Sims—a title hiding in plain sight, within Bremer’s original SimCity manual—in earnest.²⁰⁸

Dollhouse had, once again, found a new home. As EA cancelled straggling Maxis projects, key staff were transferred onto The Sims, such as Claire Curtin and Roxy Wolosenko, who became Wright’s codesigners.²⁰⁹ And it underwent a remarkable transformation. All along it had been about architecture, but it began to be about the people.²¹⁰ From practically the beginning, Wright’s female collaborators, like Martin, had been pushing him in that direction, to make Dollhouse about its inhabitants.²¹¹ His daughter, Cassidy, who had played with the prototypes, helped him appreciate Dollhouse’s social and creative dimensions.²¹² Wright’s original vision concentrated on efficient house design and time management; simulated people scored the player’s architectural handiwork²¹³—he had compared them to the pinballs in Pinball Construction Set.²¹⁴

But now, with EA’s encouragement, The Sims blossomed into something new. Eros appeared. A sexy TV star became the inspiration for an iconic new character, and Dollhouse’s slovenly characters became more well rounded, stylish, and sensual.²¹⁵ Relationships became important. As with SimCity, functional systems layered atop one another, and something new emerged. People came to the fore.²¹⁶ The embodied cognition Wright took from SimAnt swelled and overflowed into fuller and richer kinds of embodiment: families, relationships, desire, personalities, sexuality, attractive and homely bodies. In the end, it was a passionate kiss between two simulated women on the testosterone-packed 1999 E3 show floor that helped rescue The Sims from oblivion.²¹⁷ This demo, moreover, arrived at a moment of spiraling concern over violent entertainment—games like Doom were linked to a tragic spate of US school shootings, and the ensuing moral panic probably helped create an environment more receptive to The Sims.²¹⁸

EA backed The Sims (2000), if not with the fervor its eventual blockbuster success justified, then enough for the project to come to fruition. Even so, Wright’s role—and the project itself—remained embattled; some EA managers introduced Hollywood scriptwriters, sought to throw him off the project, and even scuttle it.²¹⁹ But in the end, Wright’s prediction was proven correct: The Sims became a sleeper hit—and a lucrative one at that. As of 2020, the franchise has earned EA over $5 billion.²²⁰

§§§

Looking back, Wright and his collaborators had built SimCity out of diverse materials familiar to a wide range of communities. SimCity, as a result, seeded a space for different communities to come together and imagine new possibilities. This SimCity “network forum”²²¹ brought together scientists, business managers, videogame makers, venture capitalists, educators, and policymakers. The pliant contact languages of simulation, computing, and play underwrote these exchanges, as did SimCity itself. Functioning as an evocative prototype, SimCity demonstrated that a computer program could simultaneously be entertaining, accessible, educational, and scientific.

The SimCity network was seeded by Wright’s bricolage and nourished by Braun, who built a small team around Wright and his City Planner prototype. As a result of SimCity’s reception, Wright came to see himself as a science popularizer, as did other members of the growing SimCity network. As Chevron and other enterprise firms approached Maxis for bespoke training and advocacy simulations, venture capitalists at Warburg, Pincus saw an investment opportunity and granted Maxis more cash, prestige, and social connections. The Santa Fe Institute, seeking to expand the reach of its own network, cultivated and celebrated its relationship with Wright and Maxis, which used SFI’s research to create products for the consumer, education, and business markets—an affiliation that elevated the prestige and reach of both organizations. SimCity’s liminality also attracted the interest of Nintendo, which saw a sure-fire hit that would help it make inroads with new audiences, further extending Maxis’s reach. SimCity’s liminality also helped MIT explain its own relationship with Nintendo. And Wright’s long-standing research affiliations imbued Maxis with a halo of leading-edge technology that persisted into its merger with Electronic Arts.²²²

These remarkable associations were underwritten by Wright’s far-reaching bricolage, which provided Maxis with a rich set of resources for finding common ground with diverse computing communities. These resources included cybernetics, system dynamics, games, personal computers, graphical user interfaces, cellular automata, and artificial life. Remarkably, many of these contact languages were then in their heyday, which further propelled the growth of the SimCity network. Incredibly, Maxis found itself at the vanguard of videogames, complexity science, and Silicon Valley.

Although waggish, Wright’s honorary doctorates from both Nintendo (“Dr. Wright”) and the Santa Fe Institute (a recurring misprint) reveal a sincere “legitimacy exchange.”²²³ By enabling experts from different fields to bestow their legitimacy on one another, the SimCity network elevated the status of its participants and produced Wright’s “network celebrity.”²²⁴ The transformation of Wright, a college dropout, into “Dr. Wright” points to another product of the SimCity network: Wright’s ongoing education. Brought into close contact with preeminent leaders in a variety of fields, Wright, an inquisitive and self-directed learner, effectively apprenticed himself to elite videogame designers and computer scientists.²²⁵ This helps explain how Wright, who already had a knack for tracking leading-edge developments in computing, was able to stay at its forefront.

The SimCity network’s most visible products are numerous software titles and their publisher, Maxis, which developed many of them. Some of these titles, like SimEarth, SimAnt, SimLife, and The Sims, are every bit as marvelous and strange as SimCity, and, it’s safe to say, wouldn’t exist without the SimCity network. Less visible is the wealth created by the SimCity network. Maxis’s sale to Electronic Arts for $125 million of EA stock—whose value would soon multiply—surely made Braun and Wright wealthy (they owned, at the time of the merger, 28.5 percent, and 11.5 percent of Maxis, respectively).

The least tangible product of the SimCity network is how it transformed people’s conception of simulation, their relationship to computing, and, most fundamentally, their understanding of the world. This conceptual revamping wasn’t limited to cities, planets, people, or even ants. These worldly phenomena, transported into a computer simulation, became algorithms and information. The SimCity network vivified the “erasure of embodiment”²²⁶ at the ideological heart of computing—that the universe really is, deep down, nothing but informational patterns and feedback loops. Maxis titles also brought home a closely allied conception of the universe as a self-organizing system; that the interaction of simple entities—buildings, ants, genomes, motivations, people—gives rise to sophisticated order.²²⁷ Maxis transformed these high-flown ideas into popular amusements and educational toys, disseminating them in conjunction, and often in direct partnership with, actors performing similar work at SFI, GBN, Wired, and the broader Whole Earth network.²²⁸

If, as Erik Baker and Fred Turner convincingly argue, SFI and the Whole Earth network helped architect the neoliberal “new economy” of the 1990s—networked, self-organizing, deregulated, high tech, the privileging of information over matter—in partnership with a resurgent conservative moment, then Maxis played an important role in that transformation, disseminating some of its core ideas to mass audiences.²²⁹ On this account, Maxis helped build the “new economy” that enriched its backers.

Situated at the crossroads of the SimCity network—a nexus of financial capitalism, pop culture, public policy, high science, business, and computer technology—Maxis eventually lost its way. The liminal quality of SimCity infused Maxis itself, which struggled to find its identity within a rapidly changing and increasingly competitive entertainment industry. In the end, Maxis proved unable to withstand the undertows and rip currents of rapid technological change, surges of capital, and a swiftly evolving game industry. SimCity’s mystique, its multivalent appeal, was both a magical gift and a curse. Maybe Brøderbund was right: disturbing established categories is a risky business.

In hindsight, Maxis was struggling to contain a taut web of contradictions. Bruno Latour argues that technologists, for their labors to bear fruit, must reconcile a network of tensions between social forces (which includes, in his account, nonhuman actors like technological artifacts).²³⁰ The SimCity network’s extensive reach brought many such tensions. Wright’s years-long research and experimentation was at odds with an increasingly hit-driven business and, as Janeway notes, a timeline that public investors could live with.²³¹ Over time, Wright’s self-directed learning and creativity seemed to unglue itself from the play and pocketbooks of consumers. Braun’s ecumenical vision, which recognized and encouraged Wright’s talent, was in tension with his desire to create a publicly traded company.²³²

As in a fairy tale, Braun fulfilled his wish for a publicly traded company, but this had unforeseen consequences. As Maxis took on investment, its appetite for the kind of long-term risks that had made SimCity possible in the first place soured. The WP investment extended the reach of the SimCity network but also curtailed Wright’s autonomy and his ability to secure resources for The Sims. That Wright was expected to produce company-sustaining hits without any administrative authority points to the oddity of his Maxis identity; his business cards said “Llama Consultant,”²³³ but in SEC filings, he was Maxis’s “Chief Technical Designer.”²³⁴ Maxis Business Simulations also illustrates how investments can function as creative projects for venture capitalists, who may not even agree among themselves on a shared vision. When Janeway’s business simulation initiative was revealed, as Kressel had anticipated, to be a languishing consulting operation with little relevance to Maxis’s core operation, the division was spun out. In Wright’s analysis, the push to grow Maxis as a company caused things to snowball out of control. A small design studio might have survived but not a fast-growing company hungry for new products.²³⁵

In hindsight, the SimCity network was laying the foundation for The Sims, its most durable product. Far-reaching contacts in industry and academia accelerated Wright’s learning, setting the stage for The Sims’s cutting-edge AI, object architecture, and user interface. And the SimCity network’s ability to reach general audiences—beyond the narrow confines of teenage boys—paid huge dividends, as women took leadership roles within Maxis and made crucial contributions to the design of The Sims, a title that radically expanded the audience for computer games by upending gender expectations.

The long meandering path from SimAnt to The Sims captures the essence of Maxis’s trajectory, which combined creative permissiveness with financial conservatism. Maxis could have completed The Sims, Braun thinks, but lacked the necessary focus.²³⁶ The hugely ambitious project, however, could only have gestated in the permissive culture of Maxis, a place that was also, by then, too conservative and unfocused to back it to completion. Was this odyssey necessary? SimCity, another sui generis Wright title, required its own meander from Raid on Bungeling Bay and Wedit to City Planner, Micropolis, and SimCity. Wright’s career, by his own account, has been one of “serendipitous discovery.”²³⁷ The odyssey from ants and architecture to SimWorld and people enabled Wright, a master bricoleur, to try out and collect the ideas, collaborators, and techniques that became The Sims.

Even Wright’s disinterest in sequels, which frustrated the business tenets of game development, indirectly paid huge dividends. The modular design of The Sims allowed EA to cheaply produce expansion packs. This concept was born of the underlying simulation design but also Braun’s need to produce sequels without Wright, who could then pursue his latest dream.²³⁸

§§§

The rebuilt homes that sprouted up in the wake of the Oakland Hills firestorm, in their sleek modern construction, colorful contemporary designs, and exuberantly eclectic architectural stylings, stand apart from other Bay Area neighborhoods. They resemble nothing so much as a landscape of The Sims homes. The fire, in the end, unleashed a torrent of creative energies in both real estate and software. But as innumerable digital houses and families sprang from the imaginations of countless players, one important kind of creative play was, once again, withheld.

All along, Wright had wanted to share with players the joy of shaping the rules of the world. The Sims’s extensible architecture was cultivated, from early on, with an eye toward player modding—not merely “milking” the franchise with “expansion after expansion.”²³⁹ Not long after launch, he collaborated with Ken Forbus, a professor of computer science and education, on a game design course that put The Sims’s internal development tools in the hands of students. (Forbus explains this as a kind of trade: he documented the tools in exchange for using them.) Did Wright “secretly hope” that these tools would escape into the wild? “Maybe,” Forbus told me, but the students respected Wright too much to do that.²⁴⁰ While Wright “would have been happy for them to release it into the wild” and EA did release some tools, he learned, from Forbus’s class, that supporting them was simply too much work.²⁴¹ In any event, doing so would have risked upsetting the authorial balance of power—and, alongside it, the lucrative expansion pack business Braun had once dreamt of.

8

How SimCity Works

Tom West slipped into the building. Nobody suspected he didn’t belong. He just walked right in, strolled down the halls, and entered a windowless room where he found it: the machine he had been living in fear of. It was the VAX, and, to his surprise, a technician from DEC was still busy installing it. West was scared because this machine was the competition, and his team at Data General was vying with DEC, a much bigger company with a tremendous head start. He watched the technician work, and after he left, West closed the door to the room and proceeded to take apart the computer. Tracy Kidder recounts the episode in Soul of a New Machine:

The cabinet he opened contained the VAX’s Central Processing Unit, known as the CPU—the heart of the physical machine. In the VAX, twenty-seven printed-circuit boards, arranged like books on a shelf, made up this thing of things. West spent most of the rest of the morning pulling out boards; he’d examine each one, then put it back. . . . Etched into the boards, among the housings of the chips, run many silvery bands; they make patterns like the tracks in large railroad yards.

Some boards are colorful and most finished ones please the eye. A computer’s boards seem to show order triumphing in complexity. They look as if they make sense, but not in the way the moving parts of an engine make sense. The form on the surface of a board does not imply its function.

Looking into the VAX, West had imagined he saw a diagram of DEC’s corporate organization. He felt that VAX was too complicated. He did not like, for instance, the system by which various parts of the machine communicated with each other; for his taste, there was too much protocol involved. He decided that VAX embodied flaws in DEC’s corporate organization. The machine expressed that phenomenally successful company’s cautious, bureaucratic style.¹

Computer hardware can be taken apart and examined, but software, as historian Michael Mahoney points out, “is even trickier, because we can’t ‘pull the boards.’”² Software is harder because, unlike hardware, software isn’t hard. It’s software—intangible and elusive, suspended somewhere between logic, performance, and social practice.³

§§§

Peeling back the layers of SimCity’s illusion, this chapter tackles the clockwork logic within. The question of how exactly SimCity works is of interest to multiple audiences.

Studying a Will Wright masterwork is, of course, an exciting opportunity for those, like myself, who design and program simulations, games, and playful experiences. In examining SimCity’s development and influences, prior chapters established a foundation for this one, which analyzes SimCity’s simulation machinery and prepares us for the next: an examination of SimCity’s playful affordances and aesthetic effects.

Unraveling the threads that bind simulation logic to player experience does more than employ Building SimCity’s historical analyses; it underwrites an additional leg of this history. SimCity’s clockwork logic is a ledger of debts to multiple simulation traditions. This chapter surfaces these debts and examines how Wright reconciled their divergent worldviews. In this sense, SimCity is like any other piece of software; it exhibits the negotiations and aspirations of those who seek to cast computers in a desired image—one reflective of their own histories, agendas, and cultures.⁴

Extensive figures assist this chapter’s analysis. I call these reverse diagrams, as such figures are often used to guide software production, but in my case, they have been produced after the fact. For reasons explained more fully in the appendix, these figures often go into more detail than the prose narrative permits. This is, I hope, more feature than bug. While some might find these details superfluous and even perplexing, I believe that others will find them helpful and perhaps worth building on. In that vein, while this chapter engages in some critical analysis, a comprehensive critique of SimCity’s policy assumptions falls outside my scope. My hope is for interested critical theorists to elaborate on Building SimCity and the related literature to do exactly that.⁵ This chapter analyzes SimCity 1.4c for Macintosh and its associated source code.⁶

Our itinerary is as follows. We begin with the clockwork that orchestrates SimCity’s subsystems, which provides an overview of the whole ensemble. Zooming into SimCity’s landscape, we find that the roads, buildings, trees, fires, and much else are, in fact, mosaics. This tile map is simulated by processes that produce animations, simulate urban growth and decay, and represent pollution, crime, land value, and more. Local economic activity, it turns out, interlocks with a citywide demand model. Zooming out, we consider how SimCity’s top-down simulation (drawn from Jay Forrester’s work) regulates a spatial one (inspired by cellular automata). Next, we track the objects, like Godzilla, that roam SimCity and how this multi-agent simulation owes a debt to the videogaming tradition. We then look up and out, to the player, and examine how SimCity coaches play. Finally, we consider how divergences between simulation machinery and player belief underscore the vital role of imagination.

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SimCity system overview.

Orchestrating SimCity

SimCity sparkles with dazzling simultaneity. An earthquake might shake the city while Godzilla trashes it; a boat navigates a drawbridge while an airplane flies overhead; the helicopter approaches a traffic jam; meanwhile, fires burn and citizens move out. SimCity is a triumph of lifelike illusion and technical mastery—these bustling worlds unfold on meager computer hardware.

“The truly amazing part of SimCity,” says Wright, “is the comprehensive way everything pulls together and relates to other facets of the model.” “The hardest part” wasn’t “developing any particular model,” but “balancing the different models together. We had a crime model, traffic model, power model, economic model, population model, and so forth. The challenge was to juggle the resolution of each individual calculation so that they fit together correctly and were measured in a synchronized time pattern.”⁷ Orchestrating SimCity’s subsystems was the hard part.

SimCity’s feeling of simultaneity is actually an elaborate illusion. The computer can do only one thing at a time.⁸ It is by interleaving multiple tasks that the illusion of simultaneity streams forth.

This is like pointillism or a bitmap. Look closely, and you see many discrete points of color. Step back, and they merge into a figurative image—a landscape, portrait, or still life. But whereas adjacent pixels might convey an apple’s form rounding away from us in space, a simulation’s successive frames—like those of a film or animation—can produce the illusion of motion. By interleaving simulation processes, an illusion of simultaneity is produced. As in pointillist painting, pixel art, or needlepoint, this approach also produces textural effects; your eye (and hand) feels individual stitches or a tapestry’s warp and weft. Woven together in time, interleaved processes produce temporal textures that can feel like smooth motion, a steady drumbeat, syncopation, or complex polyrhythm. These rhythms play an important part in SimCity’s behavior and aesthetics.

The computer can only do so much in a limited amount of time, and interleaving allows time-consuming simulation processes to unfold without compromising the computer’s responsiveness. This juggling allows the multiple rhythms of simulation, user interaction, and computer hardware to transpire in synchrony.

Two clocks regulate SimCity’s simulation processes. A real-time clock, pulsing at sixty ticks per second, entrains user interaction (e.g., the mouse; updated sixty times per second), objects (e.g., the monster, ship, and helicopter; updated twelve times per second), tile animation (e.g., billowing smokestacks, licking flames, and traffic; twelve times per second), and blinking unpowered indicator (twice per second).⁹ As long as SimCity isn’t paused, these processes try to run at the same rate—regardless of whether simulation speed is set to slow, medium, or fast.

A second clock, the simulation clock, regulates all other simulation activity: growth and decay, taxation, fire propagation, crime, pollution, and more. The city’s fictional time advances with this clock. Its speed (relative to real time) is user adjustable. This clock drives the bulk of SimCity’s simulation logic through a master scheduler called Simulate, which allocates computer time to various subsystems and synchronizes their communication. Each time Simulate is invoked, it performs a different simulation process, thereby interleaving them.

Simulate performs one of sixteen substeps in round-robin fashion. One step is performed each time Simulate is invoked. A complete revolution through all sixteen steps is equivalent to one simulated week. Four revolutions represent a month, and forty-eight make a year. Consequently, taxes are collected on every forty-eighth revolution. Breaking down simulation into many smaller steps has numerous advantages. Systems can run at different frequencies. And activities driven by the real-time clock—animating tiles, objects, and handling user interaction—can be interleaved with a multitude of lower-frequency simulation tasks.¹⁰

The master scheduler also facilitates coordinates between simulation components. For example, as Map Scan updates the map (steps 1–8), it tallies up census figures used for tax collection (step 9) and simulating citywide demand (step 0). Information thus flows back and forth, between spatial and economic models, in a cyclical pattern. This interleaving must be precisely coordinated. If the economic simulations were to run before Map Scan finished performing its eight steps, they would receive incorrect data. Simulate thus regulates the flow of time and data.

When players adjust the simulation speed, they shift the gearing between real-time and simulation clocks, and simulated weeks pass at a changed rate. Taxes are always collected annually, but other processes, like updating land values, run less often as the simulation speed increases (relative to real time). Doing so compensates for the increased computation load, helping the computer keep pace with brisker simulation speeds.¹¹

Landscape

Just as bitmap images are composed of pixels, SimCity landscapes are composed of tiles. Consider a photograph from a news website. If you took a magnifying glass to it, each pixel would look like a single color. But inside the computer, each pixel is actually a number. In fact, pixels are typically divided into three numbers representing a mixture of red, green, and blue light.

SimCity maps work on a similar principle. Rather than representing a color, as in a bitmap, each SimCity tile represents a tiny chunk of city. SimCity’s simulation code refers to these as characters, betraying their origin in the Commodore 64’s graphics hardware.¹² SimCity has 956 possible characters, and it is by combining these that coherent forms such as road networks, power grids, bodies of water, and buildings are produced. To draw them, SimCity uses the corresponding image from a library. Cycling through some characters produces animations: moving traffic, burning fires, billowing smokestacks, sparkling fountains, explosions, sports games, and spinning radar dishes. By swapping characters, urban growth and decay take place: empty residential zones, for example, can be replaced by hospitals, churches, or luxury homes.

Players also touch this map. With the bulldozer tool, they can raze buildings into rubble. With the zoning tools, they can zone areas for different uses: residential, commercial, and industrial. Using other tools, the player can build police departments, fire departments, stadiums, parks, a seaport, an airport, roads, railways, power plants, and power lines. To determine whether a tile can be bulldozed, SimCity consults a status bit. Just as pixels are typically subdivided into three numbers (red, green, and blue) SimCity characters are divided into a character number and six status bits.

We can think of these status bits as additional layers of information used by the simulation for bookkeeping. These bits represent whether the tile is the center of a zone, it should be animated, can be bulldozed, is burnable, conducts electricity, and has electrical power. While some of these layers are for internal simulation use only, other bits, such as Animate, Bulldoze, and Powered, are apparent to the player’s sight and touch. If Animate is 1, then an animation plays. If the power is out on a zone (Zone is 1 and Powered is 0), then a blinking power outage icon is shown. And the player can only bulldoze tiles where Bulldoze is 1.

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Anatomy of a Primary Map tile. Since there are 956 characters, a 10-bit integer—enough memory to store any number between 0 and 1,023—is sufficient to represent one character. Since each primary map tile is sixteen bits, that leaves six bits for encoding more information.

§§§

SimCity’s map is lively and responsive. Players can build roads and power lines, zone lots for development, and bulldoze unwanted structures. But the player isn’t the only one modifying the map. The SimCity program is also at it: cars speed down roads, flames blaze, and unseen people erect buildings. The map, in a sense, is a place where two dance partners—player and simulator—interact. By twiddling the landscape’s characters, player and program engage in conversation about an imaginary city.

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SimCity’s character library. These characters represent the simulated landscape to both player and computer.

SimCity’s side of the dance is carried on by two parallel processes. One of them, Animate Tiles, is tied to the real-time clock, and updates flipbook animations for things like fire, traffic, and fountains. The other, Map Scan, is driven by the simulation clock and is responsible for more heavy-duty tasks: updating traffic levels, zone development, and fire propagation. Whereas Animate Tiles is a relatively shallow process that keeps things looking alive, Map Scan does the heavy lift of simulating urban development.¹³

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Animate Tiles updating the flipbook animation for a fire.

Animate Tiles is simple. Twelve times a second, it examines every visible map tile. If the Animate status bit is on, then Animate Tiles may swap in the next frame. Updating the character numbers stored in the tiles thus causes an animation to play. Despite its simplicity, this mechanism can play back animations with different temporal patterns, allowing fires, traffic, smokestacks, and football games to ripple at different rhythms.¹⁴

Map Scan is a more complex process—so complex, in fact, that the entire map is not updated in one go. That would be too much work for the computer to do at once (while still feeling responsive). Instead, one-eighth of the map is processed at a time (15 × 100 tiles), so it takes eight simulation frames to update the whole map.

Map Scan has two main jobs: evolving the state of the world and counting up how much stuff is in it. Tallying up things like fires, traffic, population, churches, and hospitals is important because these census totals are later used by simulation processes that do things like assess taxes, simulate zone demand, calculate road maintenance costs, and advise the player. By counting populations, Map Scan translates the city from a bottom-up spatial representation to a top-down economic one.¹⁵

Map Scan’s other, more demanding job is evolving the landscape. Different simulation logic is executed depending on the characters encountered. If Map Scan finds a fire, it simulates the fire’s behavior by consulting random numbers and proximity to fire departments. Then the fire might go out, spread to adjacent tiles, or just stay put. To record these changes, the numbers stored in the tiles are changed. For example, when a fire goes out, it is replaced with a random rubble character. Under the hood, it’s all numbers: fire (56–63) is replaced with rubble (44–47).

The most intricate part of evolving the landscape is simulating urban growth and decay. Working like a city planner, players demarcate residential, commercial, and industrial zones, but how these zones evolve—whether they turn into sparkling skyscrapers or remain undeveloped—depends on a multitude of factors, from citywide demand to local circumstances such as pollution, traffic, and power availability. This interplay, a dance between player and simulation agencies, is one of SimCity’s most engaging qualities. Wright compares it to “gardening. So you’re kind of tilling the soil, and fertilizing it, and then things pop up and they surprise you, and occasionally you have to go in and weed the garden, and then you maybe think about expanding it, and so on.”¹⁶ Flourishing or withering, SimCity’s evolution is guided by precise simulation algorithms. But before examining the logic of urban development, we must first consider something else: how SimCity tracks and updates all this spatial information. How does the simulator determine land value, and thus know whether to make a gleaming skyscraper or low-rent housing? How are pollution levels tracked? When deciding whether to extinguish a fire, how does SimCity know the proximity to fire departments? How is all this spatial data generated and tracked?

§§§

Shopping malls, factories, and homes are built and torn down. As people travel to home, work, and other destinations, traffic thickens and jams. Toxic emissions from highways, factories, and power plants waft over the city, unnerving residents and depressing property values. Land values fluctuate with development, pollution, and crime. Wealth and population density beget criminal activity, suppressible with nearby police stations.

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Many of SimCity’s internal maps can be viewed in the Maps Window.

Thick activity animates the city, but the main Edit Window gives only a glimpse of the action. To see beyond this visible fragment, and pierce the surface of SimCity’s many-layered world, players must consult secondary maps. The Maps Window shows a zoomed-out overview of the entire city, upon which distribution of crime, land value, fire and police coverage, traffic, pollution, population density, and rate of growth can be superimposed. These maps allow players to see more deeply into SimCity’s simulation. Like the status bits, they can be conceptualized as additional data layers.

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SimCity uses 18 maps to simulate the landscape. Only the Primary Map is saved.

Imagine a large drafting table on which is unfurled a large map of SimCity. This map tells us where everything is: trees, roads, buildings, power lines, and more. This is the map that stores the characters and status bits. But this map, the Primary Map, is stacked on top of seventeen other maps. Flipping through these, we find the Pollution Map, Land Value Map, Crime Map, and so on. In fact, many of these maps are never directly seen by the player. Temp and Fire Station, for example, are purely for internal bookkeeping, like scratch paper.

Secondary maps, whether hidden or visible, are usually modeled at lower resolutions. Whereas the Primary Map uses 120 × 100 tiles to model the entire city, the Land Value Map, for example, covers the entire city with only 60 × 50 tiles—half the resolution of the main map. (You can see the varied resolutions by looking at the relative chunkiness of the color blobs in the Maps Window—Population Density is more granular than Police Effect, for instance.) This trick allowed SimCity’s simulation model to become richer without blowing the computer’s memory and computation budget.

Multiple views into the simulation’s interior allow players to hold up the simulation, as it were, and turn it over, and examine it from different perspectives. Just as players can navigate across the map by scrolling the Edit Window, they can navigate into different aspects of the simulation through these alternate representations.

These maps influence one another and thus play a crucial role in simulation. The Crime Map, for instance, influences the Land Value Map, which in turn influences the Crime Map; high property values attract crime, and crime lowers property values. Maps are linked in a web of feedback loops. As a result, looking and thinking across maps helps players understand causality. These linked representations, in other words, help both player and simulator to think across aspects of the city.¹⁷

Say you are done playing SimCity for now. You save your city to a file and quit the program. SimCity only saves the Primary Map. This is because the seventeen other maps are all derived from it. Later, when you’re ready to continue playing, you load up your saved city, and SimCity recreates the secondary layers.¹⁸ The other maps are like funhouse mirrors that transform and reflect different aspects of the Primary Map.

But this information flow is not one way. Secondary maps, such as Pollution, then go on to influence the evolution of the Primary Map. If a neighborhood has high pollution, then people are less inclined to live there. The mirrors reflect one another. This is an example of a circular data flow, and SimCity employs many such feedback loops. Information flows and circulates across the maps, like blood coursing through a many-chambered heart. These flows are carefully orchestrated by Simulate.

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Data flow between map layers.

And so, as the simulator runs, these maps are continually updated. Map Scan iterates over the Primary Map, notes the presence of police and fire stations, and records their locations in the Police Map and Fire Station Map. Map Scan also generates simulated trips, and records their paths in the Traffic Density Map. Map Scan also consults many other maps. All of this happens in steps 1–8 of Simulate. Other Simulate steps handle other data flows. Step 15, Fire Analysis, transforms the Fire Station Map into a Fire Radius Map. Step 11, Power Scan, simulates the flow of power across the electrical grid. In this way, different processes transform and relay spatial information about the city.

This carefully scored choreography governs the dance between SimCity’s various map layers. With a sense of how these different elements fit together, we are now ready to discuss the dynamics of urban growth and decay. All of these maps—traffic, crime, land value, pollution, and power—affect the city’s development.

Notice a few things before we move on. First, the Primary Map is at the center of almost everything. Given that all the other maps are derived from it, this shouldn’t be surprising. Second, the police and fire systems are comparatively peripheral. They look like elaborations on a core model, which is exactly what they are; police and fire stations were not part of Wright’s original City Planner prototype.¹⁹ Finally, notice how interconnected the Land Value Map is. Sitting within a tangle of data flows, the Land Value Map exerts a strong influence on urban growth and decay and fuses influences from a wide variety of sources. According to Wright, it was early in SimCity’s development that he introduced an “economic based system in which land value was central to the way everything integrated together.” These feedback loops made the “overall model less stable, more dynamic, and hence, more realistic.” It was, he notes, a “very effective” approach.²⁰

§§§

When Wright began turning Wedit, the world editor he had made for Raid on Bungeling Bay, into a city editor, he started down a path in which the player and computer would collaboratively produce the city. The player, acting as a kind of city planner, would draw roads and zone lots for different uses. The program, acting in the role of a living city, would worry about the details. It would choose the proper road characters so that the entire road network—like a web of mating dominoes—would fit together. The program’s simulator would also play the part of citizens, businesses, real estate developers, and construction firms. While the player directly builds roads, power lines, and police stations, they can only zone lots for residential, commercial, or industrial use. It is SimCity’s unseen inhabitants who desire, build, and inhabit the single-family homes, apartment buildings, factories, stores, and shopping malls.

The simulator decides how (and if) empty zones will develop. An empty residential zone might become a church or hospital if the city thinks more are needed. (Conversely, a church or hospital might devolve back into an empty zone.) If the simulator decides that new residents should move in, it adds single-family homes and then increasingly dense apartment buildings. Development also reflects wealth. As commercial and residential zones transform, they not only become more and less populous, but reflect the Land Value Map.²¹

This process is driven by Map Scan. If it encounters a residential, commercial, or industrial zone tile, it increments the citywide census and then simulates zone development. Development is stochastic—it involves throwing and consulting dice (a random number generator). But development isn’t totally random. The odds are shaped by two “valves,” as SimCity’s code calls them—a terminological debt to Forrester’s work—that act like spigots that direct an influx (or outflux) of development. A global valve reflects citywide demand, and is computed by the overarching system dynamics model. (More on this below.) The local valve represents spatial influences like access to power, transit connectivity, and pollution.²²

This development process will cause a zone to grow (increase in population), decay (lose population), or stay the same. To effect a change, the characters that comprise a building are swapped out for new ones that depict—and encode—the new population level. The chosen building, in other words, represents population to both player and simulator. The new characters also visually indicate the area’s land value (for commercial and residential zones). As a result, buildings realistically lag behind Land Value, so old buildings in a once-fancy neighborhood will stay that way even though the neighborhood is now run down—until the simulator redevelops it.²³

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Zone development—whether to grow, shrink, or stay the same—is a stochastic process influenced by local and global factors.

Zones also generate traffic. Residential, commercial, and industrial zones periodically attempt to make trips via the road and rail network to complementary zone types. And more populous zones make more traffic. Each trip takes a series of random turns in an attempt to reach a desired destination type. The success or failure of these trips influences zone growth and decay. If a trip is successful, then the route taken is recorded in the Traffic Density Map. (Players will later see this traffic—as tiny animated cars—after Map Scan updates the road characters to reflect the Traffic Density Map.)

The spatial logic of these rules creates a kind of puzzle for the player. More growth means more traffic, and traffic causes pollution, and pollution hinders growth. As Wright puts it, “You could have two cities with the same number of identical zones and terrain, but it is the way it is laid out that counts. It’s a living jigsaw puzzle.”²⁴ The logic of this puzzle reflects ideas Wright imbibed from Walter Christaller’s central place theory. Location drives land value and thus influences where zones—the jigsaw pieces—will thrive.²⁵

According to Wright, the city’s internal market—gas stations, grocery stores, and shopping malls—serves the residents. This market is modeled by commercial zones, which thrive toward the city’s center of mass, where property values are highest. The city’s external market is modeled with industrial zones. These zones serve the world outside the city, so they aren’t too picky about their location. This means that players are encouraged to locate industry at the city’s outskirts, where land is cheapest and pollution won’t bother residents—which is where factories are typically found in real cities. Residential zones thrive away from pollution and toward the city center, and thus flourish somewhere in between center and periphery. SimCity thus encourages development along a gradient defined by distance to the city center.²⁶ Wright’s interpretation (and implementation) of central place theory shaped the living jigsaw puzzle that players attempt to solve.

Things in SimCity’s map—the jigsaw puzzle pieces, as it were—interact in a variety of ways. In addition to adjacency—interacting with one’s neighbors, as in a canonical cellular automaton—other spatial topologies are also employed: proximity (such as distance to city center or police and fire departments), layering (interaction between maps), networks (transit and power), globals (like citywide demand), and zones (a special central character governs the whole zone). While space in Forrester’s urban simulation was practically absent, SimCity employs diverse spatial relations. In addition to representing urban space, these diverse topologies project overlapping affinities, repulsions, and constraints for players to puzzle through.

Economics

SimCity’s citywide economic model can be glimpsed in a tiny bar chart on the left side of the Edit Window. Although it’s small, these figures have outsize effects: they determine global demand for residential, commercial, and industrial zones. SimCity players pay close attention to this chart as it gives succinct feedback about a city. Positive values mean the city wants more of a particular zone type. Negative values indicate the city wants less. Zone development is guided by these global values (as well as the local valves discussed earlier). If citywide demand for housing is high, then existing residential zones are more likely to grow. The bar chart also guides players, who perceive whether they should add (or remove) certain zones.²⁷ This citywide economic simulation is a system dynamics model informed by Forrester’s work on urban dynamics. But integrating spatial and economic models required Wright to reconcile two very different simulation traditions: one bottom-up and spatial, the other top-down and numerical.²⁸

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Left: Forrester system dynamics figure. Right: SimCity’s citywide system dynamics model is joined to a cellular automaton.

How did Wright combine these representations? Since the Primary Map is ground truth about what exists in a city, Wright had Map Scan tally census figures, effectively translating a spatial representation into a numerical one. Those census figures are then fed into Set Valves, the system dynamic model, which computes citywide demand. If, for instance, a city has a bustling business sector—indicated by a high commercial and industrial population—but not enough people to work all those jobs, the city will demand more residential zones. Citywide demand is then fed back into Map Scan, where it influences zone development.

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Simulating citywide hospital demand.

What happens inside Set Valves? To understand how zone demand is computed, it helps to look at a simple example: hospital demand. The simulator strives for a ratio of one hospital (or church) per 128 simulated citizens. To accomplish this, Map Scan and a system dynamic calculation work hand in hand. Map Scan counts up the number of residents and hospitals. Later, a system dynamic process calculates hospital demand using the following steps:

1. Consider the population of people and hospitals. These are the input levels to the calculation.

2. Calculate desired hospital count. A ratio of one hospital per 128 citizens is desired, so divide the total population by 128.

3. Set the demand valve. Too many hospitals? Make demand negative. Too few hospitals? Make demand positive. Just right? Demand is zero.

Hospital demand affects the probability that Map Scan will transform an empty residential zone into a hospital (or back). This feedback loop steers citywide hospital development towards an amount that tracks population changes. The exact same mechanism applies to churches. And the same underlying principle applies to calculating demand for residential, commercial, and industrial zones.

Although the logic for Set Valves is more complicated, the basic idea, of using population ratios to guide growth, is the same. Zone demand is computed using the same three steps:

1. Examine existing population levels. ResPop, ComPop, and IndPop represent the current population for each zone type. These are the input levels to Set Valves.²⁹

2. Project a desired population level. Set Valves does some math to represent births, migration, employment (the balance between available labor and jobs), labor, an internal market, and game difficulty.³⁰ The result is a set of projected populations for each zone type.

3. Set the demand valve. Comparing projected versus current populations tells Set Valves whether to stimulate or restrict growth. Is the city growing so fast that more people are needed to do the available work? If so, then the projected residential population will be higher than the current residential population. As a result, the Residential Valve is nudged in the positive direction—the valve opens up, stimulating growth. The city’s relative attractiveness has gone up. If a population is projected to decrease, then its valve will be nudged downward—the valve closes down, potentially going negative and causing decay. On top of this, if the city nurses an unsatisfied desire for a stadium, airport, or seaport, then growth is halted.³¹ Each “spigot”³² is thus adjusted to guide the growth and decay of zones across the map.

Following the system dynamics tradition, levels and rates joined in feedback loops are used to represent a social system. The goal-seeking behavior of a servomechanism becomes the goal-seeking behavior of a teeming population. As in Urban Dynamics, ratios between various quantities (like jobs versus population) modulate rates of change. These rates, or valves, drive business creation (and destruction) and relative attractiveness (immigration and emigration). Enmeshed in feedback loops, zone populations coevolve but never stabilize and are prone to oscillation. Like Forrester’s model, low taxes stimulate growth and high taxes discourage it.

As in Forrester’s work, intentional time delays and instabilities are introduced for representational effect. Using slightly out-of-date census data means that SimCity’s inhabitants take time to respond to changes. They pursue their goals, but imperfectly. Seeing an apparent lack of housing, a housing developer makes plans to build more homes. But by the time financing is secured, permits are pulled, and construction is complete, the city’s housing shortage has already become a surplus. Latency represents slowly dawning awareness, time-delayed response, and speculation. Set Valves heightens these effects by accumulating unsatisfied demand (or aversion) over time, giving them additional inertia. SimCity reiterates not just Forrester’s work but the servomechanism control theory he drew on. These techniques help make SimCity feel realistic.

As in Urban Dynamics, the notion of relative attractiveness (between the city and its limitless environment) drives migration in and out of the city. As in Forrester’s model, if unemployment is low and there is not enough labor to do the available jobs, then more people are attracted to the city (and its presumably high wages). If unemployment is high, then the city becomes less desirable for workers but more desirable for new businesses (low wages).³³

§§§

These simulation techniques have dynamical and aesthetic consequences. If we consider SimCity as a plaything, we see that system dynamics helps imbue it with a feeling of liveliness and surprise. It teases and evades. The city never quite reaches a steady state—there is always something to adjust. It’s like chasing after a bouncing ball.

This citywide economic model also imbues the simulated cities with global coherence. Whereas cellular automata excel at bottom-up dynamics that infuse space with lively local detail and variation, the system dynamic model ties everything together. SimCity’s sprawling and bustling landscape is yoked to a centralized control system.

This has important consequences. By channeling information through a centralized clearinghouse, every aspect of SimCity’s simulation becomes coupled, and the city can exhibit overarching behaviors. From this command center, the simulator can govern, or at least influence, its many components. Moreover, this privileged centralized perspective also enables the player to understand and affect the city as an organic whole. One glance at the user interface shows citywide demand for residential, commercial, and industrial zones. Using the budget interface, players can make adjustments—to the tax rate, for instance—that modulate activity across the city. This centralized perspective also serves the simulation maker, who can fine tune the behavior of an entire city from a single place. System dynamics, acting like an invisible hand that guides the whole city, is itself guided by the simulation author. Wright could, from a single place, adjust parameters and rules with global implications. SimCity’s systemic processes thus provide a common place for engaging with the city in overview, for simulation, player, and author.

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The Budget Window allows players to set citywide policy.

The macroscale complements the microscale. By hybridizing simulation techniques, Wright created a representation that combined the top-down affordances of system dynamics with the bottom-up texture of cellular automata. SimCity’s sprawling landscape is embroidered with fine detail. This cellular world holds out microscale dynamics, perspective, and affordances. Players scroll across the map, and watch tiny traffic jams clog up their carefully crafted road network. They erect finely detailed power grids, transit systems, and development plans. And they contend with land value, crime, traffic, and pollution—dynamics that emerge from these spatial systems. But players can also take a step back and inspect their city in overview, and see global dynamics unfold. The Maps Window shows them the entire city. Systemic factors like citywide demand and budgeting allow players to perceive and act on their city from the highest vantage of all.

Objects

So far, we’ve considered simulation dynamics at two very different scales: the microscale dynamics of the cellular landscape and the macroscale dynamics that guide it from a single point of control. The agents that roam SimCity’s landscape add another textural layer, nestled in between the centralized overview of system dynamics, and the diffuse detail of cellular automata.

SimCity’s code refers to these as objects. Their activities are driven by the real-time clock rather than the simulation clock, which means that they romp in real time, as in a videogame, imbuing the world with bright spots of vivacity. Whereas the economic simulation is indebted to system dynamics and the map is informed by cellular automata, the animated objects reflect the influence of computer games. Games like Super Mario Bros. (1985) and Raid on Bungeling Bay (1984) make extensive use of simulated objects careening in space—mushrooms, helicopters, missiles—as well as specialized graphics hardware for drawing these sprites. The Commodore 64 supported eight simultaneous sprites, and SimCity has up to seven objects, reflecting this constraint.³⁴ SimCity’s objects also emit game-like sound effects: Godzilla bellows, the ship sounds a foghorn, explosions roar, and the helicopter pilot reports on high traffic.

Objects interact with the landscape. The airport spawns the helicopter and airplane, the seaport summons a ship from the edge of the map, and railroad tracks spawn the train. The ship navigates waterways—its approach causes bridges to draw open—and trains traverse railways. Objects also respond to the city in systemic fashion. Godzilla spawns (if automatic disasters are enabled) when the pollution average is high, and seeks out the most polluted part of the city. The helicopter seeks out congested roads (as directed by the traffic generator) and reports on high traffic as it flies overhead. The city’s sense of aliveness—even self-awareness—mounts.

Objects interact with one another, and collisions are violent. Godzilla and the tornado smash buildings as well as the airplane, helicopter, ship, and train. If airplane and helicopter intersect, the midair collision destroys them both. Ships can run aground. This mayhem produces explosions, fires, and rubble.

Destruction is a humorous, simple, and robust way of handling the unexpected collisions that are certain to occur in such an open-ended system. By putting players in a design role, Bill Budge’s Pinball Construction Set invited them to create arbitrary polygons, some of which might be malformed. His program gracefully and robustly handled these. SimCity players are similarly invited to try strange experiments, juxtapositions, and push on edge cases—just as Wright enjoyed doing with Flight Simulator. Can the ship be trapped by quickly building bridges? If it runs aground, it explodes. Players are liable to push boundaries, and SimCity’s madcap mayhem enacts one of Wright’s central design tenets: failure should be fun.

Messages

In order to make his initial prototype more accessible to others Wright added “more clear feedback, in terms of what people need and why they need it.”³⁵ One crucial channel of feedback comes to players via a messaging system.³⁶ Messages alert players to things like plane crashes, citizen demands, power outages, flooding, and demand for infrastructure and zones. These messages narrate the fictive world, directing player attention toward important events and needs, as well as possible solutions. (SimCity 2000’s irreverent newspaper elaborated on this approach.) This narrative technique deepens our immersion in SimCity’s make-believe world and guides play by coaching, coaxing, and goading the player.

These messages come from one of two places. They either directly emanate from simulated events, like an earthquake, or they come from a system called Send Messages.

The first kind of message, organic simulation events, narrates the unfolding action, such as when players attempt to use a tool they can’t afford—“Insufficient funds to build that”—or something goes wrong, such as, “A plane has crashed!,” “A Monster has been sighted!!,” or “Brownouts, build another Power Plant.” Such messages are dispatches from within the simulation, which narrates its own unfolding logic.

The second kind of message is generated by Send Messages, which, operating like a coach, observes the simulation and supplies a running commentary. Send Messages is periodically called on (by Simulate) to reflect on various city statistics, such as population growth, the ratio of citizens to roads (“More roads required”), and tax rates (“Citizens upset. The tax rate is too high”). If certain conditions are met, a message is generated explaining what is happening and sometimes why and what might be done about it.

Some of these messages make infrastructure requests (for a stadium, airport, or seaport), and signal to Set Valves that growth be restricted until the request is met. “Commerce requires an Airport,” for instance, is a demand that halts commercial growth if it is unmet. By making these requests, Send Messages narrates and places obstacles of escalating difficulty in the player’s path, forming a kind of dramatic arc.

The risk with a system like this is that the player is flooded with messages or starved of helpful guidance. To avoid irritating the player with an onslaught of uninvited guidance, a schedule regulates when Send Messages checks its various conditions.³⁷ Some simple prioritization logic acts as a kind of traffic controller, ensuring that simultaneously firing messages are properly prioritized and that messages responding to user actions, for instance, always take precedence. In this way, the player doesn’t see a rambling or flickering message display.

Simulation and Make-Believe

Just as a playmate might scaffold the transformation of a banana into a telephone, SimCity invites players into a make-believe that transforms the computer into a city and the player into a super-powerful city planner.

SimCity is a carefully constructed software illusion, but its artifice becomes starkly apparent when we examine apparent simulation bugs: places where mechanism diverges from player belief.³⁸ For example, only one power plant needs to be connected to the power grid, but all power plants supply the city with electricity.³⁹ Such divergences shed light on how SimCity conjures illusion and invites player belief.

We believe power plants should be connected to the power grid, and indeed the simulation supports that belief. Summoning our expectations about power grids, the simulation requires that the first plant be connected, and so we continue to do so for the second and third—even though this is not necessary. A seaport suggests the sea, and so we place it on the shore—even though the citizens and ship are indifferent to its location. We happily remain in the dark, imaginatively filling in the details, a process Janet Murray describes as “the active creation of belief.” SimCity implies and we simulate.⁴⁰ All of this highlights the extent to which we dine on the expectations we ourselves bring to the simulation table, and the artful setting of that table. The active creation of belief doesn’t just forgive simulation shortcomings; it underpins the entire enterprise.

The traffic system employs an illustrative legerdemain. Zones periodically emit invisible probes that snake their way across roads and rail in search of certain zone types. But players don’t see these trips. Instead, peering through frosted glass, they see only a composite blur: the smoothly animating veneer of traffic. A rowdy stochastic process blossoms into perceptual softness.⁴¹ Through artful misdirection and abstraction, SimCity implies processes that aren’t actually taking place.⁴² It hides and it reveals; it is transparent and opaque. According to Wright, “there are certain things we just cannot simulate on a computer, but on the other hand that people are very good at simulating in their heads. So we just take that part of the simulation and offload it from the computer into the player’s head.”⁴³ SimCity’s most powerful simulation component isn’t inside the computer: it’s the player’s imagination.

9

Playing SimCity

In 1991, Sun Microsystems saw porting SimCity to their Unix platform as an opportunity to offer “a true ‘desktop’ GIS [geographical information system] solution” as well as a “leading entertainment package.” “The game,” they noted, “is currently being used by many government offices to train their city planners.” GIS industry leaders were supportive. While the aspiration to professional GIS tool was overblown and the contradiction between professional tool and entertainment unexamined, this amorphous ambition enabled SimCity to be ported to Unix with backing from Sun’s marketing department.¹ That SimCity could be earnestly envisaged by Sun as both amusement and professional tool illustrates the reach of the SimCity network and SimCity’s remarkable ability to be recast for numerous roles: entertainment, city planning, education, and more.²

Years later, when the One Laptop Per Child (OLPC) initiative sought open-source, educational, and child-friendly software for their low-cost Unix machines, they looked to SimCity. Rooted in Seymour Papert’s philosophy of constructionism, and championed by MIT Media Lab cofounder Nicholas Negroponte (alongside figures such as Alan Kay), OLPC was imagined as a philanthropic project for children of the Global South.³ Like Sun, OLPC was drawn to SimCity’s ambiguous potentialities. To OLPC, SimCity was educational, accessible, and fun. Moreover, a Unix port already existed and SimCity’s age made it suitable for OLPC’s low-cost hardware. Since SimCity was superseded by sequels, and unlikely to be making much money, perhaps Electronic Arts would open-source it?⁴ Behind the scenes, the push received support from a variety of actors: Will Wright, SimCity’s influential designer; Doreen Gehry Nelson, an innovative educator and long-time Maxis collaborator; Don Hopkins, who had ported SimCity to Unix, worked on The Sims, and at Sun; John Gilmore, an early Sun employee and cofounder of the Electronic Frontier Foundation; and Walter Bender, executive director of MIT’s Media Lab (and an OLPC leader).

As their 2008 press release made clear, Electronic Arts’s decision to open-source SimCity turned on the association with OLPC, a “not-for-profit humanitarian effort,” allowing them to boast that this was “the first time a major video game publisher has gifted a game to the world.”⁵ The SimCity network, once again, facilitated cross-community collaboration, legitimacy exchange, and the production of something new: an open-source SimCity.⁶

§§§

SimCity’s profound adaptability has enabled it to be taken up by different communities and facilitated exchanges between those communities. As Peter Galison has shown, the adaptability of games, as a conceptual category, has underwritten the exchange of tools and ideas between communities. Games make excellent “contact languages.”⁷ SimCity’s adaptability can further be explained by looking to play theory. According to Brian Sutton-Smith, ambiguity is one of the defining features of play, which can be construed as uplifting—as in learning, imagination, and enjoyment—as well as concerned with fate, power, identity, and frivolity.⁸ SimCity’s ambiguous potentialities, in other words, are paradigmatic of play.

This chapter examines SimCity’s ludic affordances to advance two interrelated arguments. First, it aims to show that SimCity’s protean quality—and, by extension, the SimCity network—is rooted in SimCity’s ludic affordances. Seen through a play frame, SimCity’s liminality and contradictions help explain the diverse and often paradoxical purposes to which it has been put. Second, this chapter examines the source of SimCity’s “holding power”⁹ by analyzing it as a play phenomenon. Why, in other words, is SimCity fun?

Play theory offers a new take on SimCity’s liminality. In Jesper Juul’s “classic game model,” SimCity’s “lack of explicit goals” helps frame a definitional boundary for games.¹⁰ But rather than ask why SimCity is or isn’t a game, this chapter turns the question on its head by analyzing SimCity as a paradigmatic form of play. In this way, I hope to demonstrate the value of adopting a play-centric, rather than game-centric, perspective.

This play-centric approach dovetails with the design orientation of Will Wright, SimCity’s creator. SimCity has rules and is game-like in many other respects, but one cannot win or lose it; SimCity offers no such judgments.¹¹ This liminality is exactly what frustrated Wright’s original publishing deal with Brøderbund, who expected a win-or-lose game. From a play theory perspective, games are “institutionalized” play activities—a genre of play “structured by explicit rules that can be precisely communicated.”¹² From this vantage, SimCity retains more of play’s innate malleability and negotiability; SimCity has not ossified into a full-fledged game.

Wright contrasts Maxis titles to games by describing them as hobbies that invite differentiated forms of participation that foster player communities. He prefers the concept of toys, which he sees as “free-form,” “open-ended,” and available to experimentation and recombination, unlike games, which “tend to be isolated universes where there’s a rule set, and once you leave that universe the rule set is meaningless.”¹³ Wright’s design tenets and metaphors tend to be play-centric, stressing make-believe, absurdity, failure, creativity, and hobbies. He uses gardening, for instance, as a metaphor for how SimCity players cultivate a living world ripe with surprise.¹⁴ To describe the strategic, and more game-like, aspect of his work, Wright employs the language of economics, biology, and physics.¹⁵

Following a brief play theory primer, this chapter examines SimCity’s holding power using four play frames: playmate, toy, construction set, and game. It concludes by linking this analysis to the SimCity network, arguing that SimCity’s ludic affordances—not just its holding power but also its ambiguities and open-endedness—enabled the diverse purposes to which SimCity was put.

Play Primer

Although a precise definition is elusive, there is widespread agreement about the core characteristics of play:16

Subjective. Play harbors certain subjective qualities. It’s pleasurable, absorbing, and positively valued on its own terms—even if it isn’t “fun.” Players like playing and do so for its own sake. Play is also marked by a special mood, orientation, or frame of mind. The same action can be play or not-play—it all depends on attitude. And while play might carry physical and psychological risk (as in sports or Truth or Dare), play is preconditioned on a feeling of safety.¹⁷

Separate. Play is buffered from ordinary life. Consequences are suspended or limited. While play-fighting can be fierce, and injuries do happen, players are not truly fighting. Play is simulative: not-play is transformed into play, and this transformation is marked by characteristic stylizations: reversal, ritualization, parody, schematization, exaggeration, absurdity, and paradox. These stylizations also have a metacommunicative function: they help players coordinate and signal that this is play, as when dogs perform play bows and nip rather than bite, or when people smile, make silly faces, or give knowing looks. Toys do something similar. A toy train, for example, is an abstracted miniature. Such transformations make play easier to assimilate and grasp, both physically and imaginatively.¹⁸

Agency within structure. Play is structured performance. It involves agency: striving, action, and movement. This agency operates within structures that engender uncertainty, risk, tension, and variation: rules, ritual, similarity, order, and constraints. Players throw balls back and forth, dance in circles, fiddle with objects, role-play house, take turns making funny sounds, and follow actions prescribed by game rules. But while game rules are fixed, the structures that constrain play are mutable. And these structures are not immune to play’s parody, inversion, and stylization, giving play an inherently unstable and oscillatory quality.¹⁹

Social. Play is deeply social and collaborative. Social play requires shared attention as well as metacommunicative signaling that this is play, like the dog’s play bow. Even competition requires collaboration, as in a race, where participants agree to go around the track (and not cut across it). This is an example of shared pretense, whereby players agree to certain pretend structures. When these pretend structures are institutionalized—fixed into precise rules and named—they take on the quality of social objects, like games. Playmates help one another play, as when parents and older siblings scaffold make-believe for children, experienced opponents self-handicap, or through metacommunicative signaling, like the play bow, which helps frame and sustain play. As a result of these social qualities, play fosters shared meaning, companionship, and the formation of social groups, like the “secret societies” of Pokémon and sports, whose fans and players speak an alien language—or so it seems to outsiders.²⁰

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SimCity box, Godzilla version.

SimCity as Playmate

Playmates prime, prolong, and guide play. In SimCity, this begins with the whimsical box, which is ripe with absurdity, paradox, and inversion. A retro television (or radio?) shows a monster menacing San Francisco, as in a science-fiction film, but the buttons and dials aren’t for changing the channel. Instead, they refer to disasters, energy, budget, and population. The ostensibly sober controls, however, do not mark SimCity as a productivity tool. The controls are made of Bakelite, an anachronism that promises not the computerized efficiencies of a word processor or spreadsheet but parody and hijinks.²¹ Tools are for work, but SimCity is an instrument of play. A Godzilla-like monster adorns the box, but the manual includes a “History of Cities and City Planning.” Opposites are mixed: silly and serious; phantasmagoric and scholastic; big and small. The vast city has been transformed into an appliance-sized miniature. The back of the box calibrates expectations. Screenshots and text prime our imagination with specific visions of what we might do: confront disasters, lay roads, supply electricity, create a dream city.

Like a good playmate, SimCity frames and guides play. Don’t want to start from scratch? Then dive into a prebuilt scenario like Tokyo or San Francisco, and try to problem-solve within its narrative frame. Or, alternatively, unleash a wave of destruction from the disaster menu, like a fire, flood, or monster. Such violence, Wright came to believe, isn’t just carnivalesque mayhem; destroying a complex system promotes learning about how it works and its inherent fragility.²² Even if players choose to build their own new city, simply browsing the scenarios—and their thumbnail maps, names, and descriptions—helps establish expectations. Like the back of the box, the scenario menu proclaims: Look at what you can do with SimCity—here are some inspirations!

Feeling more creative? Start a new city. Randomly generated landscapes—novel play prompts—keep things interesting. As the settlement grows, players gradually encounter new problems, like pollution and traffic, easing them into mastery through learning by doing.

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Left: Choices presented when beginning play. Right: Scenario introduction.

To create a new city, players select from randomly generated landscapes, choose a difficulty, and then name their city.

Like an older sibling, parent, or dungeon master, SimCity scaffolds players into fluent make-believe participation through supportive discourse.²³ Messages unobtrusively narrate, explain, and guide. They congratulate the player on their growing city; warn of brownouts, earthquakes, and train wrecks; and advise on high taxes, underfunded roads, and limited housing.

“SimCity is Alive!” proclaims the box, trumpeting its signature accomplishment as a simulation but also as a playmate. Like a dog running to and fro or a person throwing a ball, SimCity’s animated world beckons and cajoles: it invites us to play, and to keep playing.

SimCity as Toy

SimCity is also an artfully crafted toy. Like a bouncing ball, pull toy, or model train, SimCity is dynamic, responsive, and sometimes unpredictable. It radiates aliveness. Cars drive on the roads, and factories spring to life. Players often get more than they bargained for. The world pushes back with pollution, traffic, and other unintended consequences—as complex systems do. SimCity shimmers with unpredictability that can frustrate and delight; it twinkles between predictability and ferality.

Striking the right balance between stability and instability was crucial, says Wright. He explains that “land value was central to the way everything integrated together. It made the overall model less stable, more dynamic, and, hence, more realistic. Some people call it ‘Life on the edge of chaos.’ I thought it was very effective.”²⁴ Wright’s comparison of SimCity’s dynamics to Christopher Langton’s notion of life on the edge of chaos is telling. Life, Langton theorized, might have originated in a phase transition, “a critical balance point between order and chaos.”²⁵ Wright was interested in finding a “realistic” balance between order and chaos. Too much chaos, or surprise, and the feeling of coherence—and thus realism and agency—is lost. Too much order and the system feels dead, unnatural, and dull. SimCity’s finely tuned systems, Wright suggests, are engaging because they also walk this edge, balancing coherence and surprise.

Mercurial aliveness is a direct result of SimCity’s interconnected simulation components. As we saw in the previous chapter, intentionally delayed communication, interleaved processing, and feedback loops conspire to produce a world that feels vivacious and unpredictable. Delayed consequences and oscillatory instabilities are also characteristic of real cities, where it takes time to see the ramifications of interventions. Moreover, waiting to see what happens creates drama, surprise, and engagement. SimCity’s holding power owes much to its instabilities, delays, and polyrhythms, which are mimetic, dramatic, and computationally expedient.²⁶

“Lay roads and traffic moves,” says the box. The city is familiar, a domain in which most players will know what to do and what might happen. Expectations are established and met, a technique Janet Murray describes as “scripting the interactor.”²⁷ Phenomena like roads, power lines, and buildings tee up expectations and engender a feeling of safe familiarity.

Toys don’t just reproduce the world; they are easy-to-grasp models. Miniaturization and stylization turn the large and overwhelming—a railroad, household, or city—into something small and manageable. In SimCity, vast landscapes are shrunk down to tiny and easily manipulated computer models. In addition to abstraction and miniaturization (a world in a window), SimCity signals toylike stylization through absurdity (monster attacks), transparency (data maps), and supernatural agency (editing a city; speed and difficulty controls).

Toys are easy to grasp and internalize. SimCity’s rich feedback—graphics, animation, sound, messages, maps, and graphs—conjures a legible world. They bring traffic, monster attacks, and economies to life. Multiple representations are key to making SimCity’s innards legible. Graphs and maps visualize things like crime, pollution, traffic, and land value. Diverse representations encourage players to make connections and mentally fill in the details of how, for instance, land value and crime are correlated. Poke here, for example by adding a power plant, and peer at what happens over there, in the pollution and power maps. Using multiple representations, players learn to interpret causality and internalize SimCity’s inner logic.²⁸

The graspability of toys is not just about legibility and agency, but the ability to assimilate to one’s own purposes. SimCity helps you understand what happened but does not tell you how you should feel about it.²⁹ It’s a toy, not a game. There is drama but no judgment. By simultaneously offering rich feedback and suspending judgment, SimCity leaves ample room for players to formulate their own activities and goals. Success and failure are narratives that players can opt into or define for themselves—if they choose to. This open-ended quality makes SimCity ripe for appropriation. Like many toys, SimCity provides players with enough structure and space to improvise their own activities.

In play, we adhere to structure and follow rules, but we also invert and negotiate them. Structure provides a stable ground against which inversion, transformation, and play can take place. Cities can be smashed. Typing “fund” injects $10,000 into the treasury—embezzlement, according to the documentation. SimCity invites inversion but also pushes back. Use this cheat code too many times and SimCity will unleash an earthquake as punishment.³⁰ But unlike Monopoly, whose open structure invites and accepts house rules, SimCity players do not negotiate with the underlying simulation mechanics.

SimCity as Game

Proper games have fixed means (permissible moves) and ends (goals).³¹ SimCity has both of these—sort of. Players can opt into scenarios with predefined goals, but the elective nature of these goals underlines the fact that all goals in SimCity are contingent. SimCity suggests goals, but it is up to the player to adapt its rich feedback toward their own ends. While SimCity’s rules are fixed, they do have some give built into them, as players can embezzle funds, adjust game speed, choose a difficulty level, select a landscape or scenario, and decide how they want disasters to be part of their play, if at all. SimCity thus combines the open-ended quality of a toy with the teleological satisfactions of a game.

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Players decide whether to heed SimCity’s rich feedback.

While SimCity is not, following Juul’s definition, a game,³² it contains design elements that can be understood in terms of games like Chess, Monopoly, and Tetris. It employs game-like mechanics, such as economic tradeoffs, that invite strategic thinking. A limited budget means that players must strategize about how to best spend their funds. Some choices are crisply delineated, as in whether to invest in coal or nuclear, road or rail. These options present different short- versus long-term effects. Coal plants cost less than nuclear, but they produce less energy, pollute more, and have a shorter life span. Other economic choices are squishier, as in what, where, and when to develop. Invest in housing or parks? Develop a new neighborhood now or later? Such economic choices are reminiscent of games like Monopoly, where one must decide whether to buy certain properties and when and how to invest in them.

Space must be allocated, which also invites strategic thinking. SimCity’s intricate and diverse spatial relationships force players to think carefully about how to best organize, and not just allocate, space.³³ These sorts of spatial conundrums are reminiscent of games like Tetris and Go. Housing should be near industry so citizens can easily get to work, but not so close that pollution drives people away. As Wright has remarked, SimCity is “a living jigsaw puzzle.”³⁴ As with Tetris, each move mutates the spatial puzzle before you—another example of SimCity’s never-ending disequilibrium and how it provokes ongoing play.

SimCity presents game-like challenges in other respects. Difficulties mount as your city grows. Growth is accompanied by unintended consequences such as crime, pollution, shifting land values, and swelling upkeep costs. As it grows, SimCity’s world pushes back harder. As in Raid on Bungeling Bay, escalating difficulty emerges from a systemic world, not game levels of increasing difficulty.³⁵ Some challenges offer the pleasure of discrete story arcs. As the city reaches certain population levels, its inhabitants demand expensive capital projects: a stadium, seaport, and airport. These demands, if unmet, choke growth. These obstacles enhance the world’s fictive reality—residents demand a stadium!—while offering the quest-like satisfactions of overcoming discrete challenges.

SimCity as Construction Set

As a construction set, SimCity offers “loose parts” that can be reconfigured to make many different cities.³⁶ Magnetic poetry kits do something similar, offering collections of words—I, love, flower, a, me, your, wreck, for, am—that can be easily recombined to make evocative phrases: “I am a wreck for your love.” Surprising meanings emerge from how the pieces are recombined, even if through chance. Each city uniquely expresses a particular play path, giving players the kind of creative autonomy more associated with building blocks and word processors than computer games.

SimCity’s graphical user interface leverages a design tradition that valued concreteness and creativity. This construction set quality is further amplified by automaticity. Players don’t choose exactly which buildings go where but specify zones that the simulation fills in with specific buildings. In addition to offering something akin to the drama, surprise, and wonder of a garden, SimCity augments the player’s creative acts. This “magic crayon”³⁷ quality happens at multiple scales. At the microscale, players place a single tile of road or power line, and the software chooses the appropriate graphics, making it so that roads and power lines appear to interconnect, or pass over water as a bridge. This augmentation happens at the macroscale too. Lay some roads and do some zoning, and a finely detailed world sprouts into existence, brimming with traffic and activity. It is a hybrid agency that unites player and machine creativity. The computer, operating in the guise of a world, functions as a playmate that breathes life into the player’s work.³⁸

SimCity’s aliveness heightens the intrinsically pleasurable act of creation. When playing SimCity, one forms design intentions, from the winding path of a neighborhood road to a vision for how one’s city will unfurl across the landscape. There’s enormous satisfaction in working toward and completing these patterns, a feeling SimCity spurs in a way that might seem counterintuitive: it resists and slows down the player. Cities don’t always develop as planned, and this resistance serves as a well-balanced creative foil, instigating adjustments in approach and inspiring new undertakings. SimCity prolongs creative play, slowing down the player’s ability to complete designs through incremental growth, limited funds, and pushback. Interventions cost money, players must wait for tax revenues to accumulate, and unintended consequences crop up. (This is part of the reason that embezzlement and speed controls are so important. They allow players to redefine, or at least negotiate, these structural limits.)

All this makes perfect dramatic sense: obstacles amplify the longing for completion, demand flexible problem solving, and accentuate the pleasure of accomplishment. You can’t complete a design right away—you have to persist. Nonetheless, total closure is always out of reach. Players often find themselves pursuing multiple goals, and simulated instabilities mean that changes introduce new disequilibriums to address. Players are rewarded with a feeling of satisfaction and the enticements of new creative challenges. A housing shortage is solved, but now more jobs are needed, and traffic must be addressed, and then . . . There are always balls in the air. The city planner never rests.

§§§

SimCity hums with paradox and bipolarity. Players are given tremendous agency and expressive power, but the world pushes back and fills in details. Self-directed play is counterbalanced by guidance and constraints. The rules are firm, but some, like fiscal limits, are negotiable. SimCity’s intricacies are made accessible through abstractions that veil its underlying rules. Players peer into the guts of the simulator, but this apparent transparency may be little more than a magician’s misdirection—opacity shrouds SimCity’s inner logic.³⁹ SimCity vibrates with contradictions: it is city and software; earnest model and trifling plaything; tool and toy; rule governed yet flexible; complicated but accessible; transparent and opaque.

From a play-centric perspective, SimCity’s paradoxes are vital to the diverse, and often contradictory, purposes to which it has been put. In 1990, a fifteen-year-old stringer for the Providence Journal ran a contest for aspiring mayors. Five candidates seeking the Democratic primary nomination participated, playing a scenario rife with “problems that will confront the next mayor of Providence.” Devised with input from a cartographer, computer scientist, and government consultant, candidates were confronted with “a high crime rate, lack of affordable housing, congested traffic, and heavy taxes.” The article was intended to appeal to a younger audience and cover computer simulation, and although the piece was never intended to have political consequences, one candidate believed that it cost her the election. Thinking she was “merely playing a game” that “had nothing to do with her political aspirations,” she made a “joke of the project.” Clumsily operating the computer, she broke the budget with new police stations and bulldozed the church—a mistake in a predominantly Catholic city.⁴⁰

In this story, SimCity was unreal—buffered from ordinary life, an inconsequential simulation—but also perhaps linked to the outcome of a real election. To Sun Microsystems, SimCity was a GIS tool and entertainment package. To OLPC, it was fun and educational. To Nintendo, it was mass-market entertainment. To Chevron and Maxis’s venture capitalists, it was a harbinger of the potential of computer simulation to train and entertain. To the Santa Fe Institute, it linked high-flown science to popular entertainment, education, and business. These unexpectedly playful acts of appropriation were made possible by the open-ended and emergent nature of SimCity and the supple quality of simulations and playthings more generally. Simulations bring together two systems, a model and a represented phenomenon. An analogical relationship allows some kind of transfer to take place.⁴¹ Play too establishes a porous boundary between play and not-play.

Studiously researched, SimCity offered a remarkably evocative representation of cities, GIS tools, serious simulation practices, and training simulations. And yet, flying under the cover of a game or plaything, SimCity’s politics and verisimilitude elude critique. Such liminality places SimCity in good stead among other play phenomena. In this light, SimCity’s flexible rules are eminently reasonable, as is the porous boundary between fantasy and reality. The ultimate manifestation of SimCity’s playfulness and holding power can be seen in the broad reach of the SimCity network, and the sprawling web of actors that came together to put SimCity to a variety of purposes, harnessing the ambiguities and instabilities at its core.

Conclusion: The World in a Machine

SimCity’s Synthesis

Players, according to Will Wright, are a designer’s most important resource. Designers do not shape electronic computers as much as they sway and stoke player imagination.¹ Unlike scientists and engineers—like Vannevar Bush, John von Neumann, and Jay Forrester—whose simulations addressed practical problems, Wright saw simulation as a supple material for expression, world building, and play. Like these other simulation makers, Wright employed aggressive abstraction to turn such representations toward new expressive possibilities. But Wright’s heightened sensitivity to the role of imagination enabled him to take the sober tools of nonfiction and turn them toward exuberant, fictive world building. This orientation, moreover, helped Wright reconcile diverse, and often conflicting, simulation traditions.

The resulting synthesis undergirds SimCity’s wide-ranging appeal and holding power. Cities bop and bounce, drawing players in with the concerted jigs and conjurings of cellular automata, system dynamics, games, and more. And SimCity coyly evokes, and flexibly meets, diverse purposes: play, education, architecture, urban planning, education, science, and others. Earnestness and irreverence go hand in hand; playfulness discreetly serves the purposes of the serious minded, while studiously researched realism bestows complexity and gravitas on play.²

Simulation representations are like kits for world building. This is true even if we set aside the artifacts, like software, that support simulation making. This is because simulation representations are fundamentally analogical: they offer domains for analogizing with. System dynamics offers the domain of levels and rates; cellular automata offer a computational lattice. To deploy these, one establishes an analogy between the model domain and the domain to be represented. And analogies, through their variegated forms—a picture, a physical model, a symbolic expression—welcome diverse minds. For some, the right representation will “stir their souls like the memories of childhood.”³ SimCity offers a specific and vivid domain, a city construction set, that can be analogized to various cities: Berlin, San Francisco, or wherever you imagine. Every analogy depends on a particular conceptual structuring of two domains: it highlights certain elements while backgrounding others. Analogies proffer worldviews.

Simulation representations prescribe certain kinds of worlds (for certain kinds of people).⁴ SimCity reconciled two very different world schema: top-down numerical aggregates (from system dynamics) with a roiling bottom-up space (from cellular automata). Whereas students role-play, negotiate, and codesign Doreen Gehry Nelson’s city simulations, SimCity is populated by automata; people are merely numbers and formulas. This has important implications. As an all-powerful Robert Moses–like city planner, players are unencumbered by multilateral negotiation with others. SimCity prescribes player agency in other ways. Unlike the wind-up worlds of cellular automata and system dynamics, players modify the system as it runs. And while players can define their own goals, they cannot see and revise the underlying rules of the world, a possibility offered by Nelson’s simulations. SimCity’s rules are fixed and largely opaque. Stepping back, we see that simulations offer different ontologies as well as divergent models of subjectivity, agency, and legibility. But these worldviews remain, for the most part, hidden from view.

The principal critique of SimCity is that it harbors a “‘hidden curriculum.’”⁵ In this appraisal, players unconsciously naturalize invisible assumptions, many of which are problematic. Which assumptions are nefarious depends, of course, on one’s own worldview, which is the sharp point of this critique: simulations ought to make their inner workings visible and, ideally, manipulable. And this, as we have seen, is the intention (however imperfectly realized) of practitioners such as Richard Duke, Nelson, Forrester, and Alan Kay. But what does it mean to surface a simulation’s worldview? This book, taken as a whole, argues that unpacking a simulation’s worldview is no simple task.

A nuanced critique of SimCity’s opacity must recognize that its design, manual, and ancillary texts do a remarkable job explaining its operation. In regard to players unthinkingly swallowing invisible ideas, one analysis notes that SimCity, by playfully highlighting its own irreality, foregrounds the gap between reality and fiction, thus inviting critical and educational stances. This view gives players (and play) far more credit.⁶ Nonetheless, players cannot change the rules. This would have demanded an even more audacious approach (never mind that SimCity pushed consumer hardware and software design to its contemporary limits). Wright aspired to something like that with The Sims, but other priorities prevailed.

Nevertheless, at a moment when ever more of our world is devoured by obscure computational processes, this critique remains especially poignant. To critics, SimCity’s opacity is an indictment of a broader simulation culture. For Sherry Turkle, “the cultural pervasiveness of simulation” is a call to “develop a new social criticism” whose aim would be to help people understand, challenge, and rewrite simulation rules.⁷ But why stop there? In what follows, I stake out a bigger critique: SimCity exemplifies problems that haunt the house of computing down to its very foundations.⁸

Software’s Craft

Travel back in time to the cusp of modern computing. The year is 1944, and the world is engulfed in war. Powerful nations focus unprecedented centralized power on developing new engines of warfare, hoping to gain the upper hand over their adversaries. The crucible of violence, dislocation, and trauma is fueled by prodigious feats of ingenuity, yielding advances such as the atom bomb, rockets, radar, and computers—technologies that come to define the latter half of the twentieth century.

Computation supported wartime efforts such as breaking encrypted communications and the design and introduction of new weapons such as aircraft, artillery, and the atom bomb. Flight simulations allowed aerospace engineers to study their proposed designs before building them and gave pilots an opportunity to fly them, or at least models of them, without consuming precious fuel. Gunners needed the booklets whose firing tables—unique to each new artillery design—told them how to aim at targets, which required simulating bullet trajectories. Los Alamos scientists sought to understand the dynamics of imploding fissile materials so they could design the atom bomb. Such projects required extraordinarily labor-intensive calculations, performed by mathematically gifted human computers, usually women. Accelerating and automating these calculations became vital to the wartime effort.⁹

The most sophisticated automatic calculating device of World War II was one of Vannevar Bush’s differential analyzers. University of Pennsylvania engineers had already upgraded one of these mechanical analyzers with electronic components, improving its speed and accuracy ten-fold. They then undertook the development of a fully electronic machine, ENIAC, that would perform a breathtaking 5,000 operations per second. Crucially, this device, like the differential analyzer, would be reconfigurable, allowing the same machine to be flexibly repurposed to become a simulator of bullets, airplanes, implosions, or the weather. The first programmers were women, “six of the best computers”¹⁰ available. But the machine’s shape-shifting superpower would also be its greatest weakness: although ENIAC promised groundbreaking calculation speeds, configuring it would require plugging in hundreds of patch cords—a slow and painstaking affair. This was programming without software.¹¹

This bottleneck captured the interest of von Neumann, who collaborated with ENIAC’s architects on a successor design, called EDVAC, which introduced a radical idea informed by Alan Turing’s work: the computer’s electronic memory would store not just the data (e.g., numbers) of a computation but the programming itself. Storing programs in memory allowed them to be quickly loaded, modified, and stored—like any data. A program was no longer a tangled mass of wires but a mercurial pattern of bits—stored in a machine adept at manipulating such patterns. Programs could be swiftly read in from punched cards. Shape-shifting would now be as fast as any other electronic operation, and programmers would compose symbols rather than patch cables. The chameleon-like computer would now swiftly transform into whatever was desired of it—a simulator of weather, airplanes, bombs, or bouncing balls.¹²

In time, these stored programs came to be seen as just as important as, if not more than, the underlying computer hardware and came to be called “software.”¹³ And as the status accorded to programming labor ascended from mere clerical work, women programmers were gradually replaced by men.¹⁴ Engineers had long sought flexible analytical aids that could be easily reconfigured. They now had the ultimate protean machine, along with the means to direct its transformations.

The shape-shifting computer would be given form through software. Software opened the door to new, increasingly leveraged, abstractions. At first, programs were written in cryptic instruction codes. Sequences of numbers encoded the steps of a program. Programmers soon turned to symbolic notations, typically called a “pseudocode,” that “compiler” programs transformed into machine-specific instructions—a technique called “automatic coding.” As these symbolic representations flourished, they became known as languages.¹⁵

Software was invented to make computing machines more labile. Inherent to software and the stored program concept is a cleavage between the hardware that actualizes computation and the software that directs its activities. Software subtracts away the hard materiality of computation—the vacuum tubes, mercury delay lines, transistors, microprocessors, and so on—leaving programmers with an abstracted machine whose logical flow they direct through supple symbolic systems. The symbolic domain, once entered, allows the formation of new, more leveraged, symbolic representations.

All of this abstraction means that a defining characteristic of computer scientists and programmers is a dexterousness with levels of abstraction, an ability to think across and “shift levels of abstraction,” and “see things simultaneously at the low level and the high level.”¹⁶ In making their ungainly computer hardware more plastic and adaptable, the originators of the stored program succeeded beyond their wildest dreams.

Abstraction hides and reveals. In this, software functions very much like a screen. Screens hide, filter, and make disappear. When you go to a theater, the lights dim, people quiet, and the outside world is shut out. Screens also present new visions. Images are etched, printed, embroidered, or projected onto them. Something is hidden, but something new is presented as well. These two functions, hiding and showing, go hand in hand. Software does the same thing. Abstraction transmutes low-level details, like bits and bytes, into new logical realities, like colors and images. What software does, then, is obfuscate one world and produce another. Operational details are effaced, while new representations and symbolic realities are actualized.

While abstraction gives programmers and operators tremendous leverage over symbolic and material worlds, it shrouds the inner workings of computer hardware and software. Opacity goes hand in hand with abstractive power; the clockwork is concealed. This was especially apparent in the transition from electromechanical devices, whose operations were directly observable, to digital computers. Instruments like the differential analyzer had a “vividness and directness of meaning,” wrote a Bush colleague, in 1950, lamenting that “a Digital Electronic computer is bound to be a somewhat abstract affair, in which the actual computational processes are fairly deeply submerged.”¹⁷

Obfuscation challenges those who would study software. Enfolding layers of abstraction act as defensive outworks—moats, walls, ramparts, palisades—obfuscating and fending off attempts to perceive and infiltrate software’s logic, tease apart hidden relationships, interpret, and critique. Moreover, outside investigators are at a structural disadvantage, since the developers of software systems have privileged access to the closely held source code, documentation, knowledge, and practices—the blueprints of these fortifications, as it were.¹⁸ Abstraction is both the crown jewel of computing and its grotesque deformity.

This situation has vexed historians of computing for some time. If software is a potential “goldmine” for historians of technology, as Nathan Ensmenger writes, then it is also a murky labyrinth whose bewildering heterogeneity “renders software extraordinarily difficult to isolate, understand, and write about.”¹⁹ Digital media and game studies scholars, in their turn toward computational materiality, wrestle with similar issues,²⁰ not to mention policymakers, legislators, and just about anyone else interested in making sense of how computing has remade the world. Software promises tantalizing glimpses into the visions, struggles, and contexts of those who shape and use computing, but software, alas, is frustratingly hard to study.

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Building SimCity demonstrates an approach to studying software and surmounting these obstacles. This approach begins by viewing software, foremost, as a cultural practice. Doing so foregrounds the imaginative purposes people bring to computers, as well as the collective nature of this activity. From this vantage, software itself is a form of simulation; software directs the computer’s protean ability to simulate any other symbolic machine.²¹

How do people put the world in a machine? To answer this question, this book has traced the historical development of multiple simulation traditions. Close attention was paid to the forces that define and reproduce simulation cultures. This has meant studying simulation artifacts, the stories people tell about them, and the communities that foster such practices. It has meant considering their ludic and intellectual dimensions. And it has meant looking at source code and advertisements; sociocultural context, biography, and the history of ideas; and, ultimately, the ways in which simulations and simulation practitioners both reflect and affect their time and place. In what follows, I summarize the techniques Building SimCity used to study how people put the world into a machine.

Artifacts

Simulation artifacts disseminate simulation culture. John Conway’s Game of Life mesmerized multitudes and, in doing so, acted as a vector for spreading not just Life, but the practice of cellular automata. Wright’s obsession with Life led to a fluency with cellular automata (and programming), just as Pinball Construction Set familiarized him with the possibilities of simulated construction sets. Simulation artifacts manifest and reproduce the essential ideas of a simulation practice. DYNAMO, the compiler for system dynamics, made it easy to build simulations out of levels and rates. The differential analyzer was recreated around the world, reflecting its utility, and spreading the ideas and practices it entailed.

Building SimCity has charted the historical impact of multiple simulation artifacts. It has also examined how these artifacts emerged from particular sociocultural contexts, which was then reflected in their material design. The technical design of system dynamics, for instance, reflected Forrester’s background in servomechanisms and digital computing. Extensive diagrams enabled a close examination of SimCity’s software design, revealing evidence of Wright’s creative appropriations and reconciliations and how these engendered its dynamics, ludic affordances, and appeal. Deep engagement with the materiality of software was fundamental to the overall approach.²²

Discursive Practices

Simulations are accompanied by chatter about them. This discursiveness takes many forms: books, manuals, marketing materials, journal articles, conference talks, packaging, diagrams, code, and user interfaces. Countless practitioners have spread simulation ideas through their books, articles, and presentations. Forrester’s prose narratives bring his simulations to life. Urban Dynamics enabled Wright to learn about system dynamics and city simulation. Players learned about SimCity from its colorful box, manual, and marketing materials. A Scientific American article popularized the Game of Life, and the Lifeline newsletter sprang up to serve its community of enthusiasts. And enthusiasts have named a sprawling menagerie of patterns with names like glider, toad, knightship, soup, and ash. And let’s not forget the diagrams and graphical conventions that describe differential analyzer configurations and system dynamics models. All of these discursive practices co-constitute and disseminate simulation practices.²³ Attending to such stories has enabled Building SimCity to analyze the intentions, worldviews, and techniques of simulation practitioners, illuminating the rhetorical strategies used to recruit people into their imaginative worlds and world-making traditions.

Schema

Simulation practices crystallize around specific representations. System dynamics proposes the representation of levels and rates. Cellular automata offer a computational lattice. These representational schemas are like world building kits. Simulation schemas are domains for analogizing with, and simulation practices employ and disseminate such schemas. As we’ve discussed, these schema prescribe certain kinds of worlds; when we employ particular schemas, we put into action a particular perspective on what the world is, who gets to be a subject in it, and how they are constructed.

A simulation’s schema—its analogical domain—is the distillation of a worldview. Such schema are reproduced through software, of course, but also diagrams, machines, minds, prose, and presentations. Most important, schema can be tracked outside of computer programs. Building SimCity has followed the footprints of cellular automata and system dynamics back in time, before they became computerized, by attending to how their underlying schema were shaped by servomechanisms, mathematics, and biology.

Style

There are many ways to do simulation. Consider cellular automata. Equipped with the same formalisms, multiple generations of practitioners have taken this tradition in radically different directions. What began as a formal mathematical proof about self-replicating machines became a spellbinding microcosm and then a poster child for emergence. And it functioned, in Wright’s hands, as an expressive world-building material. Such changes mark important shifts in the aims, methods, ideas, and values of a simulation community. They point to changing technology, perspectives, and context. Moreover, such shifts highlight a simulation style’s particular commitments—commitments that might otherwise remain inscrutable.

Context

Why do people make simulations? From where do their ideas emerge? What are their struggles and negotiations? How do social relations, such as institutional contexts, shape simulation practice?

To study simulation, we must attend to the people who make and use them. Understanding prewar simulation means looking at the culture of early-twentieth-century engineering, and examining the narratives these engineers told about their analogs. To understand system dynamics, one must examine Forrester’s journey from servomechanisms to the Sloan School of Management, the postwar demobilization of systems expertise, and his experience with digital computing. To understand cellular automata, one must examine the motivations of people like von Neumann and Christopher Langton, who wished to fathom the essential nature of life. To understand SimCity, we must examine Will Wright and Jeff Braun’s backgrounds, how they brokered relationships with a wide range of communities, and the effect the resulting SimCity network had on Maxis, its allies, and the wider world.

Neglecting sociocultural context—the agendas, backgrounds, beliefs, histories, and relations of practitioners—means we can only guess at the purposes, origins, struggles, and negotiations of the communities that create and sustain simulations.

Design in the Wild

Edwin Hutchins introduced the phrase “cognition in the wild” to distinguish “between the laboratory, where cognition is studied in captivity, and the everyday world, where human cognition adapts to its natural surroundings.” Cognition doesn’t just happen in your head; cognition unfolds with and through the world, other people, culture, and our environment.²⁴ Design in the wild expresses a similar idea: design must also be understood in its natural habitat. But this is not, as Jennifer Whitson notes, how game development is typically described; retrospective narratives and design textbooks alike tend to steer clear of the conflicts and social complexities that characterize the messy realities of development.²⁵

I offer my account of The Sims’s development, embedded within the history of the SimCity network, as an example of design in the wild. The Sims required a complex web of tensions to be resolved, a process that fits Bruno Latour’s characterization of scientific and technological projects as fundamentally social undertakings.²⁶

The SimCity network also illustrates how simulation makers—in this case, Wright—function as interdisciplinarians. By bridging conceptual domains, the analogies underwriting simulations bridge intellectual frameworks. Doing so allows separate communities to come together and exchange ideas even if, as Peter Galison observes, their worldviews are incommensurate.²⁷ As we saw with Maxis and the SimCity network, this knitting together of ideas and communities can be fraught but also fruitful. To illuminate these dynamics, I employed Fred Turner’s concept of the network forum, which highlights how communities come together, exchange ideas and legitimacy, and synthesize new products.²⁸ From the vantage of design in the wild, simulations don’t just reflect their context; they reconfigure it.²⁹

Software, as this book has argued, is a species of simulation—the means for shaping the ultimate shape-shifting simulation machine. Software, as Michael Mahoney argues, thus illuminates how different communities have adapted the computer to suit their own purposes.³⁰ Seen through the lens of a network forum, software offers a means for communities to exchange ideas, identities, and legitimacy; negotiate worldviews; form alliances; and do battle. Put simply, software representations open channels through which separate communities can come together—if only for a short while—and collaborate.

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When Maxis crumbled in 1997, it did so because it could no longer contain the contradictory forces that emerged from its multifaceted allure. As SimCity became many things to many people, so did Maxis, and eventually Wright: entertainer, philosophical provocateur, academic actor, design icon, and business opportunity. But just as SimCity’s opacity is emblematic of computing writ large, its ambiguous allure is similarly archetypical.

Vannevar Bush, whose analytical instruments pointed the way to modern computing, identified his project with an ancient tradition. He observed that the Latin word calculus—which not only describes a kind of mathematics but gives us the words calculate and calculator—originally referred to a pebble. These humble stones were themselves multifaceted instruments of reason, commerce, play, and civics. A pebble could be a game token, a bead on an abacus, or a voting chit. SimCity’s polyvalence reflects the primeval pebble and the shape-shifting computer. It too is a plaything, a tool for thought, and a political apparatus. And it too has functioned as a muse arousing new conceptions of the world.

Perhaps Wright’s genius is best understood in how he invites play’s kaleidoscopic possibilities through his design sensibility. By surfacing the latent potentialities of simulation, itself a form of make-believe play, SimCity calls forth play’s contradictory and ambiguous potentialities.

Appendix: Reverse Diagrams

Many figures aided chapter 8’s analysis of SimCity’s source code. This appendix explains the method behind these reverse diagrams.

In studying SimCity, an early challenge was access—gaining entrée to authorial resources such as the program’s source code. Fortunately, since Electronic Arts open-sourced SimCity, a version of its code circulates in the public domain. (On account of EA stipulations, it has been retitled Micropolis, an early working title for SimCity.) In addition, the original Macintosh and PC code was made available to me, allowing me to build a fuller understanding of the original program and its evolution through triangulation.

Even with access to source code, I hit a very concrete problem: making sense of a giant wall of C code—actually, less of a wall and more of a confounding labyrinth. Much of the simulation code was originally written as assembly and translated to C, and stylistic (and actual) traces of assembly remain. More generally, the trouble with code is that it is long, sprawling, and complicated. It evolves organically. It’s hard to get an overview of and talk and think about it. Code is intended to be abstracted away and hidden from players. A single subroutine might access variables and call subroutines dispersed across multiple files. And this subroutine, in turn, might be called by other subroutines scattered throughout SimCity’s code. Of course, this fragmentation and complexity would be less perplexing to the people who built it. But the authorial consciousness that created SimCity is hard to reconstruct. Some knowledge can be recovered from the program’s organization, comments, and variable names, but the bulk of it is absent—in thought, memory, and conversation now lost to time. Further complicating matters is that elaborate software systems often evolve organically, touched over time by many hands and minds, the code a residual trace of a creative consciousness whose contents are dimly perceptible, if at all, to us. And such expert knowledge would have been socially distributed, fragmented across SimCity’s development team.

Program code raises other questions of expertise. Most people are not programmers and are therefore not skilled at interpreting code. How, then, can an interpretive book, talk, or article about software be comprehensible to a nonprogrammer? Even among programmers, expertise is fragmented. A programmer might not be fluent in a particular language or possess the domain expertise for interpreting a specific program, like proficiency in simulation, graphics, or the platform abstractions of the late 1980s’ Macintosh. By analogy, literacy alone is not sufficient for interpreting a Shakespeare play or legal document; to do the latter, you must also possess legal expertise, perhaps in a specialized subfield like California insurance law. While it might be possible to humanistically interpret software without technical mastery, such expertise certainly helps. Consider that archaeologists, in order to reason about fossilized animal remains, employ a knowledge of physiology. (It’s also worth noting that many historians of science and technology bring practitioner expertise and technical know-how to their work.)

There’s a further complication. Even for expert programmers, code can be a cumbersome representation. Developers employ many representations, like prose, diagrams, and pseudocode, to design, explain, interpret, and think about software. Raw code, in other words, isn’t always the best representation for thinking and talking about software. A major issue is that it’s hard to get an overview of. Designing or solving a labyrinth might be best done with a diagram or map that projects a confounding network of passageways into a tidy visual overview.

§§§

My solution to these problems is to borrow from the methods of software developers, who often make visual and textual descriptions of their systems. Such documentation, made to support both the inception and maintenance of complex programs, can be thought of as blueprints. In the best cases, these design documents are synoptic overviews that facilitate reflection, reasoning, and communication about proposed or actual software.

I opt for a highly visual and diagrammatic style inspired by game designer Stone Librande’s one-page design documents.¹ Librande’s highly visual approach, in turn, is inspired by architectural blueprints, Lego construction manuals, cutaway illustrations, and the kinds of infographics one finds in an Edward Tufte volume. One of Librande’s insights was to limit the description of a system (or subsystem) to a single page, resulting in figures that provide holistic overviews while simultaneously capturing fine detail. One-page diagrams encourage concision and panoptic understanding of integrated wholes, which afford, in turn, problem solving and communication.²

To support my investigation of SimCity, I reverse the usual flow of translation from design into code and produce retroactive design documentation that follows rather than precedes programming. If design documentation is a blueprint, then reverse diagrams are an archaeologist’s field notes. Employing the synoptic techniques of a designer enables me to produce representations of code that support reflection, reasoning, and discourse.³ Reverse diagrams enable me to survey complexly distributed and interlinked code, and express this territory as informationally dense and visually succinct diagrams.

Reverse diagrams accomplish two key functions: translation and mapping. As translations, they express source code as visual diagrams. Translation allows me to convert pages of cryptic symbolic constants into a visual encyclopedia of SimCity’s graphical tiles. And SimCity’s main simulation loop, which schedules and coordinates many overlapping processes, can be translated into a single diagram that gives both a clear overview and detailed specifics.

Another function of reverse diagrams is mapping. Logic that is distributed across many different source files can be drawn together into a unified overview. By reading across many source files, I built maps of how their pieces fit together. It was like solving a jigsaw puzzle and mapping a labyrinth. This map data flow diagram shows how spatial data circulate among many of SimCity’s internal representations. Such diagrams grew out of notes made while surveying the code and are the trace output of my attempt to understand how fragments of dispersed code interlink into coherent wholes. The diagrams both manifested and scaffolded my incremental understanding of SimCity’s programming.

These diagrams are more than travel guides that help shepherd you, the reader, along the trail of my argument; they are maps of a territory. Because they are synoptic representations of code, the diagrams contain detail well beyond what is needed to follow my argument. I hope that the diagrams can function as territorial maps for others, enabling alternate interpretations and journeys.⁴

Reverse diagrams allow complex and diffuse code artifacts to be embedded in prose (or a presentation or conversation) for analysis and discussion. This has multiple precedents. We saw how Jay Forrester employed figures and charts to not only design his systems, but to explain and bring system dynamics to life in books and articles. The computer science literature is teeming with mathematical formulas and diagrams that distill the essence of computational techniques so that they can be embedded in prose. Such practices are widespread in science and engineering, which employ figures to capture and embed complex subjects in discourse. My approach is in the spirit of Bruno Latour, who argues that diagrams allow scientists and engineers to reduce and embed large and complex worldly phenomena into flat, portable documents.⁵ The general method I am advancing here is appropriating these tools for humanistic purposes.

Reverse diagrams help surmount the challenges of interpreting levels of abstraction, surveying complex and fragmented code, decoding obscured authorial consciousness, a lack of expertise with code, finding appropriate representations, and embedding software into discourse. Such diagrams help us make sense of the internal organization of software.

§§§

I believe that it would be disingenuous to offer prescriptive instructions on creating reverse diagrams, since I see them as highly idiosyncratic and deeply grounded in craft. Also, I’ve only used this technique on one project. My diagrams express a particular interpretation of how SimCity’s code works, attend to aspects relevant to my project, and describe it as my mastery of graphic and information design allows. I believe the most helpful guidance I can offer to aspiring diagram makers are the diagrams themselves (to be read in conjunction with the code) and some notes describing my process (as best as I can remember).

Reverse diagramming, as a craft, builds on particular faculties: the ability to read code (and other assets that describe computer programs), interpreting the meaning of that code, relating it to other phenomena of interest (dynamic behavior, software representation, history, interaction design, and so on), and designing appealing and legible informationally dense graphics that convey these interpretations. Diagram and interpretation inform one another. For example, I built many diagrams simply to scaffold my own understanding of SimCity. As my understanding improved, I was able to refine and build more complex diagrams. It’s important to note that I already knew a great deal about SimCity’s design in a general sort of way through playing it, working with Will Wright, and reading ancillary texts (for example, interviews with him, the manual, strategy guides), which bootstrapped this entire process.

For SimCity, my diagramming process probably began with the character library. In retrospect, it was an obvious place to start. The characters are inherently visual, while the symbolic and numeric constants in code, along with their associated arithmetic operations, are not. I found myself struggling to understand the meaning of such character manipulations. A key was obviously needed; it would make the correspondences between numbers and images easy to see. Second, Don Hopkins had more or less systematically collated all the character constants into one place. (Earlier versions of SimCity lacked a complete mnemonic key, and what mnemonics existed were scattered across files.) I faced a linear sequence of graphical characters, symbolic constants, and integer values that neatly, if not completely, mapped to one another. I had only to zip those sequences—character art, code symbols, and integers—together in order to build a handy reference diagram. As I built these diagrams, certain indexical patterns popped out, first with the organization of forest and water edges, and then with the buildings and network characters. Organizing the key in a way that manifested the intrinsic structure of the character ordering allowed me to visually see, on the page, why arithmetic operations on certain map characters worked as they did. Also, I wrote some scripts that stitched characters together to make this process less tedious. These scripts became possible to write only after I understood the organization of the characters.

Once I had this character library diagram, it became easier to understand more of the program. This is because just as a spreadsheet primarily traffics in numbers and a paint program traffics in bitmaps, SimCity largely traffics in these characters. I would copy and paste bits of code into my diagrams to help me puzzle over the meaning of the code and the character set. Together, they formed a kind of puzzle whose solution I strove to make clear in diagram form. By putting bits of code into the diagram and working through the code and visuals together, the diagram functioned as scratchpaper that evolved alongside my understanding of the code, which could later be deleted, transformed, or referenced as needed. Makeshift understandings gradually coalesced and solidified, were verified against code, and settled into the diagrams you see here.

The Simulate diagram is inspired by the layout of a clock, whose hands move clockwise. In code, a modulus and switch statement select the appropriate action, and since I was already familiar with this time slicing technique, it was straightforward for me to conceive of it as a clock—a cyclical schedule of actions. From there, the circular layout became a scaffold around which I collected, elaborated, and refined notes on what was happening, when, and how often. I tried to capture the sense in which information flows around the clock face by using visual and textual annotations.

Two of the most difficult diagrams to make were Map Scan and the figure depicting data flow between map layers, largely on account of how much information I wanted to condense into a single diagram. I believe this density helps the viewer to attend to both details and overview and relate information across these two scales.⁶ Both figures involved a great deal of detective work across many different functions dispersed in multiple source files, as well as intensive visual design work. The map data flow figure was motivated by my desire to know how map data were created and consumed. The answers were hard to find and involved a lot of searching for symbols across source files. As I assembled my notes, I started building a reference diagram. As it coalesced, I used the extra space to hint at connections between maps and other parts of the simulation: between pollution and the monster and between the maps and other global variables. The temporary maps used for smoothing would have made this diagram too large to fit on a page, and would have distracted from the diagram’s main purpose, so I telescoped them into smoothing emblems. In any event, they function like subroutines, so this felt appropriate.

Set Valves presented its own challenges. Once all the elements were accounted for, the diagram was a terrible jumble. A great deal of time was spent translating functional relationships into visual ones. To make these relationships legible in overview and microview, a grid undergirds the figure: columns correspond to valves and rows to processing stages.

As with design more generally, feedback and iteration were crucial. An overview diagram was made only after Janet Murray pointed out that she didn’t understand how all the subsystem diagrams fit together. Of course, I was too deep in the weeds to perceive this shortcoming, which highlights the importance of testing the diagrams in the use cases you have in mind—as part of a presentation, a discussion, or in prose—to see how they fare. All the figures underwent numerous revisions. I originally created many of the figures for my dissertation, then completely rebuilt them for this book. This allowed me to redesign them for the book’s (tighter) physical dimensions (a challenge that also improved their concision), iterate upon them, and exercise my improved visual design skills.

For all of these diagrams, I would estimate that I spent more time spent doing graphic design than poring over code. I say this not to claim that reading, interpreting, and mapping the code was easy, but rather to underline how much effort has gone into clear presentation. Such work isn’t merely aesthetic; lucid visualization is a means to and practice of lucid cognition.

Figure Credits and Notes

p. 17: Courtesy of Doreen G. Nelson. Nelson identifies Teri Fox as the likely photographer. UCLA Library Special Collections LSC.2239, box 47, folder 1. Photograph dates and locations are pulled from available documentation (e.g., slide stamps, folder labels, corroborating photos and publications) and by consulting Nelson. These dates are generally an upper bound; they may refer not only to when a photograph was taken but when it was reproduced.

p. 19: Photos © Eames Office, LLC. All rights reserved. Eames Office photographs from UCLA Library Special Collections, LSC.2239. Clockwise from top left: Eames “Kid City” [box 37 folder 3]; Warner or Brockton Elementary, 1970–71, Eames “Kid City” [b37 f3]; Eames “Kid City” (Charles Eames) [b37 f11]; Eames “Kid City” [b37 f5] (likely Charles Eames); three photos [b41, b41, b40]; The girl on the left is the final protagonist of Boorstin’s film, Westminster 1971, Eames “Kid City” (Charles Eames) [b37 f11 and f9].

p. 20: Photos © Eames Office, LLC. All rights reserved. Eames Office photographs from UCLA Library Special Collections, LSC.2239. Clockwise from top left: Canyon County, 1977 [box 39 folder 14]; c. 1976 [b38 f3]; first city, June 1970 [b37 f4]; October 1975 [b37 f10]; convertible stadium building, Brockton or Warner, 1971–72, Eames “Kid City” [b37 f9]; Westminster, city from Boorstin’s film, Eames “Kid City” [b37 f8]; redesigned city hall, first city, Eames “Kid City” [b37 f9]; c. 1974, Eames “Kid City” [b37 f8].

p. 22: Photos © Eames Office, LLC. All rights reserved. Eames Office photographs from UCLA Library Special Collections, LSC.2239. Clockwise from top left: Westminster, also depicted in Boorstin’s film, Eames “Kid City” (possibly Charles Eames) [box 37 folders 9 and 11]; Westminster, Eames “Kid City” (Charles Eames) [b37 f3 and f11]; Westminster, 1970–71, Eames “Kid City” (possibly Charles Eames) [b37 f9]; [Mitchell, Sulphur Springs?], Eames “Kid City” [b37 f9]; Compton, 1974, possibly Eames “Kid City” [b39 f14, b38 f2]; Pasadena, 1975 [b37 f4]; Consulting architecture student, Compton, 1976 [b40]; 1970–71, Eames “Kid City” (possibly Charles Eames) [b37 f3].

p. 24: Photos © Eames Office, LLC. All rights reserved. Eames Office photographs from UCLA Library Special Collections, LSC.2239. Clockwise from top left: Compton, 1977 (Grant C. Taylor) [box 39 folder 10]; Eames “Kid City” [b37 f9]; Westminster (likely), Oct. 29, 1971 [b52 f8]; Saugus (likely), November 1974 [b37 f5]; Eames “Kid City” (Charles Eames) [b37 f5, f8, f11]; Eames “Kid City” (Charles Eames) [b37 f3, f9]; 1970–71, Eames “Kid City” [b37 f3]; 1977 (Grant C. Taylor) [b39 f10].

p. 26: Nelson, interview, January 1, 2023. Photos © Eames Office, LLC. All rights reserved. Eames Office photographs from UCLA Library Special Collections, LSC.2239. Clockwise from top: Compton, 1974 [box 40]; Brockton Ave. School’s “Helium City,” 1971–72 [b37 f9]; model classroom [b37 f5]; Warner School, 1971–72, Eames “Kid City” [b37 f9]; Compton, 1974, likely Eames “Kid City” [b38 f2].

p. 28: Photos © Eames Office, LLC. All rights reserved. Eames Office photographs from UCLA Library Special Collections, LSC.2239. Clockwise from top: learning about tensegrity, Westminster, Eames “Kid City” (Charles Eames) [box 37, folder 9]; tensegrity, 1970–71, Eames “Kid City” (Charles Eames) [b37 f3 f5 f8]; Brockton or Warner, April 1972 [b37 f5]; model classroom, May 1975 [b37 f14]; first city, student photographer, Westminster, 1970–71 [b38 f2]. Nelson, Transformations, 200, dates the student photographer sequence to 1969 (before meeting the Eameses). This likely refers to the first city, built in the 1969–1970 school year, which the Eames Office later photographed.

p. 31: Photos courtesy of Alan Kay. Printed in A. Kay, “Computers, Networks and Education.” Rachel Strickland, email, November 16, 2022, identifies the teacher as Dolores Patton.

p. 33: Photos © Eames Office, LLC. All rights reserved. Eames Office photographs from UCLA Library Special Collections, LSC.2239. Clockwise from top left: Mitchell Community Elementary? [box 39, folder 9]; students analyzing their city, Mitchell, April 1973, Eames “Kid City” [b37 f3]; Mitchell, 1976 [b49 f4]; Mitchell? [b41]; Mitchell?, November 1974, Eames “Kid City” [b41]; Mitchell? [b40].

p. 34: Film stills from Kid City © 1972 Jon Boorstin and USC. Fair use, but also courtesy of Boorstin. According to Boorstin, he shares the copyright with USC (which received the film’s NEA funding). Boorstin, emails, September 12 and November 2, 2022.

p. 36: Photos © Eames Office, LLC. All rights reserved. Eames Office photographs from UCLA Library Special Collections, LSC.2239. Clockwise from top left: [box 41]; [b39 f9]; Compton, January 1976 [b40]; Compton [b41]; Compton?, 1974, Eames “Kid City” [b37 f3]; Sulphur Springs, 1973, Eames “Kid City” (Grant C. Taylor) [b37 f3 and b39 f10].

p. 37: Photos © Eames Office, LLC. All rights reserved. Eames Office photographs from UCLA Library Special Collections, LSC.2239. Clockwise from top left: first city map, Eames “Kid City” (Charles Eames) [box 37, folders 12, 3, 11]; first city [b37 f2]; first city, Eames “Kid City” [b37 f3]; first city, 1970–1971 (likely Charles Eames) [b37 f4]. All are potentially by Charles Eames.

p. 39: Photos © Eames Office, LLC. All rights reserved. Eames Office photographs from UCLA Library Special Collections LSC.2239. Clockwise from top left: [box 37, folder 9]; (Charles Eames) [b37 f3 f11]; [b37 f3]; (Charles Eames) [b37 f3 f11]. All are possibly by Charles Eames.

p. 40: Photos © Eames Office, LLC. All rights reserved. Eames Office photographs from UCLA Library Special Collections LSC.2239. Clockwise from top left: November 1974, but probably 1971–1972 [box 37, folder 8]; 1971–1972 [b37 f9]; 1971–1972 [b37 f3]; 1974, but likely 1971–1972 [b37 f3].

p. 44: Caroline Pratt, “The Play School, An Experiment in Education,” Experimental Schools: The Play School, no. 3 (1917): 6–15.

p. 48: Redrawn from Vannevar Bush, “Structural Analysis by Electric Circuit Analogies,” Journal of the Franklin Institute 217, no. 3 (March 1934): 289–329.

p. 50: Bush, Operational Circuit Analysis, 24–25.

p. 51: Redrawn from Maxfield and Harrison, “Methods of High Quality Recording.”

p. 53: Photo courtesy MIT Museum. Schematic redrawn after Bush, “Differential Analyzer.” (Spur gear figures omitted.) See Hartree, “Mechanical Integration,” for a detailed visual explanation of the mechanics and mathematics.

p. 55: Adapted and redrawn from Bush, “Differential Analyzer.”

p. 56: After Bush, “Differential Analyzer.”

p. 58: Redrawn table from C. Shannon, “Symbolic Analysis.” For more analogical tables, see Bush, “Gimbal Stabilization” and “Structural Analysis”; Maxfield and Harrison, “Methods of High Quality Recording”; Skilling, “Electric Analog.”

p. 60: Editorial cartoon by Gaston for “They Have Gone Too Far . . .” by J. M., Simulation 5, no. 2 (August 1965): 101.

p. 81: Redrawn from Forrester, “Industrial Dynamics: A Major Breakthrough.”

p. 83: Left: Redrawn from Forrester, Industrial Dynamics. Right: Redrawn from Forrester, “Basic Concepts.”

p. 85: Renshaw and Heuston, “Monopologs.”

p. 89: Forrester, Urban Dynamics, 15.

p. 91: Forrester, Urban Dynamics, 16.

p. 94: Redrawn from W. Stevens, “Computer Is Used.”

p. 96: Redrawn from Schelling, “Dynamic Models of Segregation.”

p. 97: Redrawn from Schelling, “Dynamic Models of Segregation.”

p. 101: Redrawn from Wolpert, “Religious Experience.”

p. 117: Left: Evans, “The Romance of Automata.” Right: Riefstahl, “Automata Miniatures.”

p. 120: Left: Redrawn after Charney, Fjörtoft, and von Neumann, “Numerical Integration.” Color added. Right: von Neumann, Theory of Self-Reproducing Automata, 94. Figure redrawn after Burks, Essays on Cellular Automata, 9.

p. 121: Redrawn after Burks, Essays on Cellular Automata, 9, 31.

p. 122: Redrawn from Kemeny, “Viewed as a Machine.”

p. 124: Moore, “Artificial Living Plants.”

p. 125: Film stills from Penrose and Penrose, Automatic Mechanical Self Replication.

p. 126: Redrawn after Burks, Essays on Cellular Automata; Codd, Cellular Automata.

p. 127: Ulam, “Some Mathematical Problems Connected with Patterns of Growth.”

p. 128: Schrandt and Ulam, “Recursively Defined.”

p. 129: Schrandt and Ulam, “Recursively Defined.”

p. 131: After Gardner, “Combinations of Conway’s New Solitaire Game.”

p. 132: After Berlekamp, Conway, and Guy, Winning Ways for Your Mathematical Plays, 938.

p. 133: Reproduced with permission. Copyright © 1971 Scientific American, Inc. All rights reserved.

p. 141: Retouched from Freitas and Gilbreath, Advanced Automation.

p. 142: Figures after Holland, “Universal Computer,” whose evolutionary aims are revealed in “Iterative Circuit Computers.”

p. 143: Redrawn after Holland, Adaptation.

p. 144: Redrawn from Langton, “Artificial Life with Cellular Automata,” with Golly.

p. 145: Langton, “Computation at the Edge of Chaos.”

p. 146: Redrawn from Barricelli, “Numerical Testing of Evolution Theories: Part I.”

p. 156: Used and reprinted with permission of The MITRE Corporation © 2022. See Hurst et al., “Retrospectives I.”

p. 158: I. Sutherland, “Sketchpad.”

p. 167: Courtesy of Cynthia Solomon. Solomon et al., “History of Logo.” See Perlman, “TORTIS,” and “Using Computer Technology to Provide a Creative Learning Environment.”

p. 169: A. Kay, “Personal Computer for Children.”

p. 170: Courtesy of Xerox Corporation.

p. 176: Promotional flyer detail from http://ipdb.org. Screenshot from Wikipedia.

p. 176: MacPaint illustration by Susan Kare, based on a Japanese woodcut. Susan Kare, interview by Alex Soojung-Kim Pang, 2001.

p. 178: Screenshot courtesy of Jimmy Maher.

p. 179: Photo by Ed Kashi/VII. Meyers, “Computer Construction Worker,” uses a photo from the same shoot. Packaging scan courtesy of Chris Kohler. Designed by the art director for the Berkeley Monthly, the packaging features pinball machine parts, some of which Budge salvaged from a wrecked pinball machine. Most were fabricated by a model builder. Budge, emails, February 2016.

p. 190: Screenshot from Wikipedia.

p. 198: Emulator screenshots.

p. 203: Photo courtesy of Jeff Braun. Names courtesy of Braun and Bremer. Braun, interview, May 25, 2022.

p. 206: Photo courtesy of Jeff Braun. Names courtesy of Braun and Bremer.

p. 215: Screenshots courtesy of Richard Moss, from “SimCity to Real Girlfriend.”

p. 223: Courtesy of Jeff Braun.

p. 224: Quote from Nintendo, Urban Planning Guide. Japanese and North American character art differs slightly. Image courtesy of Nintendo.

p. 231: Photo by Andy Freeberg. Originally from L., “Maxis (1987).”

p. 238: Image downloaded from http://will-wright.com, but I believe the original source is from some of Wright’s uploaded GDC slides.

p. 240: W. Wright, “Interfacing to Microworlds.” Screenshots are not from Doornbos’s Project X prototype (which didn’t load SimCity cities), but an elaborated version of Foley’s original Dollhouse prototype, according to Doornbos, emails, February 6, 2023 and Foley, email, February 7, 2023.

p. 241: MXIS, NASDAQ. Data: Mergent.

p. 276: Left: Redrawn from Forrester, Urban Dynamics.

p. 281: Funding fire, police, and transportation below 100 percent diminishes the efficacy of fire and police departments and causes roads and rails to randomly decay into rubble.

pp. 291–292: Scan courtesy of Kevin W. Ng.

Notes

Epigraphs

1. Wendy Doniger’s summary and translation in O’Flaherty, “Inside and Outside the Mouth of God.”

2. Bostrom, “Are We Living in a Computer Simulation?”

3. Andreessen, “Why Software Is Eating the World.”

4. Kemeny, “Man Viewed as a Machine.”

5. Lem, “Seventh Sally.”

Introduction

1. Chapter 7 is the source for the material summarized here, but that chapter skips the SimCopter story.

This protest Easter egg was especially hurtful given that Maxis offered same-sex partner benefits and didn’t employ the familiar sexist tropes of computer games. Braun, interviews, May 19, 2015; February 6, 2016. For more on the fiasco, see Silberman, “Boy ‘Bimbos’” and “Boycott Maxis?”; Frauenfelder, “Secret Prankster Fund”; Masaki, “Whatever Happened to . . . .’”

2. Mahoney, “Histories of Computing(s).”

3. Andreessen, “Why Software Is Eating the World.”

4. Turing, “Computable Numbers”; Papert, Mindstorms; Turkle, “Computer as Rorschach,” and Second Self; A. Kay, “Computer Software”; Mahoney, “Histories of Computing(s),” and “What Makes the History of Software Hard.”

5. Mahoney, “Histories of Computing(s),” and “What Makes the History of Software Hard.”

6. Dreyfus, What Computers Can’t Do; McDermott, “Artificial Intelligence”; Turkle, Second Self; Suchman, Plans and Situated Actions; Agre, Computation and Human Experience; Mateas, “Interactive Drama”; Wardrip-Fruin, Expressive Processing; Zhu, “Intentional Systems.”

7. Lave and Wenger, Situated Learning.

8. Galison, “Monte Carlo Simulations,” in Galison, Image and Logic, 689–780.

9. Turkle, Second Self.

10. F. Turner, “Actor-Networking the News,” and Counterculture to Cyberculture. Turner’s theory builds on Star and Griesemer, “Institutional Ecology”; Bowker, “How to Be Universal”; Galison, Image and Logic; Burt, Brokerage and Closure.

11. Turner and Larson, “Network Celebrity.”

12. Turner employs the notion of contact languages by way of Peter Galison, whose Image and Logic (760–761) also shows how simulation and games function as flexible culture-bridging concepts.

13. Following F. Turner, Counterculture to Cyberculture, I use “Whole Earth network” to refer to a web of actors and organizations affiliated with and descended from the Whole Earth Catalog, especially Stewart Brand, Kevin Kelly, Whole Earth Review, Wired, and the Global Business Network.

14. I thank Sam Arbesman, Emily Mae Harris, Jeff Braun, and May-Li Khoe for their contributions to my analysis of SimCity’s legacy. For an exemplary critique of the SimCity franchise’s ideology, see Pedercini, “SimCities and SimCrises.”

15. Newsweek Staff, “Will Wright Likes His ‘Stupid Fun.’”

16. I did do some minor TSO prototyping (SocialWeb) that morphed into a Spore prototype (Netcity).

17. See Gingold, “Catastrophic Prototyping.”

18. For example, Latour’s Aramis.

19. Hutchins, Cognition in the Wild; Whitson, “What Can We Learn from Studio Studies?” See P. Owens, Double Fine Adventure, for an excellent account of development in the wild.

20. Bijker, Bicycles, Bakelites, and Bulbs; Mahoney, “What Makes History?”

21. My comparative-historical approach follows the example of Peterson, Playing at the World.

22. S. B. Johnson, “Three Approaches.” The closest historical analysis of Forrester’s career, to my knowledge, is Baker’s “World Processors.”

23. Starr, “Seductions of Sim.”

Chapter 1

1. Nelson, Transformations.

2. Nelson, “City Building Educational Program: A Decision-Making Approach to Education” Transformations, and Cultivating Curiosity. The first summer Smithsonian workshop, in 1971, involved children from grades 2 to 10 and functioned as a research site, while the second summer workshop, in 1972, functioned as a five-week live-in teacher training seminar, and included teachers from across the country, according to Nelson, Transformations, which gives years (1970, 1971) inconsistent with her interviews and other publications, including Open Space, “Proposal,” and Nelson, Cultivating Curiosity (1971, 1972).

3. “Children Plan Future Cities,” My Weekly Reader: News Parade, September 15, 1971. This article likely describes the summer workshop, not the spring 1971 workshop that introduced Purium.

4. Shuler, “Carmen Sandiego?”; Braun, interview, May 19, 2015; William Janeway, interview, December 7, 2021.

5. “Translation” in the sense of Latour, Science in Action, 108.

6. Michael Bremer wrote the first teacher’s guide, a pamphlet published in 1989. Bremer, interview, July 17, 2015, and his SimCity, The City Simulator: Teacher’s Guide, 1989.

Curtin had been the senior producer of Where in The World Is Carmen Sandiego? prior to joining Maxis. She joined Brøderbund after completing her graduate degree with Roy Pea at NYU. Curtin, interview, August 2, 2015; Nelson, interview, January 7, 2023. I’m grateful to Curtin for introducing me to Nelson’s work. See Rankin, “Where in the World,” for an account of Brøderbund’s educational efforts highlighting the crucial contributions of Cathy Carlston, an overlooked cofounder.

Nelson authored (or coauthored) teacher guides for SimLife (with Bremer), SimCity 2000, and SimCity 3000 (with Leslie Stoltz, unpublished). See Karakotsios and Bremer, SimLife, vii; Nelson, “City Building Education: Backwards Thinking, the Highway to Change”; Hopkins, emails, September 3, 2015; Dargahi and Bremer, SimCity 2000, 432. Nelson provided the unpublished guide in an email, July 13, 2015, and gave more context in a November 10, 2022 interview.

7. Nelson, Transformations, viii.

8. I thank Paula Te, who attended the 2016 Design Based Learning Institute at Pasadena Art Center, for this Nelson quote.

9. Nelson, Transformations, viii.

10. Nelson, interview, July 13, 2015.

11. Isenberg, Conversations with Frank Gehry.

12. Nelson, interview, July 13, 2015. According to Nelson, interview, January 7, 2023, pathway and shelter were terms from the state-mandated curricular framework. See also Nelson, Transformations; Department of City Planning, “Concept Los Angeles”; Light, Warfare to Welfare, 207. According to Nelson, Transformations, she worked on LA Goals from 1965 to 1967. Nelson, “Educational Goals,” gives more specifics.

13. Nelson and Sundt, “Changing the Architecture of Teachers’ Minds.” As described in this paper, Nelson’s orientation in these early years had much in common with Nicholson’s contemporaneous theory of loose parts. See Nicholson, “How Not to Cheat,” and “Theory of Loose Parts.”

14. Nelson, Transformations. See also Open Space, “Proposal,” and Nelson and Sundt, “Changing the Architecture of Teachers’ Minds.”

15. Nelson, “City Building Educational Program: A Decision-Making Approach to Education” and Cultivating Curiosity, 21–28.

16. Nelson, interview, January 7, 2023.

17. Nelson, City Building Education; Transformations; and Cultivating Curiosity, 266; interview, July 13, 2015.

18. See Nelson, Cultivating Curiosity, 11–13; interview, November 10, 2022; and “Educational Goals” (January 1970), a typed report labeled as an “Independent Study Project”—crossed out and corrected with “THESIS.” (The report doesn’t otherwise state that it was submitted in partial requirement of a master’s degree.) The document illustrates how Nelson’s method was influenced by and synthesized the contributions of scholars like Dewey and Bruner, the LA Goals Project, her classroom experiments, and the diverse experts she had invited into her classroom.

19. Nelson, interview, July 13, 2015. She gives a different city-heart story in Cultivating Curiosity.

20. A wealthy patron paid for the cameras and film development. Nelson, interview, July 13, 2015.

21. This disempowerment holds true today. I watched Nelson deliver this story to a roomful of teachers who unconsciously nodded in unison at this point. Field notes from Design Based Learning Institute, Los Angeles and Pasadena, California, July 24–27, 2015.

22. Nelson, interview, July 13, 2015. For Gehry’s name change, see Isenberg, Conversations with Frank Gehry.

23. Nelson, interview, January 7, 2023.

24. Nelson, interview, July 13, 2015.

25. Field notes from DBL Institute (and source of these Nelson quotes); Nelson, interview, July 13, 2015.

26. Nelson, Transformations, viii, and Cultivating Curiosity, 31; Nelson interviews, January 7, 2023. Later, when Nelson received the NEA grant, she developed an “Object Interview” protocol with actors from the Mark Taper Forum that dovetailed with this costume approach. Nelson has since dropped the costumes as a gateway to her program. See Nelson, Cultivating Curiosity, 13–20.

27. Nelson, Transformations.

28. Nelson, “City Building Educational Program.”

29. Open Space, “Proposal.” For the board presentation, see Nelson, interview, January 7, 2023. and “City Building Educational Program: A Decision-Making Approach to Education,” 39, which quotes the child mayor. “Proposal” claims that the TV networks filmed the Planning Board, but Nelson, interview, says this coverage only came afterward.

30. Open Space, “Proposal.”

31. I rely on Nelson, interview, July 13, 2015, for a summary of the documentary. See also Nelson, Cultivating Curiosity. For Synanon, see Gelder, “Charles Dederich.” For Duke’s influence on Nelson, I consult Nelson, interview; “City Building Educational Program,” 39. A grant from the Ford Foundation, which also funded work on cities in the 1960s at institutions like RAND and MIT, enabled Duke to incorporate computers into his simulations. For an overview of Duke’s work, see Duke, Gaming: The Future’s Language; “Gaming: An Emergent Discipline”; and “Origin and Evolution.” See also Duke, “APEX”; Light, “Urban Security” and Warfare to Welfare; chapter 3 of this book.

32. Duke, Gaming: The Future’s Language, xvi.

33. Duke, “Origin and Evolution,” 347–348.

34. Nelson, interview, July 13, 2015.

35. Nelson, “City Building Educational Program,” and Cultivating Curiosity. Gehry’s celebrity began to shine in 1978 with his “Gehry House.” See Mafi, “Frank Gehry.”

36. Nelson, “City Building Educational Program,” Transformations, and Cultivating Curiosity.

37. Nelson, interview, January 7, 2023.

38. Nelson, “City Building Educational Program,” 39. The Smithsonian Institute has many archival materials, such as photographs, press clippings, and video related to Nelson’s work and this workshop. See mainly accession 83–002 (SIA.FA83–002). Record Unit 324 (SIA.FARU0324) contains materials related to a Nelson program for lawyers.

39. Nelson, “City Building Educational Program.”

40. Nelson, interview, January 7, 2023; Cultivating Curiosity, 30–31.

41. Nelson, interview, November 10, 2022. See also Nelson, interview, January 7, 2023, and Nelson, Cultivating Curiosity, 30–31. For more on why Nelson’s work resonated with the Eameses, see Ray Eames, interview by Ruth Bowman, 1980.

42. Nelson, interview, January 7, 2023. In this interview Nelson carefully teased apart the three different Smithsonian engagements—spring 1971 (Purium, with Gehry) and summer 1971 and 1972—and the timeline of her early city-building career in more detail than Transformations (which seems to confuse some dates) and Cultivating Curiosity.

43. See National Endowment for the Arts and National Council on the Arts, “Annual Report Fiscal Year 1972,” 56; Nelson, interviews, July 13, 2015, and January 7, 2023. Boorstin, email, November 18, 2022, also recalls the swift arrival of his film’s funding. City Building Educational Program, “A Documentation,” details the NEA-funded expansion. While Westminster was predominantly low income and racially diverse, the new schools included Brockton Avenue Elementary in West Los Angeles (middle income, racially diverse) and Warner Avenue Elementary in Westwood (high income, all Caucasian).

44. Nelson, interview, November 10, 2022; Jon Boorstin, emails, November 2 and 18, 2022; Eames Office, “Kid City,” three-projector slide show, UCLA Special Collections, LSC.2239, multiple locations and versions (but mainly box 37). The Eames “Kid City” materials at UCLA reference three projectors but mainly seem to include materials from two projectors. Boorstin describes three projectors. The Eames Office slide show was not NEA funded.

45. Boorstin, email, November 2, 2022. Separate NEA grants funded Nelson’s program; Gehry’s preparation of City Building Educational Program, “A Documentation”; and Boorstin’s film (shot at Westminster). (The NEA didn’t fund the Eames Office’s work with Nelson.) See “A Documentation,” 2, 34, and NEA and NCA, “Annual Report Fiscal Year 1972,” 54, 56, 66. According to Boorstin, email, November 18, 2022, Charles Eames had his film shoot photographed and the fluorescent bulbs changed to full spectrum. Those images, along with the bulk of the Eames Office photographs of Nelson’s work, are archived at the Library of Congress.

46. Nelson, interviews, July 13, 2015, and November 10, 2022. See also Nelson, Cultivating Curiosity, 30–31; City Building Education, copyright page; Transformations, copyright page and acknowledgments. One such film is Olney and Nelson, City Building Education. According to Nelson, interview, July 13, 2015, Charles Eames could not put his name on the films because of his NEA relationship.

47. Annual reports for National Endowment for the Arts and National Council on the Arts, 1964 to 1977. See “Annual Report Fiscal Year 1971,” 25, for quote; “Annual Report 1977,” 9, for Charles’s years of service. Nelson, interview, November 10, 2022, explains how the slideshow fit into Charles’s work with Hanks. See also City Building Educational Program, “A Documentation,” 2. For Charles’s prior success lobbying Congress (for a national aquarium), see Demetrios, 208–213. As far as I can tell, Nelson’s work remained the sole architecture in schools program at least through the 1970s. Nelson’s NEA funding declined after Hanks and Charles’s tenures ended. For Charles’s self-identification as an architect rather than a designer, see Eames and Eames, Eames Anthology, 359.

48. This was Sulphur Springs School District. Nelson, interview, November 10, 2022; NEA, “Annual Report” (1973), 49. In 1974, the funded entity switched from Sulphur Springs to her nonprofit. For more detail on these early challenges, see City Building Educational Program, “A Documentation”; Nelson, “City Building Educational Program.”

49. Nelson, interviews, July 13, 2015 and January 7, 2023. Bobbi (Robina) Mapstone, a jill-of-all-trades, transformed Nelson’s operation when she joined in 1974, according to Nelson, interview, November 10, 2022; Mapstone, interviews, November 21, 2022, and January 6, 2023. Formerly a researcher at IBM, Mapstone had already collaborated with Charles Eames, the Smithsonian, and the Eames Office on “A Computer Perspective.” Mapstone notes that her experience collaborating with the Eames Office was surely a desirable asset. See also Nelson, Transformations, x.

50. Open Space, “Proposal”; Nelson, “City Building Educational Program.”

51. The Center for City Building Educational Programs fundraised from a variety of sources, including the NEA, which helped fund programs. See NEA and NCA, “Annual Report 1975,” 27. For advisory board and nonprofit status, see “Los Angeles of the Past, Present and Future” (1981), in UCLA Special Collections, LSC.2239, box 1, folder 5. The 1981 board also included Gordon Davidson (artistic director, Mark Taper Forum), Nick England and John Goodland (deans of respectively, music and education), Susan Hamilton (Smithsonian), Chuck W. Rusch (architecture professor), Eugene Tucker (superintendent, ABC Unified), and Ken [Kenneth A.] Tye (IMTEC). For founding date, see Nelson, Transformations, ix.

52. Nelson, City Building Education. Bruner and Fuller penned brief prefaces for her 1982, 1984, and 2021 books. See also Nelson, Cultivating Curiosity.

53. Roberta (Bobbi) Blatt, the principal, had been trained in Bruner’s “Man: A Course of Study” curriculum, which she used at the school, according to Kay, email, June 30, 2022. For MACOS, see Fitchett and Russell, “Reflecting on MACOS.” For the Open School, see Open Magnet Charter School, “School History,” accessed September 30, 2015, http://www.opencharter.org/node/20; “The Los Angeles Open Charter School,” research reports, accessed September 30, 2015, https://www2.ed.gov/pubs/EdReformStudies/EdTech/opencharter.html; Timnick, “Innovative School.”

54. A. Kay, “Personal Computer for Children”; Kay, emails, June 30, 2022.

55. For the Open School’s collaboration with Apple and Nelson, see A. Kay, “Computers, Networks and Education”; Nelson and Sundt, “Architecture of Teachers’ Minds”; and Nelson, Cultivating Curiosity, 121, 265, which recounts (along with Nelson, interviews, July 13, 2015, and January 1, 2023; and Rachel Strickland, personal communication, July 18, 2023) how Rachel Strickland found her work through SCI-Arc. See also Strickland, Rachel, “Curriculum Vitae,” October 2018, https://www.spontaneouscinema.com/wp-content/PDFS/TEXTS/STRICKLAND_Resume_2018.pdf. (Henry Lowood, email, November 4, 2022, indicates that the “Life Lab” Kay mentions is directly connected to the Santa Cruz–based Life Lab nonprofit.) Vivarium’s roots harken back to Kay’s time at Atari and his collaborator, Ann Marion. See Kay, emails, 2015, 2016, and 2022; Yaeger, “Vivarium History”; Marion, Ann R. “Resume,” accessed January 10, 2023, http://www.marionworks.com/portfolio/AMarion2page.html and “Vita,” accessed January 10, 2023, http://www.marionworks.com/portfolio/work/vita.html. See also Fenton and Beck, “Playground”; Travers, “Agar,” “Recursive Interfaces,” and “Programming with Agents”; Yaeger, “PolyWorld.”

Papert later invited Nelson to a week-long scholar-in-residence. See Nelson, Cultivating Curiosity, 265, and interview, November 10, 2022.

56. A. Kay, “Computers, Networks and Education.”

57. A. Kay, “Computers, Networks and Education”; Nelson, Cultivating Curiosity; Kay, emails, June 30 and July 1, 2022.

58. A. Kay, emails, September 30, 2015, and July 11, 2022.

59. Nelson, interviews, July 13, 2015, and November 10, 2022.

60. Wright, interview, February 2, 2016.

61. Turner and Larson, “Network Celebrity.”

62. Nelson, interview, July 13, 2015.

63. Nelson, interview, January 7, 2023, explains that she used Purium well into the 1980s (she now thinks persisting with it was a mistake). Purium comprises the first chapter of Nelson’s Transformations (10–23), published in 1984—“a plunge into the totality of City Building” (10).

64. Nelson, Transformations.

65. Nelson, Transformations, 16.

66. It is possible for the city to develop underground, which can be modeled by building on the underside of the tabletop/landscape.

67. Nelson, Transformations.

68. These roles also restore authority to the teacher who has otherwise relinquished much of the power that she normally wields. Nelson’s method leans heavily on the bond between classroom governance and city governance, whose power structures mirror one another. Teachers can exploit this conceptual transfer to simultaneously give more agency to their students while exerting control over classroom behavior. See Nelson, Transformations, 15–23, 126–147; interview, July 13, 2015.

69. Nelson, Transformations, 20.

70. Nelson, Transformations and Cultivating Curiosity, 249.

71. Dewey, Child and the Curriculum; F. Turner, Counterculture to Cyberculture; Lange, Design of Childhood.

72. Caro, Power Broker.

73. Such computer models enact the Cartesian and cybernetic project of stripping agency from life. Riskin, Restless Clock.

74. A. Kay, emails, July 11 and 12, 2022.

75. A. Kay, “Computers, Networks and Education.”

76. Forrester, Industrial Dynamics; Duke, Gaming: The Future’s Language; A. Kay, “Computers, Networks and Education.”

77. Nelson, interview, July 13, 2015. It is SimCity’s rich causality and feedback, which Nelson greatly admires, that allows players to define their own goals (as discussed in chapter 9).

78. See Mahoney, “Computing in the History of Technology”; Agre, Computation and Human Experience; Turkle, “Seeing through Computers.”

79. Laurel, Computers as Theatre; J. Murray, Hamlet on the Holodeck; Mateas, “Interactive Drama”; Zhu, “Intentional Systems.”

80. Nelson and Sundt, “Architecture of Teachers’ Minds,” vividly illustrates this argument, which builds on Galison, Image and Logic; F. Turner, Counterculture to Cyberculture.

81. For Fred Turner’s network forum theory, see F. Turner, Counterculture to Cyberculture; Turner and Larson, “Network Celebrity”; the Introduction, this book.

82. Nelson, interview, January 7, 2023.

83. Nelson and Sundt, “Architecture of Teachers’ Minds.”

Chapter 2

1. Kepler, as quoted in Gentner et al., “Analogy and Creativity,” 441. See also Kepler, Optics, whose translation differs slightly.

2. Pratt, I Learn from Children.

3. Pratt, “Play School,” and I Learn from Children; Lange, Design of Childhood, 298–200. For another precursor, see Light, States of Childhood.

4. For the philosophy of computer simulation, see Galison, “Monte Carlo Simulations,” in Image and Logic, 689–780; Keller, “Models, Simulation”; Rohrlich, “Computer Simulation”; Winsberg, “Sanctioning Models,” “Computer Simulation,” and Science in the Age of Computer Simulation; Lenhard, Küppers, and Shinn, Simulation; Humphreys, “On General Formulations”; DeLanda, Philosophy and Simulation; Ulam, “Simulation and Model Construction.”

5. Vygotsky, Mind in Society; Turkle and Papert, “Epistemological Pluralism”; Turkle, Life on the Screen.

6. Bush, “Instrumental Analysis.”

7. Oxford English Dictionary. In medicine, calculus is a hardened, stone-like deposit in the body. Schmandt-Besserat, How Writing Came About, argues that such tokens also prefigured writing systems.

8. Bush, “Instrumental Analysis.” For Kelvin’s harmonic analyzer, see Bromley, “Analog Computing Devices.”

9. Vygotsky, Mind in Society; Turkle and Papert, “Epistemological Pluralism”; Turkle, Life on the Screen.

10. I. Sutherland, “Sketchpad”; W. Sutherland, “On-Line Graphical Specification.”

11. Garfield, “Historiographs”; Shneiderman, “Emergence of Direct Manipulation.”

12. Miller, “Cognitive Revolution”; Silvers, Rerepresentation. Computer science and cognitive science developed in tandem, which explains some of the shared patterns of thought, terminology, and graphics. See Gentner and Grudin, “Evolution of Mental Metaphors”; Hutchins, Cognition in the Wild; Dumit, “Plastic Diagrams.”

13. Zhang, “Interaction of Internal and External Representations”; Zhang and Norman, “Representations.”

14. Kirsh and Maglio, “Distinguishing Epistemic from Pragmatic.”

15. Zhang and Norman, “Representational Analysis of Numeration Systems.”

16. Zhang and Norman, “Representations”; Hutchins, Cognition in the Wild; Clark, Being There. Games and puzzles often seem to be the drosophila of cognitive science, in part because they are easier to study in the lab than how cognition works “in the wild,” as Hutchins argues.

17. Zhang and Norman, “Representations.”

18. See also Oxford English Dictionary. Representation, of course, has other meanings in other discourses. In popular culture studies, it refers to how different peoples are portrayed.

19. What might, at first glance, appear to be a philosophical limitation inherent to “representation”—that they are passive objects like marks on paper, not active subjects that do things and with which we participate and collaborate with in ongoing performances, or that they must represent something—dissolves once we adopt the cognitive perspective just described.

20. Mahoney, “History of Computing”; Turkle and Papert, “Revaluation of the Concrete.”

21. This approach is heavily informed by Bret Victor’s work, especially “Media for Thinking the Unthinkable.”

22. Gibson, “Theory of Affordances.” The theory of affordances was introduced to interaction design by Don Norman. See Norman, Design of Everyday Things and “Affordance, Conventions, and Design.”

23. Quotes from Bush, “Differential Analyzer.” See also Mindell, Between Human and Machine, which offers a comprehensive account of analog developments at MIT during this time period.

24. Bush, “Instrumental Analysis.”

25. Maxfield and Harrison, “Methods of High Quality Recording.”

26. Hazen, Schurig, and Gardner, “MIT Network Analyzer.”

27. Preston, “Network Analyzer.”

28. Bush, “Instrumental Analysis.”

29. Rayleigh, “Principle of Similitude.” Rayleigh and his contemporaries—for example, Hardy, “An Application of the Principle”; Davis, “Heat and Similitude”—were concerned with changes in scale, not medium.

30. Hardy, “An Application of the Principle,” 85, source of quote. See also Bush, “Structural Analysis.”

31. Bush, “Instrumental Analysis.”

32. Hazen, “Electrical Water-Level Control.” This was built in collaboration with the Army Corps of Engineers, which later built a similar model, in Sausalito, of the San Francisco Bay (and California Delta), that is now open to the public.

33. Bush, “Structural Analysis,” 318.

34. Bush, Operational Circuit Analysis, 1, 8.

35. Bush, “Gimbal Stabilization,” 200.

36. Maxfield and Harrison, “Methods of High Quality Recording,” 493.

37. J. Turner, “Maxwell on the Method of Physical Analogy,” especially 234–235; Kargon, “Model and Analogy”; Nersessian, “‘Method of Physical Analogy.’”

38. Bush, “Structural Analysis,” 315; Mindell, Between Human and Machine, 163.

39. Rayleigh, “Principle of Similitude”; P. Stevens, Patterns in Nature; Ball, Self-Made Tapestry; West, Scale.

40. Gentner, Holyoak, and Kokinov, Analogical Mind.

41. Maxwell, “Address”; J. Turner, “Maxwell on the Method of Physical Analogy.”

42. Bush, “Instrumental Analysis.” Note that Bush stretches the concept of “dynamic similitude,” deployed here as “dynamically similar,” to describe analogs.

43. Bush, “Instrumental Analysis,” 661–662.

44. Bush, “Differential Analyzer.” The basic ideas had been worked out in earlier iterations. See Bush, Profile-Tracer; Bush and Hazen, “Integraph Solution”; Bush, Gage, and Stewart, “Continuous Integraph”; L. Owens, “Vannevar Bush”; Mindell, Between Human and Machine.

45. Bush, Gage, and Stewart, “Continuous Integraph,” 63. This paper contrasts the differential analyzer, which dealt with scientific functions directly (as graphs), to the business “computing machines” that did arithmetic and tabulation.

46. Bush, “Differential Analyzer,” 451.

47. As quoted in L. Owens, “Vannevar Bush.”

48. Bush, Pieces of the Action, 262.

49. Bush, “Differential Analyzer,” 477.

50. See Bush and Caldwell, “New Type.”

51. L. Owens, “Vannevar Bush.”

52. L. Owens, “Vannevar Bush.” The quote is originally from Brock and Price, “Squared Paper,” who quote an observer from 1908. Mensuration means measuring geometry: length, area, volume.

53. L. Owens, “Vannevar Bush.” For the “one universal language” see Anthony, Introduction to the Graphic Language, iii.

54. This argument generalizes Owens’s argument, from the differential analyzer to analogs more broadly.

55. Maxwell, “Address.” See also Kargon, “Model and Analogy.”

56. Letter from Warren Weaver to Bush, 1950, quoted in L. Owens, “Vannevar Bush.”

57. Harold Hazen, quoted in Mindell, Between Human and Machine, 163.

58. See A. Kay, “Doing with Images.”

59. Mindell and Owens draw this connection, which Building SimCity aims to flesh out with its emphasis on the history of human-computer interaction design, here and in chapter 5.

60. Bush and Caldwell, “New Type”; L. Owens, “Vannevar Bush”; Waldrop, Dream Machine; Mindell, Between Human and Machine. Mindell describes how the machine was rapidly reconfigured with punched paper tapes. Input curves were no longer traced by male graduate students. Instead, female clerical workers translated the graphs into numerical tables and then punched tape, foreshadowing the clerical labor that women later provided for computing machines. See Ensmenger, Computer Boys Take Over. Blackett and Williams, “Automatic Curve Follower,” proposed an alternative organization of labor.

61. C. Shannon, “Symbolic Analysis”; Mindell, Between Human and Machine.

62. Mindell, Between Human and Machine, 174.

63. Paynter, A Palimpsest.

64. Bush, “Differential Analyzer” and “Instrumental Analysis.”

65. Small, Analogue Alternative; Bush and Caldwell, “New Type”; Mindell, Between Human and Machine. The RDA was one such hybrid. Lowood, “Video Games in Computer Space,” notes the contemporaneity of TTL-based Pong (analog) and microprocessor-based Spacewar! (digital).

66. Bromley, “Analog Computing Devices”; Small, Analogue Alternative. Mindell, by contrast, helpfully complicates this distinction in Between Human and Machine, 10, 171–173, 318–319.

67. Mindell, Between Human and Machine, 174, 295, 303. Mindell, 318, argues that the terms analog and digital arose simultaneously.

68. Small, Analogue Alternative. For other early dichotomous framings, see von Neumann’s influential “First Draft” and “General and Logical Theory of Automata.” The transition from digital to analog wasn’t merely technical; it tracked with cultural, social, intellectual, and institutional shifts, argues L. Owens, “Where Are We Going.”

69. Bromley, “Analog Computing Devices.” See also Tympas, “Digital to Analog.”

70. From analogos, or proportionate, in Greek.

71. In biology and natural science, an analog means a functionally similar organ (Oxford English Dictionary). Chemistry has structural analogs and functional analogs.

72. Gentner, “Structure-Mapping.”

73. Gentner et al., “Analogy and Creativity.” See also Nersessian, “Method of Physical Analogy,” especially 144; J. Turner, “Maxwell on the Method of Physical Analogy.”

74. Von Neumann, for instance, uses analog to describe digital representations in the “First Draft,” 5.1, 6.2, 7.3, 8.1, cf. 12.8.

75. See chapters 1 and 10 for more on this point.

76. Smith, “Gaming in Military Training”; Watson, Double Helix; Zhang, “Internal and External Representations”; Hutchins, Cognition in the Wild; Clark, Being There; M. Johnson, Meaning of the Body.

77. Ferguson, “Mind’s Eye”; Mind’s Eye; Latour, “Visualization and Cognition”; Kirsh and Maglio, “Epistemic Benefits of Action” and “Distinguishing Epistemic from Pragmatic”; Maglio and Kirsh, “Epistemic Action”; Maglio et al., “Interactive Skill in Scrabble”; Kirsh, “Intelligent Use of Space”; Clark, Being There.

78. Maxwell, “Address”; Bush, Pieces of the Action; Turkle and Papert, “Epistemological Pluralism.”

79. Brennan, “Conversation as Direct Manipulation” and “Grounding Problem”; Clark and Brennan, “Grounding in Communication”; Donald, A Mind So Rare; Tomasello, Origins of Human Communication; J. Murray, Inventing the Medium.

80. Lave and Wenger, Situated Learning; Hutchins, Cognition in the Wild.

81. Hartree, “Mechanical Integration”; Bush and Caldwell, “New Type”; L. Owens, “Vannevar Bush”; Mindell, Between Human and Machine.

82. Hartree, “Differential Analyser”; Beard, “Construction of a Small-Scale Differential Analyser”; L. Owens, “Vannevar Bush”; Mindell, Between Human and Machine.

83. Owens, L. “Vannevar Bush.”

84. Bush, “Differential Analyzer,” 488.

85. Campbell-Kelly et al., Computer.

86. Massey et al., “Scale Differential Analyser”; Lennard-Jones, Wilkes, and Bratt, “Design of a Small Differential Analyser”; Owens, “Vannevar Bush”; Robinson, “Meccano Set Computers.”

87. This was Maurice Wilkes. See Lennard-Jones, Wilkes, and Bratt, “Design of a Small Differential Analyser”; Robinson, “Meccano Set Computers”; Campbell-Kelly et al., Computer, 81–85.

88. L. Owens, “Vannevar Bush”; Redmond and Smith, Project Whirlwind.

89. Selfridge, “Coding a General-Purpose Digital Computer Differential Analyzer”; Lesh, “Methods of Simulating a Differential Analyzer”; Wilkes, “Differential Analyzer.”

90. Computers now use a bus to flexibly interconnect various components, like memory and processors (and sometimes software entities). The term busses was also used in the 1930s to describe the wiring that interlinked components on US Navy ships. See Mindell, Between Human and Machine, 51.

91. Bromley, “Analog Computing Devices.”

92. Bush, Pieces of the Action; Owens, “Where Are We Going.” To address the economic problems posed by their expensive digital machines, practitioners also turned to a solution remarkably similar to the one used by the RDA: time-sharing.

93. L. Owens, “Vannevar Bush.”

94. Waldrop, Dream Machine. As quoted by Robert Fano. See Lee, “Vannevar E. Bush.”

95. L. Owens, “Vannevar Bush.” An irony: Warren Weaver lamented the RDA’s demise, but it had already begun to replace legible machinery with opaque electronics.

96. Bush, Pieces of the Action.

97. L. Owens, “Vannevar Bush.”

98. See, for instance, Shneiderman, “Direct Manipulation”; Suchman, Plans and Situated Actions; Brooks, “Elephants”; Hutchins, Cognition in the Wild; Clark and Brennan, “Grounding in Communication”; Agre, Computation and Human Experience; Clark, Being There. See Turkle and Papert, “Epistemological Pluralism: Styles and Voices.” Chapters 4 and 7 situate Maxis in relation to these developments.

99. Turkle and Papert, “Revaluation of the Concrete.” See Mahoney, “History of Technology”; Turkle and Papert, “Epistemological Pluralism: Styles and Voices.” Chapter 5 examines this topic in more detail.

100. See Gentner and Grudin, “Evolution of Mental Metaphors”; Dumit, “Plastic Diagrams”; Hutchins, Cognition in the Wild.

101. Bush, “As We May Think.”

102. See Barnet, “Technical Evolution of Bush’s Memex.”

103. T. Nelson, “File Structure”; Nyce and Kahn, Memex to Hypertext.

104. Bush’s essay indirectly informed the design of the Google PageRank algorithm via the Science Citation Index. See Garfield, “Science Citation Index”; Page et al., “PageRank”; Masic, “Most Influential Scientist.”

105. Engelbart, “Letter to Vannevar Bush” and “Augmenting Human Intellect”; Waldrop, Dream Machine.

106. Bush, “Instrumental Analysis.”

107. These agencies include the Office of Naval Research, the Department of Defense’s Advanced Research Projects Agency, and the National Science Foundation. See Zachary, Endless Frontier; Waldrop, Dream Machine.

Chapter 3

1. Forrester, “Feedback Control Concepts”, 54.

2. Asimov, Foundation. Sociology and the physical sciences have long influenced one another. See Ball, “Physical Modelling of Society”; Toscani, Sen, and Biswas, “Kinetic Exchange Models of Societies and Economies”; Wiggins and Jones, How Data Happened.

3. Meadows et al., Limits to Growth (1972 and 2005); Lee, “Requiem”; Forrester, “Beginning of System Dynamics” (1995), 13; Edwards, Vast Machine.

4. Meadows et al., Limits to Growth (1972).

5. Limits to Growth was coauthored by a team of four led by Dennis Meadows that built on Forrester’s work. See K. Baker, “World Processors.”

6. Mindell, Between Human and Machine; Lane, “Power of the Bond.”

7. Systems Dynamics Society, founded in 1984; Stella, a software program introduced in 1985; and Dynamica, a publication for system dynamics papers, started in 1975, which morphed into the System Dynamics Review in 1985. See “Dynamica Digital Archive,” accessed July 6, 2023, https://systemdynamics.org/resources-old/dynamica-digital-archive/.

8. Wiener, Cybernetics, 11; Turner and Larson, “Network Celebrity.”

9. Forrester, “Urban Planning”; Mindell, Between Human and Machine, xi–xiii. See also Richardson, “Feedback Concept” and Feedback Thought.

10. My account of servomechanisms follows Mindell’s authoritative history. See Mindell, Between Human and Machine; Hazen, “Theory of Servo-Mechanisms.”

11. Mindell, Between Human and Machine, 138–168.

12. Hazen, “Theory of Servo-Mechanisms.”

13. Bush, Gage, and Stewart, “Continuous Integraph,” 64.

14. Mindell, Between Human and Machine, 145, 164.

15. Brown had also worked on the network analyzer and an automatic curve follower for the differential analyzer, as well as the cinema integraph. See Mindell, Between Human and Machine.

16. Mindell, Between Human and Machine. Mindell uses the terms perception, integration, and articulation.

17. Lane, “Power of the Bond.”

18. Forrester, “From the Ranch,” 339–340, and “Beginning of System Dynamics” (1989), 3; Lane, “Power of the Bond”; K. Baker, “World Processors.”

19. Mindell, Between Human and Machine.

20. Redmond and Smith, Project Whirlwind.

21. Redmond and Smith, Project Whirlwind; Edwards, Closed World; Zachary, Endless Frontier; Mindell, Between Human and Machine.

22. Mindell, Between Human and Machine, 317.

23. S. B. Johnson, “Three Approaches.” See also Mindell, Between Human and Machine.

24. McDougall, Heavens and the Earth; Rhodes, Making of the Atomic Bomb.

25. Edwards, The Closed World.

26. Engelbart, “Augmenting Human Intellect,” 48–55, and “Letter to Vannevar Bush.”

27. S. B. Johnson, “Three Approaches”; Jardini, “Blue Yonder”; Light, “Urban Security,” and Warfare to Welfare.

28. Forrester, “Beginning of System Dynamics” (1995), 8; “Personal View of the First Fifty Years.” This renaming happened in 1964, according to Institute Archives, MIT Libraries, “Sloan School of Management,” accessed August 10, 2022, https://libraries.mit.edu/mithistory/research/schools-and-departments/sloan-school-of-management/.

29. Forrester, “From the Ranch,” 344.

30. Forrester, “Beginning of System Dynamics” (1995), 9.

31. Forrester, “Beginning of System Dynamics” (1995).

32. Forrester, “Personal View of the First Fifty Years,” 347.

33. Forrester, “Beginning of System Dynamics” (1995), 9–10.

34. Forrester, “Personal View of the First Fifty Years,” 347.

35. Forrester, “Dynamic Models.”

36. Forrester, “Impact of Feedback Control Concepts,” 60.

37. Janeway, “System Dynamics.”

38. See chapter 2’s discussion of analogy.

39. Forrester, “Dynamic Models.”

40. Forrester, “Dynamic Models.”

41. Forrester, “Industrial Dynamics: A Major Breakthrough.”

42. Forrester, “Industrial Dynamics: A Major Breakthrough.”

43. Forrester, “Industrial Dynamics: A Major Breakthrough.”

44. Forrester, “Beginning of System Dynamics” (1989, 1995); Phyllis Fox, interview by Thomas Haigh, Mountain View, CA: Computer History Museum, June 2005.

45. Forrester, “Beginning of System Dynamics” (1989, 1995).

46. Word count analysis shows that Forrester prefers level and rate. State and flow are also common. Stock and valve are rare, and valve exclusively refers to the graphical symbols for rates. Ratios are consistent across “Industrial Dynamics: A Major Breakthrough”; Industrial Dynamics; Urban Dynamics; and World Dynamics.

47. Forrester, Collected Papers, 56.

48. Forrester, “Impact of Feedback Control Concepts,” 58–59.

49. Forrester, “From the Ranch,” 345, and Industrial Dynamics, vii, 353–357.

50. Forrester, Industrial Dynamics. DYNAMO development was led by Phyllis Fox, whom Forrester credits rather strangely (and later not at all). See Industrial Dynamics, ix, 369; Fox, interview by Haigh, 2005.

51. Additional symbols elaborate on this foundation, indicating delays, pipelines, constants, shared equations, and flow types (e.g., money, material, people).

52. There is one rate per flow, so rate and flow are practically synonymous.

53. Forrester, Industrial Dynamics, 81.

54. Forrester, Industrial Dynamics, e.g., 61.

55. Bush, “Differential Analyzer,” 477.

56. Forrester, Industrial Dynamics, 44.

57. Quote from Forrester, Industrial Dynamics, 44. Simple equations direct a Euler’s method style integration. See Industrial Dynamics for a lucid explanation.

58. Forrester, Industrial Dynamics, 60.

59. Forrester, Industrial Dynamics, 61, 369.

60. Forrester, Industrial Dynamics, 61.

61. Forrester, “Beginning of System Dynamics” (1995), 10.

62. Forrester, Industrial Dynamics, 357–60.

63. Renshaw and Heuston, “The Game Monopologs.”

64. Renshaw and Heuston, “The Game Monopologs.” See also Poundstone, Prisoner’s Dilemma; Galison, Image and Logic; Peterson, Playing at the World; Bhattacharya, Man from the Future.

65. Longworth, “From War-Chess.” See also Clarkson and Simon, “Simulation of Individual and Group Behavior.”

66. Bellman et al., “Multi-Person Business Game.”

67. Meier, “Explorations”; Moncreiff, “‘Sumerian Game.’” See also Willaert, “The Sumerian Game.” The game seems to have entered the folk culture of BASIC games as Hammurabi. See Ahl, 101 BASIC Computer Games, 128. Such management games are the background from which popular educational titles like Lemonade Stand emerged.

68. Duke, “Origin and Evolution.” See chapter 1.

69. Forrester, Industrial Dynamics, 358. Starr, “Seductions of Sim.”

70. Forrester, Industrial Dynamics, 360.

71. Sterman, “Modeling Managerial Behavior”; Holweg and Bicheno, “Supply Chain Simulation”; Akkermans and Dellaert, “Rediscovery of Industrial Dynamics”; Forrester, “First Fifty Years,” 347–348; Dizikes, “The Beer Game.”

72. Light, “Urban Security.”

73. The reasons for this shift are manifold. See Jardini, “Blue Yonder”; Light, “Urban Security.”

74. Pascal, Cities in Trouble; Duke, “Origin and Evolution”; Forrester, Urban Dynamics, x. Forrester also notes the use of Project MAC’s time-sharing computer service. For more on that, see Waldrop, Dream Machine, 230–231.

The Ford Foundation also helped seed the MIT Urban Systems Laboratory (founded in 1968), through which it paid for Forrester’s computer time. See Jones, “Harvard, M.I.T. Urban Studies.”

For a deep dive into the broader context of Forrester’s urban modeling research, see Light, Warfare to Welfare.

75. AP, “John Collins”; MIT News, “John F. Collins.”

76. MIT Office of Public Relations, “John F. Collins”; Forrester, “Beginning of System Dynamics” (1989, 1995). See also Light, Warfare to Welfare.

77. Forrester, Urban Dynamics, vii.

78. Forrester, “Beginning of System Dynamics” (1989), 7–8.

79. Forrester, Urban Dynamics, ix.

80. See Forrester, Urban Dynamics, x. Lewin had attended the Macy cybernetic conferences. See Richardson, “Feedback Concept,” 16.

81. Forrester, Urban Dynamics, 1.

82. Forrester, Urban Dynamics, 14.

83. Richardson, “Reflections on the Foundations of System Dynamics.”

84. Two more such figures can be found in Forrester, “Industrial Dynamics: A Major Breakthrough” and “Basic Concepts.”

85. Forrester, Urban Dynamics, 3.

86. Fox also had a MS in electrical engineering from MIT and dual undergraduate degrees in math and electrical engineering. See Fox and Haigh, “Interview.”

87. See, e.g., Forrester, Urban Dynamics, 24.

88. Forrester, Urban Dynamics, 174, 189, 208.

89. Forrester, Urban Dynamics, 67.

90. Forrester, Urban Dynamics, 107.

91. Forrester, quoted in W. Stevens, “Computer Is Used.”

92. Forrester, Urban Dynamics.

93. K. Baker, “World Processors.”

94. See Forrester, Urban Dynamics, 115, 253, for the book’s brief, dismissive, comments on race.

95. One can imagine a highly contrived and cumbersome system dynamics model of space that works against the grain of the modeling paradigm.

96. My analysis is of Schelling, “Dynamic Models of Segregation.” Schelling’s two “Models of Segregation” publications provide more context. For a playable version, see Hart and Case, “Parable of the Polygons.”

97. Schelling, “Dynamic Models of Segregation,” mainly uses the word color, but he notes that his model also works for other kinds of preferential segregation, like sex, income, and religion.

98. Schelling, “Dynamic Models of Segregation.” Schelling’s acknowledgment of pretense collapses once he moves into his quantitative model. Other differences in approach include number of variables, discrete versus continuous states, and variable versus fixed time steps.

99. Foreshadowing SimCity’s packaging, Forrester often describes cities as living things in Urban Dynamics, 15, 18, 117, 129. On the “system dynamics” title, see K. Baker, “World Processors.”

100. Forrester, World Dynamics.

101. Meadows et al., Limits to Growth (1972); Edwards, Vast Machine, 366.

102. Forrester, World Dynamics; Meadows et al., Limits to Growth (1972, 2005); Edwards, Vast Machine, 366; K. Baker, “World Processors.”

103. Forrester, World Dynamics, viii.

104. See Shubik, “Review of World Dynamics”; Naylor, “Review Essay”; Passell, Roberts, and Ross, “Limits to Growth”; D. Lee, “Requiem”; Kleiner, Age of Heretics; Edwards, Vast Machine; K. Baker, “World Processors.”

105. West, Scale.

106. That specific predictions didn’t come true, as Meadows et al., 30-Year Update, point out, is to miss the point. Kleiner, Age of Heretics, helpfully contextualizes Forrester’s method alongside other future-forecasting techniques, like Herman Kahn’s scenario-based approach.

107. Forrester, email, February 4, 2016; Wright, interview, February 8, 2016.

108. Wolpert, “Religious Experience.”

109. Forrester, “Basic Concepts.”

110. On the preference for concrete versus analytical approaches, see L. Owens, “Vannevar Bush”; Mindell, Between Human and Machine; Forrester, “Basic Concepts,” 15. See also chapter 2, this book.

111. Brown, “Foreword.”

112. See chapter 2.

113. See Hopkins, “Soul of The Sims.”

114. Forrester, Industrial Dynamics, 61; Forrester, Urban Dynamics, 14.

115. Forrester, World Dynamics, viii.

116. Turner and Larson, “Network Celebrity.” See also Galison, Image and Logic; F. Turner, Counterculture to Cyberculture. Building SimCity’s introduction and conclusion elaborate this argument.

Chapter 4

1. Jungk, A Thousand Suns, 201

2. Bird and Sherwin, American Prometheus, 388.

3. Rhodes, Making of the Atomic Bomb.

4. “J. Robert Oppenheimer, Atom Bomb Pioneer, Dies,” New York Times, February 19, 1967; Bird and Sherwin, American Prometheus.

5. Schrödinger, What Is Life?; Keller, “Physics and the Emergence of Molecular Biology.”

6. Schrödinger, What Is Life?; Keller, “Physics and the Emergence of Molecular Biology”; Watson, Double Helix.

7. See Bellman, Mathematical Problems in the Biological Sciences, and its bibliographies. For Turing’s biological work, see “Chemical Basis of Morphogenesis” and Keller, Making Sense of Life.

8. Kemeny, “Man Viewed as a Machine.”

9. Gardner, “Fantastic Combinations of Conway’s New Solitaire Game”; “Cellular Automata.” See Berlekamp, Conway, and Guy, Winning Ways for Your Mathematical Plays, 927–960.

10. Wolfram, “Statistical Mechanics.”

11. Wolfram, “Computer Software in Science,” 203.

12. Mitchell, Complexity. Cf. Keller, “Organisms, Machines, and Thunderstorms.”

13. Toffoli and Margolus, Cellular Automata Machines, 5. This chapter’s subtitle references their introduction.

14. See also Toffoli and Margolus, Cellular Automata Machines, chap. 1.

15. Keller, “Models, Simulation, and ‘Computer Experiments.’”

16. Turkle, Second Self.

17. O’Flaherty, “Inside and Outside the Mouth of God.”

18. Campbell-Kelly et al., Computer, 53. See also Aspray, John von Neumann; Light, “When Computers Were Women.”

19. Campbell-Kelly et al., Computer; Haigh et al., ENIAC in Action.

20. Goldstine, Computer from Pascal to von Neumann. It might seem strange that von Neumann wouldn’t have known about ENIAC, but the project was relatively immature, and the computer wouldn’t be operational until after the war. See Mindell, Between Human and Machine; Aspray, John von Neumann; Campbell-Kelly et al., Computer.

21. Aspray, John von Neumann.

22. Rhodes, Atomic Bomb.

23. Aspray, John von Neumann.

24. Goldstine, Pascal to von Neumann; Davis, Universal Computer; Phyllis Fox, interview by Thomas Haigh, 2005. Part of the apparent naiveté (Mindell, Between Human and Machine) may be due to engineering expedience (Aspray, John von Neumann). For an authoritative historiography of ENIAC, see Haigh et al., ENIAC in Action.

25. Goldstine, Pascal to von Neumann; Davis, Universal Computer. Haigh and Priestley, by arguing, in “Turing’s Universal Machine,” that Turing’s work was “irrelevant” to EDVAC, overshoot their mark: correcting the misconception that Turing invented the modern computer.

26. Bush, “Instrumental Analysis”; Mindell, Between Human and Machine; chap. 2.

27. M. Davis, Universal Computer, 8. For Leibniz’s “optical instrument” quote, see Gödel, Collected Works, vol. 2, 140–141.

28. M. Davis, Universal Computer. See also Goldstine, Pascal to von Neumann.

29. M. Davis, Universal Computer, 93.

30. M. Davis, Universal Computer; Turing, “Computable Numbers.”

31. von Neumann, “First Draft”; Goldstine, Pascal to von Neumann; Aspray, John von Neumann; M. Davis, Universal Computer; Campbell-Kelly et al., Computer.

32. Turing, “Computable Numbers”; von Neumann, “First Draft,” especially sections 1 and 2; M. Davis, Universal Computer.

33. M. Davis, Universal Computer.

34. von Neumann, “First Draft,” 2.3, 2.4.

35. Chun, “Enduring Ephemeral”; Haigh et al., ENIAC in Action.

36. von Neumann, “First Draft,” sections 4, 2.6.

37. McCulloch and Pitts, “Logical Calculus”; von Neumann, “First Draft”; Davis, M. Universal Computer.

38. McCulloch and Pitts, “Logical Calculus,” 129.

39. McCulloch, Embodiments of Mind, 159.

40. McCulloch and Pitts, “Logical Calculus,” 132, referencing Sir Charles Sherrington. See McCulloch, “Den of the Metaphysician,” 18.

41. von Neumann, “First Draft,” 4.1.

42. von Neumann, “First Draft,” 4.2.

43. See also Aspray, John von Neumann.

44. Campbell-Kelly et al., Computer. Turing soon drafted his own computer design called ACE. See M. Davis, Universal Computer; Copeland, Essential Turing.

45. For examples of early computer programmers simulating analyzers, see Selfridge, “Differential Analyzer,” and Aspray, John von Neumann, 167, which describes a simulated network analyzer.

46. With the caveat that no real computer can ever have infinite memory.

47. M. Davis, Universal Computer, 185.

48. Ryan, “Guy Makes a Minecraft-Playing Computer in Minecraft.”

49. Hopper and Mauchly, “Programming Techniques,” 471, 473; Hopper, “Automatic Coding”; Nofre, Priestley, and Alberts, “When Technology Became Language.”

50. This turn was part of a broader cybernetic conflation of organisms and machines. Some, like von Neumann (e.g., “Theory of Automata,” 289), noted the imaginative leap. For Descartes, automata, and cybernetics, see Riskin, Restless Clock.

51. Aspray, John von Neumann.

52. Oxford English Dictionary.

53. Riskin, Restless Clock.

54. Freeland, “Tit-Tat-To Machine”; Evans, “The Romance of Automata.”

55. Turkle, “Subjective Computer.”

56. Riskin, Restless Clock.

57. Von Neumann, “Theory of Automata,” makes reference to the increasing role of automata in mathematics and natural sciences, describing Turing’s “automatic machine” as an “automaton,” even though Turing, “Computable Numbers,” does not use the terms automata or automaton. Automata is now a conventional term in computer science. Did von Neumann’s work initiate or give force to this usage? My literature search for pre-1948 uses of automata and automaton reveals, instead, the kind of machines Riskin discusses in Restless Clock, giving credence to this hypothesis.

58. von Neumann, “Theory of Automata,” 1–2. Aspray, John von Neumann, 189, writes that these ideas were first developed and presented in 1948 and 1946. Burks, in “Theory of Automata,” offers more chronological detail.

59. von Neumann, “Theory of Automata,” 2.

60. C. Shannon, “Von Neumann’s Contributions.”

61. von Neumann, “First Draft”; Aspray, John von Neumann.

62. von Neumann, “Theory of Automata,” 25.

63. Aspray, John von Neumann.

64. von Neumann, “Theory of Automata,” 2.

65. Aspray, John von Neumann, 197.

66. von Neumann, “Theory of Automata,” 29.

67. This description wouldn’t be inscribed on paper tape, but “constructed of rigid elements” whose arrangement encoded information. See von Neumann, Theory of Self-Reproducing Automata, 83–84, 306.

68. von Neumann, “Theory of Automata,” 83.

69. von Neumann, “Theory of Automata,” 15, 25. Vichniac, “Simulating Physics,” notes that this idea anticipated the discovery of DNA’s role in reproduction.

70. Goldstine, Pascal to von Neumann.

71. Goldstine, Pascal to von Neumann, 278; Ulam, “Random Processes and Transformations.”

72. Ulam, “Random Processes and Transformations.” Ulam describes the lattice formulation “considered by von Neumann and the author.” For Ulam’s contributions, see also von Neumann, Theory of Self-Reproducing Automata, 94, 102.

73. Ulam, “Random Processes and Transformations”; Aspray, John von Neumann; Dyson, Darwin among the Machines; Edwards, Vast Machine.

74. For Monte Carlo simulation’s complication of theoretical versus experimental physics, see Galison, Image and Logic.

75. Goldstine, Pascal to von Neumann, 278.

76. von Neumann, Theory of Self-Reproducing Automata, 94.

77. Von Neumann also planned continuous and probabilistic models. See von Neumann, Theory of Self-Reproducing Automata, 27, 97–99, which Burks links to Turing, “Morphogenesis”; Goldstine, Pascal to von Neumann, 278. Continuous models were later developed by others—for example, MacLennan, “Continuous Spatial Automata”; Rucker, “Continuous-Valued Cellular Automata”; Rafler, “Generalization of Conway’s ‘Game of Life’”; Chan, “Lenia and Expanded Universe.”

78. Burks describes von Neumann’s adaption of EDVAC’s “logical design primitives” into a twenty-nine-state system as “ingenious” in Burks, Essays on Cellular Automata, 58–59.

79. For various reasons, it was arranged for Kemeny to write the magazine article. See von Neumann, Theory of Self-Reproducing Automata, 95; Aspray, John von Neumann, 201–202.

80. Kemeny, “Viewed as a Machine.”

81. Kemeny vacillates between putting such labels in quotes and letting them stand without qualification.

82. Kemeny, “Viewed as a Machine.” See Keller, Making Sense of Life, 277.

83. Goldstine, Pascal to von Neumann; Aspray, John von Neumann.

84. See also Block, “Statistically Composite System.”

85. Penrose and Penrose, “A Self-Reproducing Analogue”; Penrose, “Mechanics of Self-Reproduction,” “Automatic Mechanical Self-Reproduction,” and “Self-Reproducing Machines.”

86. Penrose, “Self-Reproducing Machines,” 105.

87. Jacobson, “Information, Reproduction and the Origin of Life” and “On Models of Reproduction.”

88. Jacobson, “On Models of Reproduction.”

89. Burks worked with von Neumann and Goldstine on ENIAC and the IAS computer.

90. The research lab was founded in 1949 with funding from the Burroughs Company and came to be called “The Logic of Computers Group.” See Levy, Artificial Life.

91. Grimes, “John Henry Holland.”

92. See Burks, Essays on Cellular Automata, 60–61; Codd, “Two-Dimensional Cellular Spaces” and Cellular Automata. Advised by Edward Fredkin, Edwin Banks found two- and three-state simplifications. See Banks, “Universality in Cellular Automata” and “Information Processing and Transmission in Cellular Automata.”

93. See Codd, Cellular Automata, 106–108, 110, 116; Burks, Essays on Cellular Automata, 61–64; Levy, Artificial Life, 60; Brender, “Programming System.” Although Codd designed and simulated components, he never fully specified a self-reproducing automaton.

94. Burks, Essays on Cellular Automata, 56, describes these approaches as analytical or computational.

95. Codd, Cellular Automata, 107, describes a man-machine collaboration that freed him to focus on the creative and strategic work. It even allowed him to improvise with incompletely specified transition functions.

96. Codd, Cellular Automata, 110.

97. Ulam, “Patterns of Growth.” This work was presented at a 1961 conference and outlined in Holladay and Ulam, “Some Combinatorial Problems in Patterns of Growth”; Schrandt and Ulam, “Growth of Figures.”

98. Metropolis and Ulam, “The Monte Carlo Method”; Galison, Image and Logic.

99. Ulam, “Some Mathematical Problems Connected with Patterns of Growth.”

100. Ulam, “Some Mathematical Problems Connected with Patterns of Growth,” 221, describes using a “scope” (screen). Published photographs and graphics indicate that at least for the 3D models, visualization required manual effort.

101. Schrandt and Ulam, “Recursively Defined.”

102. Schrandt and Ulam, “Recursively Defined.”

103. Schrandt and Ulam, “Recursively Defined.”

104. Galison, Image and Logic, 764–771.

105. Gardner, “Quarter-Century.”

106. Roberts, Genius at Play.

107. Gardner, “Quarter-Century.”

108. Roberts, Genius at Play. Cf. Gardner, “Quarter-Century,” which recounts Conway introducing the game in person.

109. Gardner, “Quarter-Century.”

110. Levy, Artificial Life.

111. Roberts, Genius at Play.

112. Levy, Artificial Life.

113. Roberts, Genius at Play.

114. Levy, Artificial Life, 51.

115. Levy, Artificial Life, 51. Cf. Roberts, Genius at Play; Conway, “Sexual Side of Life.”

116. Roberts, Genius at Play.

117. For the glider’s discovery, see Levy, Artificial Life, 53. See Roberts, Genius at Play, 126.

118. Gardner, “Combinations of Conway’s New Solitaire Game.”

119. If gliders could act like signals traveling down wires, it would open the door to reducing the number of states needed to build a self-reproducing automaton to two: “alive” and “dead.”

Levy, Artificial Life, 55–56, is the closest thing to a primary source for the oft-stated claim that Conway’s objective, in offering a prize, was to enlist others in his quest to embed a Turing machine inside Life. Alvy Ray Smith, who helped Gardner write the follow-up article (and interacted with Conway), believes Conway was searching for computational universality (Smith, emails, December 2023).

120. Gardner, “Cellular Automata”; Wainwright, “Conway’s Game of Life.”

121. Ulam, Adventures of a Mathematician, 285, describes Conway’s game as auguring a transformation in science and mathematics. See Galison, Image and Logic.

122. Gardner, “Quarter-Century.”

123. A. Smith, emails, December 2023 and “Cellular Automata Theory.”

124. Roberts, Genius at Play.

125. For contact language, see Galison, Image and Logic; F. Turner, Counterculture to Cyberculture; Turner and Larson, “Network Celebrity”; this book’s Introduction.

126. Wainwright, “Conway’s Game of Life.”

127. In response, Wainwright, “Life Saver,” floated the idea of a game-console-like device for playing Life.

128. See, for example, http://conwaylife.com.

129. Gardner, “Cellular Automata.”

130. L. Kay, Who Wrote the Book of Life.

131. Burks, Essays on Cellular Automata, 53; see also 52–56.

132. Toffoli, “Cellular Automata Mechanics”; Levy, Artificial Life, 61.

133. Levy, Artificial Life, quoting from Toffoli, “Cellular Automata Mechanics,” 12.

134. Toffoli, quoted in Levy, Artificial Life, 61–62.

135. See Burks, Essays on Cellular Automata, xviii–. The formalist tradition persisted; see Moore, “Machine Models of Self-Reproduction.”

136. Wolfram, “Statistical Mechanics”; Levy, Artificial Life; Ball, Self-Made Tapestry; Ganguly et al., “Survey on Cellular Automata.”

137. Gleick, Chaos; Waldrop, Complexity; Wolfram, “Statistical Mechanics.”

138. Gleick, Chaos.

139. Wolfram, “Cellular Automata as Models of Complexity,” 419.

140. Wolfram, “Statistical Mechanics,” 601.

141. Wolfram did not use the term space-time, but this representation would later be described this way; see Mitchell, Complexity. This representation was prefigured by Nils Barricelli and the 1971 Scientific American cover by Alvy Ray Smith, who knew of Barricelli’s work.

142. I simplify slightly. These codes are interpreted against a few fixed assumptions. Totalistic automata specified k (number of states) and r (radius of interaction).

143. Wolfram, New Kind of Science, 51–52.

144. Wolfram, “Models of Complexity.”

145. Wolfram, “Universality and Complexity.” For more accessible summaries, see Dewdney, “Building Computers in One Dimension”; Langton, “Edge of Chaos”; Mitchell, Complexity.

146. Mitchell, Complexity.

147. Gardner, “Cellular Automata.”

148. This claim is superficially self-evident: our universe contains universal computers (like smartphones).

149. See F. Turner, Counterculture to Cyberculture; Building SimCity’s introduction.

150. As befits a network celebrity, he later downplayed others’ contributions. See Mitchell, “Is the Universe a Universal Computer?”; Turner and Larson, “Network Celebrity.”

151. See Farmer, Toffoli, and Wolfram, Cellular Automata, and the workshop’s associated special issues, no. 1–2, of Physica D 10.

152. Keller, “Organisms, Machines, and Thunderstorms.”

153. SFI, “Annual Report,” 1988.

154. Gell-Mann, “Concept of the Institute,” 3.

155. For SFI’s origin, see Mitchell, Complexity; Gell-Mann, “Concept of the Institute”; Helmreich, Silicon Second Nature, 43–46; Pines, Emerging Syntheses in Science; E. Baker, “Ultimate Think Tank.”

156. Examples include Margolus and Toffoli, “Cellular Automata Machines,” Toffoli and Margolus, Cellular Automata Machines, and Hillis’s cellular automata-inspired Connection Machine. See also Hillis, “New Computer Architectures”; “Connection Machine: A Computer Architecture”: Computer Architecture”; and “The Connection Machine.”

157. Hayes, “Cellular Automaton”; Reiter, “Life and Death”; Toffoli and Margolus, Cellular Automata Machines.

158. Freitas and Gilbreath, Advanced Automation; Levy, Artificial Life; Bhattacharya, Man from the Future.

159. Forrest and Mitchell, “Adaptive Computation.”

160. Holland quoted in Mitchell, Complexity, 128.

161. Holland, “Iterative Circuit Computers.”

162. Holland, Adaptation. Holland published promising results a little earlier, in “Genetic Algorithms.” In von Neumann’s original research program, self-reproduction, evolution, biology, and intelligence were all considered as a piece (e.g., Shannon and McCarthy, Automata Studies). But artificial intelligence split off (e.g., McCarthy et al., “Proposal for the Dartmouth Summer Research Project”) and came to eschew the kinds of connectionist approaches, like cellular automata and neural nets, from whence it came.

163. Brooks, “Achieving Artificial Intelligence”; Pollack, “Setbacks for Artificial Intelligence”; Brooks, “Elephants Don’t Play Chess”; Suchman, Plans and Situated Actions; Hutchins, Cognition in the Wild; Olazaran, “Sociological Study of the Official History of the Perceptrons Controversy”; Agre, Computation and Human Experience; Wiggins and Jones, How Data Happened.

164. See, for example, Holland, “Outline for a Logical Theory” and “Hierarchical Descriptions.”

165. Holland, Adaptation.

166. Levy, Artificial Life.

167. Langton, “Self-Reproduction in Cellular Automata” and “Artificial Life with Cellular Automata.”

168. Physica D 10, no. 1–2.

169. Langton, Artificial Life (1989); Keller, Making Sense of Life. Smith, “Polyautomata Theory,” gives priority to an earlier conference as the first such convening.

170. Langton, Artificial Life (1989), xv–xvi.

171. Packard, “Adaptation Toward the Edge of Chaos.” See Keller, “Organisms, Machines, and Thunderstorms.”

172. Langton, “Computation at the Edge of Chaos.”

173. Langton, “Computation at the Edge of Chaos.”

174. Conway, “Sexual Side of Life.”

175. Another isolated example is Turing, “Chemical Basis of Morphogenesis.” Like von Neumann’s work on cellular automata, Turing’s work was also animated by an interest in intelligence. See Keller, Making Sense of Life, 89; Hodges, Alan Turing. Tamayo and Hartman, “Cellular Automata, Reaction-Diffusion Systems,” weaves these threads together—Turing’s morphogenesis, von Neumann’s self-reproduction, and cellular automata.

176. Goldstine, Pascal to von Neumann, 292; Aspray, John von Neumann, 169–170; Smith, “Self-Reproducing Machines”; G. Dyson, Darwin among the Machines; Taylor, “Artificial Evolution to Artificial Life”; G. Dyson, “Birth of the Computer”; F. Dyson, “Biology,” and “Nils A. Barricelli.”; G. Dyson, Turing’s Cathedral; Galloway, “Computational Image of Organization”; Kelly, “Hacker Historian George Dyson”; Hackett, “Meet the Father of Digital Life.”

177. Sagan, “Origin of Mitosing Cells”; Margulis, “Words as Battle Cries”; Gray, “Lynn Margulis and the Endosymbiont Hypothesis.”

178. Barricelli, “Numerical Testing of Evolution Theories: Part I.”

179. Barricelli, “Numerical Testing of Evolution Theories: Part I.”; G. Dyson, Darwin among the Machines; F. Dyson, “Nils A. Barricelli.” This also anticipated Tierra; see Ray, “An Approach to the Synthesis of Life.”

180. Alvy Ray Smith, who designed the February 1971 Scientific American, was aware of Barricelli’s work, and the cover was likely the best-known example of a one-dimensional cellular automata space-time diagram—before Wolfram’s work.

181. Barricelli, “Numerical Testing of Evolution,” part 2.

182. Hackett, “Meet the Father of Digital Life.”

183. For Langton’s use of the Apple II, see Levy, Artificial Life, 93–100. Smith, email, December 16, 2022, describes Barricelli’s picture-making process as “tortuous.” See also Hackett, “Father of Digital Life.”

184. Gardner, “Quarter-Century.”

185. Hayes, “Introducing a Department.”

186. Wolfram, “Computer Software in Science.”

187. Reiter, “Life and Death.” The title is, in all likelihood, an accidental nod to Brand, “Spacewar.”

188. Dennett, “Real Patterns.” See also Dennett, Intentional Stance, 37–39.

189. Eno, “The Limits of Intuition.”

190. Eno, “Generative Music”

191. Poundstone, Recursive Universe; Langton, Artificial Life (1989), xvi; Resnick, Turtles, Termites, and Traffic; Steven Johnson, Emergence. See also Turkle, Life on the Screen.

192. Toffoli, “Cellular Automata as an Alternative”; Fredkin, “Digital Mechanics”; Wolfram, New Kind of Science.

193. Levy, Artificial Life; R. Wright, “Did the Universe” and Three Scientists; Fredkin, “A New Cosmogony.” See also Zenil, Computable Universe, which includes Zuse, “Calculating Space”; Bostrom, “Computer Simulation?”; Gallimore, Alien Information Theory; and, of course, Douglas Adams, The Hitchhiker’s Guide to the Galaxy (New York: Harmony, 1979).

194. Bostrom’s compelling yet preposterous simulation argument, in “Are We Living in a Computer Simulation?,” can be read as trolling these analogical collapses. See also Turkle, Simulation and Its Discontents, 8.

195. Dewdney, “DIY Fractals.”

196. Dewdney, “Computer Microscope.” See also “Fractal Mountains” and “Wallpaper for the Mind.”

197. Dewdney, “Probing the Strange Attractions”; Gleick, Chaos. See also Crutchfield et al., “Chaos”; Keller, “Organisms, Machines, and Thunderstorms,” 15.

198. Rucker and Walker, CA Lab; Rucker, “Cellab Downloads”; Ken Karakotsios, interview, June 6, 2020.

199. Gleick, Rucker, and Autodesk, CHAOS.

200. See advertisement, Dewdney, “Invisible World,” 121.

201. Gleick, Rucker, and Autodesk, CHAOS.

202. Mandel, “CA Lab”; F. Turner, Counterculture to Cyberculture.

203. Langton, Artificial Life (1989), xxiv; Dawkins, “Evolution of Evolvability.”

204. Gleick, Chaos; Gleick, Rucker, and Autodesk, James Gleick’s CHAOS.

205. Resnick, “Lego, Logo, and Life”; chap. 5 of this book.

206. Langton and Kelly, “Toward Artificial Life”; Greene, “SimCity”; Kelly, “Perpetual Novelty”; F. Turner, Counterculture to Cyberculture, 198–199; Kevin Kelly, interview by Aaron Lammer, Longform Podcast, no. 376.

207. Poundstone, The Recursive Universe; Waldrop, “Fast, Cheap, and Out of Control,” and Complexity; Levy, “Exploring the Evolution of Artificial Life,” and Artificial Life; P. Lewis, “Personal Computers: ‘Creatures’”; Schwartz, “A-Life”; Kelly, Out of Control; Resnick, Turtles, Termites, and Traffic; Rucker, Hacker and the Ants; Steven Johnson, Emergence.

208. P. Lewis, “Personal Computers: ‘Creatures’”; Schwartz, “This Is Your A-Life”; Waldrop, Complexity.

209. Santa Fe Institute, “Annual Report” (1988), 5.

210. See chapter 7.

211. Santa Fe Institute, “Annual Report” (1988), 7.

212. Building on Galison, Image and Logic, I take up this argument in more detail in the Introduction, chapter 7, and the Conclusion.

213. Santa Fe Institute, “Annual Report” (1988), 7.

Chapter 5

1. Hanson, “Wozniak’s Breakout.” See Freeland, “An Automatic Tit-Tat-To Machine”; C. Shannon, “Game Playing Machines”; Keister, Automatic Game Playing Machine.

2. Wozniak, “Then There Was Apple.”

3. Hanson, “Wozniak’s Breakout.”

4. Wozniak, “How Steve Wozniak Wrote BASIC.”

5. Ahl, 101 BASIC Computer Games; Hanson, “Wozniak’s Breakout”; Wozniak, “Wrote BASIC.”

6. Hanson, “Wozniak’s Breakout.”

7. Lowood, “Video Games in Computer Space.” Microprocessor-based arcade games had only recently appeared and were the exception, not the norm.

8. Hanson, “Wozniak’s Breakout.”

9. Williams and Moore, “Early History.”

10. Nooney, Apple II Age.

11. Rankin, People’s History.

12. Ames, Charisma Machine.

13. See chapter 2.

14. A. Smith, Biography of the Pixel, 137–157.

15. Hurst et al., “Retrospectives I,” 20; A. Smith, Biography of the Pixel.

16. Griffith, Marksmanship Practicing Means; Carter, Light Gun; French, Electric Target Range; Sands, “Sands Mechanical Museum.” See Monteiro, Fabric of Interface, 124–127. For the historical inseparability of computing and games, see Pias, Computer Game Worlds.

17. Hurst et al., “Retrospectives I,” 21–23; A. Smith, Biography of the Pixel, 154–156.

18. See it Now, “Jay W. Forrester”; Hurst et al., “Retrospectives I,” 20.

19. See chapter 3; Mindell, Between Human and Machine, 317.

20. Waldrop, Dream Machine, 143–146; Ceruzzi, History, 127, 238. The TX computers then led to DEC and their PDP line, which then played host to MIT’s famous hacker culture. As Waldrop explains, engineers had developed a taste for interactive computing with Whirlwind but needed a less costly machine.

21. Pfeiffer, “Mouse Is Smarter”; C. Shannon, “Presentation of a Maze-Solving Machine”; Life, “Better Mouse”; Ward, “MOUSE”; Graetz, “Origin of Spacewar”; Bell, “Computer Museum Report.”

22. Machover, “Computer Graphics”; Hurst et al., “Retrospectives I,” 21; Krull, “Origin of Computer Graphics”; Llach, Builders of the Vision.

23. Llach, Builders of the Vision.

24. I. Sutherland, “Sketchpad.”

25. William Sutherland’s work paved the way for future graphical programming language efforts at RAND that combined aspects of block diagrams, flowcharts, blueprints, and conventional programming through a pen, tablet, and handwriting recognition. See W. Sutherland, “Online Graphical Specification”; Ellis, Heafner, and Sibley, “Grail Language”; “Grail Project”; Gaboury, Image Objects.

26. C. Shannon, “Theory and Design”; Mindell, Between Human and Machine, 173, 196.

27. I. Sutherland, as quoted by Alan Kay, email, November 3, 2014.

28. I. Sutherland, “Sketchpad,” 27.

29. See chapter 2 and L. Owens, “Vannevar Bush.”

30. I. Sutherland, “Sketchpad.”

31. Licklider, “Man-Computer Symbiosis”; Waldrop, Dream Machine.

32. A. Kay, “Doing with Images”; Brennan, “Conversation as Direct Manipulation,” “Conversation with and through Computers,” and “Grounding Problem”; Clark and Brennan, “Grounding in Communication”; Donald, Mind So Rare; Tomasello, Origins of Human Communication; J. Murray, “Cultural Theory of Gaming.”

33. Spacewar! ran on the PDP-1, the commercial successor to the TX-0 and TX-2. For the culture of analog, see chapter 2. For Spacewar!, see Graetz, “Spacewar!”; Kuhfeld, “Spacewar”; Brand, “Spacewar”; A. Kay, “Personal Computer for Children,” 2; T. Nelson, Computer Lib, 48, DM27, DM57; Graetz, “Origin of Spacewar”; Levy, Hackers; Lowood, “Video Games in Computer Space”; Monnens and Goldberg, “Space Odyssey.”

34. Kurtz, “BASIC Session”; Waldrop, Dream Machine. For an authoritative account of BASIC see Rankin, People’s History.

35. Kurtz, “BASIC Session”; Waldrop, Dream Machine, 190.

36. In fact, the NSF grant only referred to the operating system. See Kurtz, “BASIC Session.”

37. Kurtz, “BASIC Session.”

38. Kurtz, “BASIC Session.”

39. Ahl, 101 BASIC Computer Games.

40. Wozniak, “In the Beginning There Were User Groups” and “Then There Was Apple”; Campbell-Kelly et al., Computer.

41. F. Turner, Counterculture to Cyberculture.

42. Rankin, People’s History; Levy, Hackers.

43. Rankin, People’s History.

44. People’s Computer Company, What to Do, vi.

45. Rankin, People’s History.

46. Albrecht, “Modern-Day Medicine Show,” and My Computer Likes Me; People’s Computer Company, What to Do; Levy, Hackers; F. Turner, Counterculture to Cyberculture; Campbell-Kelly et al., Computer; Albrecht, interview, May 4, 2014; Rankin, People’s History.

47. Johnstone, Never Mind, 86.

48. Feurzeig et al., “Programming-Languages as a Conceptual Framework”; Feurzeig, “Logo Lineage,” and “Educational Technology at BBN”; Papert, Children’s Machine; Johnstone, Never Mind; Solomon et al., “History of Logo.”

49. Johnstone, Never Mind, 73–79.

50. Papert, quoted in Johnstone, Never Mind, 79.

51. Papert, quoted in Johnstone, Never Mind, 79.

52. Cynthia Solomon, email, August 25, 2022. This account differs slightly from Johnstone, Never Mind, 79–80; Solomon et al., “History of Logo.”

53. G. Miller, “Cognitive Revolution.”

54. G. Miller, “Cognitive Revolution.”

55. See Hutchins, Cognition in the Wild, 361–370.

56. Papert, Children’s Machine, 169.

57. Papert, Children’s Machine, 168.

58. Papert, Children’s Machine, 169.

59. For the link to hacker culture, see Ames, Charisma Machine. Quote from MIT Office of Public Relations, “John F. Collins,” 2.

60. Papert, Children’s Machine, 158.

61. Feurzeig, “Educational Technology at BBN.” See Suppes, “Uses of Computers in Education.”

62. See chapter 2.

63. Turkle, Second Self; Turkle and Papert, “Epistemological Pluralism”; Papert, Children’s Machine.

64. Papert, Children’s Machine, 180.

65. The turtle was partly inspired by William Grey Walter’s cybernetic tortoises. Screen-based “turtle graphics” came later. See Walter, “A Machine That Learns”; Papert and Solomon, “Twenty Things”; Feurzeig, “Educational Technology at BBN”; Solomon et al., “History of Logo.”

66. Papert, Children’s Machine; Johnstone, Never Mind.

67. Papert, Mindstorms.

68. Bush, Pieces of the Action, 262.

69. Cf. Sutherland, “Head-Mounted.”

70. See chapter 2.

71. Papert and Solomon, “Twenty Things”; Johnstone, Never Mind.

72. Papert, “Teaching Children Thinking,” 1–1.

73. Johnstone, Never Mind, 91, 108–16; Lange, Design of Childhood, 174–175; chapter 1, this book. For the revival of progressive educational ideas, see Lange and Johnstone.

74. Papert, Children’s Machine, 142.

75. Papert, Children’s Machine, 139.

76. Papert, Mindstorms, 6.

77. MIT Office of Public Relations, “John F. Collins.”

78. For “network celebrity” and “network forum” see F. Turner, Counterculture to Cyberculture; Turner and Larson, “Network Celebrity”; see this book’s Introduction. See also Ames, Charisma Machine.

79. Waldrop, Dream Machine.

80. A. Kay, “Personal Computer for Children.” See also Kay, “Early History.”

81. A. Kay, “User Interface.”

82. A. Kay, “Early History,” 523.

83. A. Kay, “Personal Computer for Children.”

84. A. Kay, “Early History,” 523.

85. A. Kay, “User Interface.”

86. A. Kay, “Personal Computer for Children.”

87. A. Kay, “Personal Computer for Children,” 6.

88. Waldrop, Dream Machine, 368; F. Turner, Counterculture to Cyberculture.

89. See also A. Smith, Biography of the Pixel.

90. Kay embraced Jerome Bruner’s multimodal lexicon: enactive, iconic, and symbolic. See A. Kay, “User Interface” and “Early History.”

91. Waldrop, Dream Machine, 371.

92. A. Kay and Goldberg, “Personal Dynamic Media”; A. Kay, “User Interface,” and “Early History.”

93. A. Kay, “User Interface.”

94. This recombined ideas from Sketchpad, NLS, and GRAIL. See A. Kay, “Early History.”

95. A. Kay, “User Interface.”

96. A. Kay, “Early History.” This vision of OOP is more expansive than those now commonly used.

97. Ingalls, “Smalltalk-76.”

98. A. Kay, “Early History.”

99. A. Kay, “Early History”; Adele Goldberg, oral history interview by John Mashey, May 10, 2010 (Mountain View, CA: Computer History Museum).

100. Kay and Goldberg, “Personal Dynamic Media,” 31.

101. Kay and Goldberg, “Personal Dynamic Media,” 32.

102. Kay and Goldberg, “Personal Dynamic Media,” 40.

103. Kay and Goldberg, “Personal Dynamic Media,” 36.

104. McLuhan’s “remediation” concept has been used to analyze computer interfaces (e.g., Steven Johnson, Interface Culture; Bolter and Grusin, Remediation), but Kay was consciously applying McLuhan’s ideas. See A. Kay, “User Interface.”

105. A. Kay, “Early History,” 552. See also G. Rifkin, “Seymour Papert.”

106. Pisha and Coyne, “Smart from the Start.”

107. A. Kay, “Early History,” 544–545.

108. Cf. Ames, Charisma Machine.

109. Lemmons, “Interview: Macintosh Design Team”; A. Kay, “Early History.”

110. A. Kay, “Early History,” 544.

111. Ames, Charisma Machine.

112. Rankin, People’s History. See also F. Turner, Counterculture to Cyberculture, 103–118.

113. Maloney et al., “Scratch”; Resnick et al., “Scratch”; Rankin, People’s History; Maloney, interview, October 5, 2020.

114. A. Kay, “Early History,” 560; Levy, Insanely Great; Waldrop, Dream Machine.

115. My account of Pinball is indebted to Hague, Halcyon Days as well as Kelly, “Bill Budge”; C. Kohler, “Pioneer Award”; Budge, “Postmortem”; Maher, “Pinball Wizard.”

116. Budge, “Classic Game Postmortem”; Maher, “Pinball Wizard.”

117. Budge, “Classic Game Postmortem.”

118. C. Kohler, “Pioneer Award.”

119. Hague, Halcyon Days; Budge, “Classic Game Postmortem.”

120. Budge, quoted in Hague, Halcyon Days.

121. Hague, Halcyon Days.

122. Budge, “Classic Game Postmortem.”

123. Budge, “Classic Game Postmortem.”

124. Many such titles borrowed the “Construction Set” phrase: Music Construction Set (1984), Racing Destruction Set (1985), Adventure Construction Set (1985), Tom Scheffler’s Girlfriend Construction Set (1989), and Tale Construction Set (1991). See Barton and Loguidice, “History of the Pinball Construction Set.”

125. Budge, “Classic Game Postmortem”; Wright, interview, February 2, 2016.

126. Smith et al., “Star User Interface,” 526. See also “scripting the interactor” in J. Murray, Hamlet, 79, 192, 219, 232.

127. For Gates and Basic, see Bill Gates, “An Open Letter to Hobbyists,” Homebrew Computer Club 2, no. 1 (January 31, 1976), https://en.wikipedia.org/wiki/An_Open_Letter_to_Hobbyists; Campbell-Kelly et al., Computer, 237; Levy, Hackers, 232–234; Haigh and Ceruzzi, New History, 178. For Hawkins and EA, see Stuart, “Seeing Farther”; Jager and Rafael, Company of Giants, 177–189; Carol Tice, “What Trip Hawkins Learned from His First Startup Failure That Helped Him Create Electronic Arts,” Forbes, February 14, 2020, https://www.forbes.com/sites/caroltice/2012/06/04/what-trip-hawkins-learned-from-his-first-startup-failure-helped-create-electronic-arts/. For the masculinized turn in computer games, see Dyer-Witheford and Peuter, Games of Empire. For Pinball and EA, see Hague, Halcyon Days; Kelly, “DICE 2011: Bill Budge”; Budge, “Classic Game Postmortem”; Maher, “Pinball Wizard.”

128. Shneiderman, a computer scientist, popularized the phrase and identified video games as exemplars. See Shneiderman, “Direct Manipulation,” and “Future of Interactive Systems and the Emergence of Direct Manipulation”; Hutchins, Hollan, and Norman, “Direct Manipulation Interfaces.”

129. See Pias, Computer Game Worlds, for a wide-ranging meditation on the inchoate boundaries between games, computing, and serious-minded applications. See also Shneiderman, “Direct Manipulation,” 60–62.

Chapter 6

1. Le Corbusier, Aircraft, 5, 11–12.

2. Caro, Power Broker.

3. See also Scott, Seeing Like a State; Di Palma, “Zoom: Google Earth.”

4. Spear and Wilson, Planning Commission; Seabrook, “Game Master”; Wright, interview, February 8, 2016; Miller, “Pioneer of Simulation.”

5. Kantrowitz, “Meet Mr. Simcity”; Miller, “Pioneer of Simulation”; Spear and Wilson, Official SimCity 2000 Planning Commission; Seabrook, “Game Master.”

6. Wright, interview, Feb. 8, 2016.

7. Seabrook, “Game Master.”

8. Seabrook, “Game Master”; Donovan, “Will Wright”; Ültanir, “Epistemologic Glance.”

9. Spear and Wilson, Official SimCity 2000 Planning Commission; Seabrook, “Game Master.”

10. Donovan, “Replay Interviews: Will Wright”; Wright, interview, February 8, 2016.

11. Pearce, “Sims, BattleBots”; Donovan, “Will Wright.” These games were all designed by Jim Dunnigan.

12. Peterson, Playing at the World. See also Lenoir, “All But War”; Lenoir and Lowood, “Theaters of War”; Harrigan and Kirschenbaum, Zones of Control.

13. Pearce, “God and Go”; Donovan, “Will Wright.” Justin McCormick, interview, September 30, 2021, describes his and Wright’s fascination with wargame ambiguities and mishaps.

14. Papert, Mindstorms; Seabrook, “Game Master”; Cagle, “Web 2.0 Expo Preview: Will Wright.”

15. Chapter 5.

16. Donovan, “Will Wright”; Wright, interview, Feb. 8, 2016.

17. Wright, interviews, February 8, 2016; September 26, 2022.

18. Pearce, “God and Go”; W. Wright, “Classic Game Postmortem”; Donovan, “Will Wright.”

19. Pearce, “God and Go”; Kushner, “Life Is a Game”; W. Wright, “Classic Game Postmortem”; Donovan, “Will Wright”; Wright, interview, February 8, 2016.

20. W. Wright, “Classic Game Postmortem”; Donovan, “Will Wright”; Wright, interview, February 8, 2016.

21. Wright, interview, September 26, 2022.

22. Rouse, Game Design; Cagle, “Will Wright”; Wright, interview, February 8, 2016.

23. Seabrook, “Game Master”; W. Wright, “Classic Game Postmortem”; Braun, interview, February 6, 2016; Wright, interview, February 8, 2016.

24. Zientara, “Commodore Unveils”; Rouse, Game Design, 409; W. Wright, “Classic Game Postmortem”; Spear and Wilson, Official SimCity 2000 Planning Commission, 200.

25. Commodore, Commodore 64 Programmer’s Reference; W. Wright, “Classic Game Postmortem.” Helicopters became an ongoing theme for Wright. Helicopters had always interested him. As a child, he once rode one at a state fair, and he had loved “Mutual of Omaha’s Wild Kingdom”—a television program that always ended with a helicopter chase.

26. Foley, interview, July 31, 2015.

27. W. Wright, “Classic Game Postmortem.” Despite citing cellular automata as an influence, Raid is mainly an agent-based simulation.

28. W. Wright, “Classic Game Postmortem.”

29. W. Wright, “Classic Game Postmortem.” For contemporaneous examples of custom development tools see Mechner, “Postmortem”; Budge, “Postmortem.”

30. Rouse, Game Design, 412; Donovan, “Will Wright.”

31. W. Wright, “Classic Game Postmortem.” Brøderbund titles featuring the Bungeling empire include Choplifter! (1982) and Lode Runner (1983).

32. Seabrook, “Game Master”; Donovan, “Will Wright.”

33. Rouse, Game Design, 409; W. Wright, “Classic Game Postmortem”; Donovan, “Will Wright.” Sales figures range between 750,000 and a million units depending on the source.

34. Wright acquired one for his home according to Foley, interview, July 31, 2015.

35. Trunzo, “Raid on Bungeling Bay.”

36. Compute!’s Gazette, “Raid on Bungeling Bay,” November 1984: 110–112.

37. Enter, “Raid on Bungeling Bay,” April 1985, 43.

38. W. Wright, “Classic Game Postmortem.”

39. Braun, interview, May 19, 2015; Foley, interview, July 31, 2015; Wright, interview, February 8, 2016; Wilson, Official SimCity 2000 Planning Commission, xxiii.

40. Rouse, Game Design, 410.

41. Donovan, “Will Wright.”

42. Kelly, Out of Control, 201–203.

43. Wright, interview, February 8, 2016. Wright, quoted in Kelly, Out of Control, 203.

44. Wright, quoted in Phipps, “Will Wright.” See also Levy, “Simulation versus Reality”; J. Wilson, Planning Commission, foreword.

45. The OEO was one aspect of Johnson’s Great Society. After funding dried up in the 1970s Joffe took a graduate scholarship from MIT (1974–1977).

46. Joffe played an important role in fostering SimCity by all accounts: Bremer, SimCity (User documentation), 2; J. Wilson, SimCity Planning Commission, xxiii–xxiv; Spear and Wilson, Official SimCity 2000 Planning Commission, 201; Seabrook, “Game Master”; Braun, interview, May 19, 2015; Foley, interview, July 31, 2015; Wright, interview, February 8, 2016; Joffe, interview, June 9, 2015; Joffe, emails, February 2016. Following our 2015 interview, Braun told me that “without Bruce Joffe there would be no SimCity” (personal recollection).

Wright, interview, 2016, recalls showing an early prototype to Joffe, who grew excited and brought him books he wasn’t familiar with, such as those by Jane Jacobs and Paulo Soleri. (Seabrook claims Joffe introduced Wright to Forrester’s Urban Dynamics, but this seems to be an understandable misreading of Spear and Wilson contradicted by both Joffe and Wright.) Wright disputes Joffe’s claim, also reported in Wilson, that the idea for SimCity emerged through a conversation with him.

47. Christaller, Central Places, 2, 14.

48. Wright, interview, February 8, 2016. Some of this analysis is drawn from reading SimCity’s code, which suggests some deprecated variations on this concept. For central place theory, see Ullman, “Theory of Location”; Christaller, Central Places; Haggett, “Three Pioneers”; Hottes, “Walter Christaller.”

49. Rouse, Game Design, 410.

50. Donovan, “Will Wright.”

51. Wright, interview, February 8, 2016.

52. Rouse, Game Design, 410.

53. Donovan, “Replay Interviews: Will Wright.”

54. Rouse, Game Design, 411.

55. Kelly, Out of Control, 203.

56. Braun, interview, February 6, 2016; Wright, interview, February 8, 2016. It’s not clear exactly when the renaming happened, but Braun and Wright agree that this was the title when Braun saw it.

57. Rouse, Game Design, 411; J. Wilson, SimCity Planning Commission, foreword.

58. Donovan, “Replay Interviews: Will Wright.” Joffe’s recollections and the working title suggest that Wright may have thought, at one point or another, that he might be making a tool for city planners. Joffe, interview, June 9, 2015; emails, February 2016.

59. Donovan, “Replay Interviews: Will Wright.”

60. Pearce, “Sims, BattleBots,”; Donovan, “Replay Interviews: Will Wright”; Pitts, “Will Wright Cites Pinball Construction Set”; Joffe, emails, February 2016.

61. Wright, interview, February 8, 2016.

62. W. Wright, “Classic Game Postmortem.”

63. Wright, interview, February 8, 2016.

64. W. Wright, “Classic Game Postmortem.”

65. J. Wilson, SimCity Planning Commission, xxiii–xiv; Lew, “City Planning”; Spear and Wilson, Planning Commission, 201.

66. Lem, “Seventh Sally.”

67. Lem, “Seventh Sally.” See also Clarke, Posthuman Metamorphosis, 107–114, which examines Lem’s collection in the context of cybernetics and John von Neumann’s self-reproducing automata.

68. Wright, interview, February 8, 2016.

69. In addition to the cooperating multitasking analyzed in chapter 8, SimCity originally also used preemptive multitasking to interleave simulation and interaction processing (Wright, interview, February 8, 2016). This design, which carried forward a conventional C64 interrupt-based multitasking technique to the Macintosh (Conrad, interview, March 21, 2016), would be revised for Apple’s MultiFinder and other platforms (Foley, interview, July 31, 2015; Foley, email, July 6, 2020; Wright, interview).

Some cellular automata systems, such as those used by E. F. Codd, intermixed editing and running more flexibly (see chapter 4).

70. Lem, “Seventh Sally.”

71. Wright, interview, February 8, 2016.

72. Rouse, Game Design, 415.

73. Silverman, “Win at Life.”

74. Rouse, Game Design, 415.

75. Silverman, “Win at Life.”

76. Make-believe and immersion go hand-in-hand. See J. Murray, Hamlet on the Holodeck, and Laurel, Computers As Theatre; Turkle, Simulation and Its Discontents, 6–8; chapter 9, this book.

77. Rouse, Game Design; Wright, interview, February 8, 2016. See Nintendo’s SimCity manual, Nintendo, Urban Planning Guide.

78. Rouse, Game Design, 415.

79. Rouse, Game Design, 411.

80. J. Wilson, SimCity Planning Commission, xxiv; Wright, interview, February 8, 2016. PanzerBlitz introduced the idea of scenarios and open-ended play to consumer wargames, according to Wikipedia.

81. Donovan, “Will Wright.” See J. Wilson, SimCity Planning Commission, foreword.

82. Kushner, “Life Is a Game.”

83. Wright, interview, February 8, 2016.

84. J. Wilson, SimCity Planning Commission, foreword. As in wargames, which also typically harbored multiple win conditions, the most fascinating play often emerges when things go haywire. McCormick, interview, September 30, 2021.

85. Wright, interview, February 8, 2016.

86. Rouse, Game Design, 411.

87. Wright, interview, February 8, 2016.

88. Donovan, “Will Wright.”

89. Braun, interviews, May 19, 2015; February 6, 2016. For California Museum (and associated quote), see Time, “Museums,” September 4, 1964. For more on the Eames Office Mathematica exhibit, see Demetrios, Eames Primer, 179–189. Ice Bag emerged from a curatorial project that matched artists with technology corporations, in this case Disney’s amusement park design and fabrication arm. See Los Angeles County Museum of Art, “Report on the Art and Technology Program.”

90. Braun later amassed Dick manuscripts and donated his collection to California State University, Fullerton (interview, May 19, 2015). See Ulin, “Inside the Archives.” See also Bittanti, “Do Game Designers Dream,” for a speculative discussion of Dick’s influence on Wright that can also be read as an explanation of why Braun was inclined to appreciate Wright’s work.

91. Braun, interview, May 19, 2015. Cf. Greco, “Looking for Mr. Right.”

92. Braun, interview, May 19, 2015.

93. Braun, interview, May 19, 2015; Foley, interview, July 31, 2015; Redmer, emails, July 2022. Foley had play-tested Raid and would later join Maxis (interview). Redmer and Kilham would have headed Maxis’s production if they hadn’t gone to Lucasfilm Games, according to Braun, interview, May 25, 2022; Redmer, emails. Bremer, SimCity (User documentation), 2, acknowledges Redmer and Kilham.

94. Braun, interview, May 19, 2015. For dedication, see Bremer, SimCity (User documentation), 2.

95. Eisenhart, “Building SimEmpires”; Braun, interview, May 19, 2015.

96. Braun, interview, May 19, 2015; Rouse, Game Design, 411.

97. Braun, interview, May 19, 2015. Brøderbund did publish nongames like The Print Shop; City Planner’s liminality was the issue.

98. Incidentally, Daglow, earlier in his career, had produced an early simulation-style game, Utopia (1981). See Wallis, “Playing Catch Up”; Barton and Loguidice, “History.”

99. Braun, interviews, May 19, 2015, and February 6, 2016; Wright, interview, February 8, 2016. See also Electronic Arts, “Form S-4.”

100. Braun, interview, February 6, 2016; Braun, emails, February 10, 2016. See McCormick, “SimCity & Maxis”; McCormick, interview, Sep. 30, 2021.

101. Rouse, Game Design, 411; Braun, interview, May 19, 2015; Wright, interview, February 8, 2016.

102. Michael Bremer, interview, July 17, 2015, which also recounts a miserable late-night work session with Richard Bagel, when the two of them were in their underwear because of the heat. Bremer recalls telling Bagel, “Rick, remember this night. Someday, we’re gonna think of this as the good old days.”

103. Braun, interview, May 19, 2015.

104. Cambron, “Will Wright.”

105. Bayless had made shareware fonts with Calligrapher.

106. Bayless, interview, March 23, 2016.

107. Brett Durrett helped with the front-end programming (supplementary map drawing and editing tools). Dan Goldman did the PC port. Brian Conrad, who initially approached Braun with his own game proposal, completed the Amiga port (begun by Brian Witt), introducing OS-native multitasking (Conrad, interview, March 21, 2016). Mick Foley worked on the Bern and Detroit scenarios, and recalls Wright giving him a map of Detroit to work from (interview, July 31, 2015). See Bremer, SimCity (User documentation), 2, which does not credit Foley.

108. Rouse, Game Design; Wright, “Classic Game Postmortem.”

109. Wright, quoted in Donovan, “Will Wright.”

110. The C64 version indicates density by filling the eight perimeter squares of a zone.

111. Braun, interview, May 19, 2015; Wright, interview, June 1, 2022; and Bremer, interview, July 17, 2015, who didn’t realize that suggesting the name meant it wouldn’t belong to him anymore. His introduction to technical writing came at his previous job, where his boss had said, “You wanna be a writer? Write a manual.”

112. Braun, interview, May 19, 2015. Braun first incorporates, alone, on August 10, 1988. See Braun, “Articles of Incorporation of Maxis Software.” According to Braun, this incorporation happened about six months after he began working with Wright.

113. Braun, interview, May 19, 2015.

114. Ellis, emails, 2022.

115. Dynae, “Will Wright”; Braun, interviews, May 19, 2015, and February 6, 2016; Wright, interview, February 8, 2016. Figures from Braun, 2015 interview, and their revised partnership agreement, Braun and Wright, “Maxis Partnership Agreement” (1990), when Braun bumps Wright to a 70/30 share after SimCity’s successful launch, according to Braun, interviews. According to “Maxis Partnership Agreement,” the initial partnership was an oral agreement.

116. Maxis assumed the manufacturing risk, but favorable terms from both Brøderbund and an upstart software fulfillment manufacturer enabled them to pass along the risk to the manufacturer while keeping 80 percent of revenue. Braun, interview, May 19, 2015.

117. Although Brøderbund plays the part of a foil in this story, both Braun and Wright are grateful to Brøderbund’s founders for their support and mentorship. Braun, interview, May 19, 2015; Wright, “Classic Game Postmortem.”

118. Greco, “Looking for Mr. Right”; Braun, interview, May 19, 2015.

119. Braun, interview, May 19, 2015; Conrad, interview, March 21, 2016; Wright, interview, February 8, 2016; and reviews, like Barol, “Big Fun,” which begin in May. Many ports followed, developed both in-house and with external developers, including for Super Nintendo (1991). For unreleased prototype ports, see Cifaldi, “Recovering Nintendo’s Lost SimCity for the NES”; Call-A.P.P.L.E., “The Long Fabled SimCity for the Apple IIgs Exists!,” January 1, 2013, https://www.callapple.org/software/the-long-fabled-simcity-for-the-apple-iigs-exists/; and Rebecca Heineman, email, July 16, 2019, who did the IIgs conversion.

120. Braun, interview, May 19, 2015; Wright, interview, February 8, 2016; Braun, email, February 10, 2016.

121. J. Wilson, SimCity Planning Commission, foreword.

122. Rouse, Game Design, 431–432.

123. Wright, interview, September 26, 2022; Levy, “Simulation versus Reality”; Ellis, email, May 26, 2022.

Chapter 7

1. Angus, “Sim City.”

2. Hedrick, “SimCity”; Academy of Adventure Gaming Arts and Design, “Origins Awards Winners 1990,” accessed February 2, 2023, https://www.originsawards.net/origins-award-winners.

3. For award, see Computer Gaming World, “Game of the Year Awards,” October 1989: 8, 41–42. For rankings, see Computer Gaming World, May 1989 and onward.

4. J. Wilson, “‘Sim’ple Folk.” This community saw Wright as a designer, not a programmer, in contrast to Angus, “Sim City”; Barol, “Big Fun”; and Lew, “City Planning.”

5. Ferrell, McLean, and Netsel, “Best Games of ’89,” 53.

6. J. Wilson, “‘Sim’ple Folk.”

7. L. R. Shannon, “Sky Is Falling.”

8. Lew, “Making City Planning.”

9. Juba, “25 Years.”

10. Koebler, “Real Mayors.”

11. Barol, “Big Fun”; Lew, “Making City Planning”; L. R. Shannon, “Sky Is Falling.”

12. Barol, “Big Fun.” Johnny Wilson, author of the CGW review, provided screenshots for the Newsweek piece and later authored multiple Maxis strategy guides.

13. Levy and Mello, “Game Hall of Fame.”

14. Levy, “Simulation versus Reality.” For boids, see Reynolds, “Flocks, Herds and Schools.” For such ontological claims, see Rohrlich, “Computer Simulation”; Galison, Image and Logic; Winsberg, “Sanctioning Models.” See also Levy, “Exploring the Nature,” and Artificial Life.

15. See F. Turner’s “principle of juxtaposition,” Counterculture to Cyberculture, 84.

16. Cowan, “New Science of Complexity.”

17. F. Turner, Counterculture to Cyberculture.

18. Turkle, “Computer as Rorschach.”

19. The original version 1.0 Mac documentation lacked the “system simulation” framing added by the 1.1 addendum. Bremer, SimCity Documentation.

20. Bremer, SimEarth, 5.

21. Bremer, SimAnt, 6.

22. This argument, that computers complicate distinctions between games and tools, is made in chapters 2, 4, 5, 7, 9, and 10. See Pias, Computer Game Worlds.

23. W. Stevens, “Computer Is Used”; Gardner, “Fantastic Combinations”; Brand, “Spacewar”—articles that appear within a few years of one another.

24. Braun, interview, February 6, 2016.

25. F. Turner, Counterculture to Cyberculture. See this book’s Introduction.

26. This included SimCity’s Terrain Editor. Figures from Greco, “Mr. Right”; Maxis, “Annual Report 1996,” and “Board of Directors.” Note that Maxis’s fiscal year ends on March 31.

27. Braun, interview, May 19, 2015. This is filed retroactively, in May 1990, for January 1, 1989. See Braun and Wright, “Maxis Partnership Agreement.”

28. Electronics Boutique, Holiday Savings.

29. Greco, “Mr. Right.”

30. Braun, interview, May 19, 2015; Wright, interview, June 6, 2022.

31. Wright, interview, September 26, 2022; Levy, “Simulation versus Reality”; J. Wilson, SimCity Planning Commission, foreword; Ellis, email, May 26, 2022.

32. Juba, “Will Wright.”

33. Clarke, Gaian Systems; Lovelock and Watson, “Carbon Dioxide and Climate.”

34. Quote from Lovelock, Homage to Gaia, 264. I give Wright’s version of how they met (interview, June 6, 2022); Lovelock’s account (267) differs. For Daisyworld, see Watson and Lovelock, “Biological Homeostasis”; Lovelock, “Gaia as Seen”; Lenton and Lovelock, “Daisyworld Revisited.”

35. Clarke, Gaian Systems.

36. Markoff, Whole Earth. Quote from Brand’s diary (Markoff, 182).

37. A member of The WELL, Wright met Kelly, who had reviewed SimCity in the Whole Earth Review, at a Whole Earth network event. They bonded over their love of social insects (Wright, interview, June 6, 2022). For Brand’s trusteeship, see E. Baker, “Ultimate Think Tank,” 7; SFI, “Bulletin” (Summer–Fall 1989), 22. For Brand, cybernetics, and games, see F. Turner, Counterculture to Cyberculture; Brand, “Spacewar”; Markoff, Whole Earth.

38. Bremer, SimEarth; Gaia, “Gaia: Report and Accounts,” 2; Lovelock, Homage to Gaia, 267. Wright, interview, June 1, 2022, recounts that early SimEarth registration cards also invited respondents to choose an environmental charity for Maxis to donate $1 to. The options didn’t include Lovelock’s nonprofit, but did include Earth First!, which Maxis had failed to realize was a radical environmentalist group. I thank Phil Salvador for help verifying this story by sharing an image of an early registration card from the Strong Museum of Play, ID 114.2093.

39. Levy, “Simulation versus Reality”; Wright, interview, June 1, 2022; F. Turner, Counterculture to Cyberculture.

40. For GBN and Brand, see F. Turner, Counterculture to Cyberculture. For Biosphere 2, see Reider, Dreaming the Biosphere and also Turner; Clarke, Gaian Systems. See also chapter 4 and Clarke, “Autopoiesis and the Planet.” Turner notes that both GBN and its forerunner hosted events at Biosphere 2. I thank Ander Monson for drawing my attention to the potential relationship between SimEarth and Biosphere 2. For GBN’s wargaming-descended scenario methodology, see Kleiner, Age of Heretics.

41. Fuller, Operating Manual for Spaceship Earth.

42. For example, the Montreal Protocol (1987), Jim Hansen’s congressional testimony, nuclear winter, and global warming. See Weart, Discovery of Global Warming.

43. Dargahi and Bremer, SimCity 2000, 383–386; Rouse, Game Design; Haslam, “SimEarth,” 47–48, 54, 66. Wright proudly showed me the branch-free assembly code that rendered the 3D planet when I interned with him (personal recollection).

44. For example reviews, see Elmer-Dewitt, “The Day I Played God”; Rothstein, “Electronics Notebook: Just a Game?”; L. R. Shannon, “A World of Your Own.” Maxis, “Board of Directors,” reports initial domestic sales, for FY 1991 and 1992 at 113,506 units. Lifetime domestic sales were about 30 percent of the original SimCity.

45. Computer Chronicles, “Battle of Video Game Consoles.” See Wright, “Interfacing to Microworlds.”

46. Eisenhart, “Building SimEmpires”; Rouse, Game Design, 416.

47. Braun, interview, May 19, 2015. For strategy guides, see DeMaria, SimEarth; J. Wilson, SimEarth Bible.

48. See box designs.

49. Rouse, Game Design, 419. For Wilson’s pheromone discovery, see E. Wilson, “Chemical Communication”; Hölldobler, “Edward O. Wilson.”

50. Quote from L. Murray, “Wilson Inspires SimAnt.” See also O’Connor, “Bugging Out,” and “Maxis’s Anticipated SimAnt”; NPR Morning Edition, “E.O. Wilson and Will Wright,” September 1, 2009, http://www.npr.org/templates/story/story.php?storyId=112203095.

51. The recent publication of Hölldobler and Wilson, The Ants, helped. McCormick was also Wright’s long-time wargaming companion and rally race navigator. McCormick, “SimCity and Maxis”; McCormick, interview, September 30, 2021; Wright, interview, June 1, 2022.

52. Bremer, SimAnt.

53. Abram, Spell of the Sensuous.

54. Rouse, Game Design, 418–420.

55. Rouse, Game Design, 416. Such relatability is, of course, culturally specific (Henrich, Heine, and Norenzayan, “WEIRD”), which might help explain SimAnt’s younger, less enculturated, fans.

56. Bremer, interview, July 17, 2015. See also Dargahi and Bremer, SimCity 2000, 362–369.

57. L. Murray, “Wilson Inspires SimAnt.”

58. Langton, Artificial Life III, x–xi, describes academe’s burgeoning interest. For popular reportage, see Gleick, “Artificial Life”; Langton and Kelly, “Toward Artificial Life”; Levy, “Evolution of Artificial Life”; Ernsberger, “Electronic Ants”; Lewis, “Personal Computers ‘Creatures,’” and “Simulating Life”; S. Levy, Artificial Life; Schwartz, “This Is Your A-Life”; Kelly, “Genetic Images”; Rucker, Hacker and the Ants. See also chapter 4.

59. Quote from Helmreich, Silicon Second Nature, a definitive anthropological study of artificial life researchers at the Santa Fe Institute (55). The SimLife demo (“BioSim” at the time) is described by Helmreich, 56–57; Karakotsios, interview, June 29, 2020; SFI, “1992 Annual Report,” appendix VI, 40.

60. Karakotsios, interview, June 29, 2020; SFI, “1992 Annual Report,” appendix VI, 32–42.

61. Langton, Artificial Life III, xx; Helmreich, Silicon Second Nature, 57–58.

62. Yaeger, “Vivarium History”; A. Kay, “Computers, Networks and Education”; chapter 1, this book; Travers, “Agar,” 3; Fenton and Beck, “Playground.”

63. The Maxis delegation included Bremer, Harper, John Hiles (Delta Logic), Karakotsios, McCormick, and Vladimir Pokhilko (El-Fish), according to SFI, “1992 Annual Report,” which excludes Wright, who recalls attending (interview, June 1, 2022). SFI’s roster also lists Kressel and Peter Molyneux.

64. Karakotsios, interview, June 29, 2020; Karakotsios, CASim User’s Guide; Levy, “Evolution of Artificial Life”; Karakotsios and Bremer, SimLife, viii–x; Wright interview, June 1, 2022; chapter 4 this book. Karakotsios had specialized in hardware simulation prior to working on cellular automata (interview, July 1, 2020).

65. Karakotsios’s compensation would be royalty based. Karakotsios, interview, June 29, 2020.

66. Braun, interview, May 19, 2015. P. Lewis, “Personal Computers: ‘Creatures’” and “Simulating Life”; Schwartz, “A-Life.”

67. Langton, “SimLife from Maxis”

68. Knapp, “Banner Year”; E. Baker, “Ultimate Think Tank.”

69. Waldrop, Complexity, 267, describes Maxis–SFI software collaborations and Holland’s love of SimCity. For Gell-Mann’s admiration of Wright see Gell-Mann, Quark and the Jaguar, 363, and also Waldrop, Complexity, 256, 261. See chapter 4 for additional context. See SFI, “Annual Report,” for workshop rosters (1991, 1992) and Wright’s public lecture (1993, appendix IX, 1). See also SFI, “Research Program in Adaptive Computation.” In 1991, Karakotsios gave a colloquium on “Biosim,” SimLife’s working title. See “1991 Annual Report,” appendix III, 1. Karakotsios, email, September 27, 2022, confirms the working title.

70. Wright, interview, June 1, 2022. Maxis often gave special thanks to figures such as Kelly, Dawkins, and Langton in their manuals.

71. SFI, “Annual Report” (1988), 7; (1993), 48; “Bulletin” (Winter 1994), 34; E. Baker, “Ultimate Think Tank,” 5–9. I employ here the theories of F. Turner, “Counterculture to Cyberculture”; Galison, “Image and Logic.” For somewhat analogous interest from system dynamicists, see Langley and Larsen, “Can’t Believe.”

72. SFI, “Bulletin” (Winter 1994), 34. See, for instance, Rouse, Game Design, 417.

73. Langton, “SimLife from Maxis.”

74. Langton, Artificial Life III, xiii, xiv.

75. Karakotsios also presented “The New Frontier” at an artificial life–themed Ars Electronica (also attended by Kelly). Karakotsios, interview, June 29, 2020.

76. Karakotsios, interview, June 29, 2020.

77. Karakotsios, interviews, June 29 and July 1, 2020. (Quotes from July 1.) See Helmreich, Silicon Second Nature, 95. For the Anasazi model, see T. Kohler, “Prehistoric Villages.” See also Karakotsios, “Introducing Colony.”

78. Wright, interview, February 8, 2016.

79. See chapter 4.

80. See Suchman, Plans and Situated Actions; Brooks, “Elephants Don’t Play Chess”; Hutchins, Cognition in the Wild; Agre, Computation and Human Experience.

81. Waldrop, “Fast, Cheap, and Out of Control.”

82. Taylor, “Will Wright”; Seabrook, “Game Master.”

83. Rouse, Game Design, 425–427; Donovan, “Will Wright.”

84. Kelly, “Will Wright”; Wright, interview, February 8, 2016.

85. Wright, interview, February 8, 2016; McCormick, interview, September 30, 2021; Martin, interview, June 11, 2020; Martin, emails, September 2 and 8, 2021. Martin also recalls an early voxel-based 3D terrain experiment.

86. Foley, interview, July 31, 2015; email, June 29, 2016. The animation system encoded body part positions as a compact bytestream.

87. Braun describes the Nintendo deal and visit in interviews, May 25 and August 5, 2022; email, July 17, 2022. My account of interactions between Maxis and Nintendo was fact-checked with Nintendo, which corroborated many specifics with internal documentation and recollections, including those from Miyamoto. I am grateful to NOA representative Matt Peckham (emails, 2022) for facilitating these exchanges. Nintendo history is drawn from Sheff’s authoritative Game Over. See also Cifalfi, “Recovering Nintendo’s Lost SimCity for the NES.”

At the exact same time of Braun’s Washington visit, Aki Kodama, an executive at Imagineer (Maxis’s future Japanese publishing partner), contacted Braun and then acquired the Japanese PC publishing rights, which then broke sales records (Kodama, email, July 13, 2022). In July 1989, LOGiN, a Japanese game magazine with many industry subscribers, ran a cover story on SimCity (no. 16/17), according to its former editor-in-chief, Shintaro Kono (email, July 23, 2022). By August, Miyamoto was communicating with NOA about SimCity (Peckham).

88. This account is drawn from Braun, interviews, May 25 and August 5, 2022; Wright, interviews, June 1 and September 26, 2022; Peckham, emails, 2022; Nintendo Power, “Game Designer Profile: Shigeru Miyamoto,” 23; Sheff, Game Over. Braun’s slides, Kodama’s recollections, and an internal Nintendo memo all point to October (Peckham; Kodama).

Satoru Iwata, a developer destined to succeed Yamauchi, expressed interest in SimCity to Miyamoto, and later personally contributed to a Nintendo 64 port (Peckham; Kodama; Braun, interview, May 25). Wright recalls a design-focused, week-long visit without Braun, but the available evidence from Braun, Wright, and Nintendo leads me to conclude it was the same trip (and that there may have also been future trips that Wright undertook alone).

89. Wright, interview, June 1, 2022; Nintendo Power, “Shigeru Miyamoto”; Sheff, Game Over; Paumgarten, “Master of Play”; Game Informer, “2018’s Underrated Games, Will Wright Interview.”

90. Schoenberger, “Nintendo to Fund Learning.”

91. For Nintendo’s shifting demographic aspirations, especially with regard to Tetris, see Sheff Game Over, 292–296. For Miyamoto’s SimCity backstory, see Nintendo Power, “SimCity,” February 1991: 94. Braun recounts Miyamoto’s role in licensing SimCity in interviews, May 25 and August 5, 2022, and email, July 17, 2022. Wright, interviews, June 1 and September 26, 2022, and Miyamoto (via Peckham, emails), describe the design collaboration. For more on Nintendo’s experimental orientation, see Sheff, and for Gunpei Yokoi’s lateral thinking with “withered technology,” see Jones, “Lateral Innovation”; Epstein, Range.

92. Peckham, emails, 2022.

93. Harel, “Glimpse into the Playful World”; Pereira, “Nintendo Looks to MIT”; Schoenberger, “Nintendo to Fund Learning.” Kafai, Minds in Play, describes work supported by this grant, thus pointing to one link between the Nintendo grant and Scratch (which Kafai later contributed to). See also Harel, “Playful World.” For Negroponte and Wired, see F. Turner, Counterculture to Cyberculture.

94. Pereira, “Nintendo Looks to MIT.”

95. Kelly, “Will Wright”; Salvador, “SimCity Got Serious.”

96. Greco, “Looking for Mr. Right,” which gives Derber’s start date as April 1992; Eisenhart, “Building SimEmpires”; Braun, interview, May 19, 2015. Sales data for FY 1992.

97. Braun, quoted in Eisenhart, “SimEmpires.”

98. Kressel and Lento, Investing in Dynamic Markets, 200; Robarge, “From Landfill”; “Atari Parts Are Dumped,” New York Times, September 28, 1983.

99. Borsook, “Release.”

100. Janeway, interview, December 7, 2021, and Braun, emails, December 8, 2021, recount their introduction. For winning over Braun and Wright, see Kressel and Lento, Investing in Dynamic Markets, 200. For Dyson, see Borsook, “Release”; F. Turner, Counterculture to Cyberculture; E. Baker, “Ultimate Think Tank,” 10. For Janeway’s career, see Warburg Pincus, “William H. Janeway,” accessed June 17, 2020, https://www.warburgpincus.com/people/william-h-janeway/; Janeway, Doing Capitalism. Wright, interview, February 8, 2016, recollects meeting Janeway and Kressel and the investment’s initial effects.

101. Wright, interview, February 8, 2016.

102. Janeway, interview, December 7, 2021. Janeway’s 1977 “Research Note” describes and contextualizes his enthusiasm for and engagement with system dynamics. (For the National Modeling Project, see Forrester, Mass, and Ryan, “National Model.” For additional context, see K. Baker, “World Processors,” 282; Kleiner, Age of Heretics.) For Janeway’s career and interest in simulation, see Janeway, Doing Capitalism; “Economic Policy.” For Janeway’s family, see Gilpin, “Eliot Janeway”; Vitello, “Michael Janeway.”

103. Janeway, “Research Note.”

104. Janeway, Doing Capitalism, 40.

105. Janeway, “Research Note.”

106. Janeway, interview, December 7, 2021; Kressel, interview, August 12, 2022; Kressel and Lento, Dynamic Markets, 200.

107. Dyson, “What If?” SimRefinery was particularly resonant given that Janeway’s wife, in her role as an investment analyst, was involved with chemical industry modeling (Janeway, interview, December 7, 2021). By March 1992, Dyson had already attended two SFI workshops with Wright. See SFI, “Annual Report” (1991, 1992).

108. Members included John Seely Brown (who helped set it up), David Liddle, and Avram Miller (who later sat on Maxis’s board and advisory board). Janeway, interview, December 7, 2021; Braun, emails, December 8, 2021; Maxis, “Annual Report 1996,” 29.

109. Kressel and Lento, Dynamic Markets, 200–201.

110. Kressel, interview, August 12, 2022; email, December 1, 2022. For Kressel’s career and family, see Yeshiva University, “Kressel Elected to Board”; Kressel, Competing for the Future, 11–12; Warburg Pincus, “Henry Kressel,” accessed September 20, 2022, https://warburgpincus.com/team/henry-kressel/; Yeshiva University, “Dr. Henry Kressel,” March 10, 2013, https://web.archive.org/web/20130310083511/https://www.yu.edu/support/meet-donors/kressel/; Clara Mermelstein, interview by Lorrie Fein, September 11, 1996, USC Shoah Foundation. Kressel and Lento, Dynamic Markets, 200–201, describes Maxis’s appeal.

111. Kressel, interview, August 12, 2022.

112. Greco, “Mr. Right”; Quinn, “Maxis”; Wright, interview, February 8, 2016. Wright’s stakes shifted; he no longer received royalties for new titles. Wright, interview; Electronic Arts, “Form S-4,” 100, F-26.

113. Janeway, interview, December 7, 2021.

114. Braun, interview, May 25, 2022.

115. Greco, “Looking for Mr. Right.”

116. Janeway, interview, December 7, 2021; Curtin, interview, August 2, 2015.

117. Greco, “Looking for Mr. Right”; Brady, “I’ve Looked at Life”; Kressel and Lento, Dynamic Markets, 202.

118. For an authoritative account of Maxis Business Simulations, see Salvador, “When SimCity Got Serious,” “John Hiles,” and “SimRefinery.” Joe Scirica, emails, 2022, clarifies that Delta Logic wasn’t acquired—staff were hired with bonuses. For Wright’s involvement, see Spear and Wilson, Planning Commission, 202.

119. Santa Fe Institute, “1993 Annual Report,” 48.

120. Salvador, “When SimCity Got Serious”

121. Eisenhart, “SimEmpires.”

122. Starr, “Seductions of Sim”; chapter 1, this book.

123. Martin, interview, June 11, 2020; emails, September 2 and 8, 2022. See also Dargahi and Bremer, SimCity 2000, 343–44.

124. Martin, interview, June 11, 2020.

125. Braun, interview, May 25, 2022; Doornbos, interview, April 19, 2020; Bulwa, “Stage Fight.”

126. Martin, interview, June 11, 2020; Scirica, emails, 2022; Braun, interview, May 25, 2022; Wright, interview, June 1, 2022. According to Braun (interview, May 25, 2022), he flew to Moscow at the invitation of Henk Rogers (who had helped Nintendo license Tetris) and struck a deal with El-Fish’s creators, one of whom had originated Tetris. Braun convincingly contradicts Ruby, “El-Fish,” which attributes the introduction to Esther Dyson.

127. Harper, emails, 2022; Braun, interview, May 25, 2022. For the SimCity 2000 release, Harper employed a “line extension” (Harper) model she had learned while working with Clorox, a Procter & Gamble company. See also Kim, “Marketing 100: Robin Harper”; Pettis, TechnoBrands, 58–61; Greco, “Looking for Mr. Right.”

128. Foley, interview, July 31, 2015; Derber, interview, Oct. 29, 2021.

129. Braun, interview, May 19, 2015.

130. Rouse, Game Design, 420.

131. Kantrowitz, “Meet Mr. Simcity”; Dargahi and Bremer, SimCity 2000, 343–354, 387–388; Rouse, Game Design, 420–422; Foley, interview, July 31, 2015, and email, June 29, 2016.

132. Foley, email, June 29, 2016; McCormick, interview, September 30, 2021.

133. Braun, interview, May 19, 2015.

134. Kelly, “Will Wright”; W. Wright, “Future of Content”; Diamante, “Marketing 100: Future of Content.”

135. Kressel and Lento, Investing in Dynamic Markets, 202–203. For a fuller discussion of industry risk, see Electronic Arts, “Form S-4.”

136. Kressel and Lento, Investing in Dynamic Markets, 201.

137. Greco, “Looking for Mr. Right”; Maxis, “Annual Report 1996”; “Proxy Statement” (July 17, 1996), 5; Kressel and Lento, Investing in Dynamic Markets, 201–203, 213; Scirica, emails, 2022; Braun, interview, May 25, 2022.

138. SF Gate, “Maxis Inc. Ready to Go Public,” March 30, 1995; Curtin, interview, August 2, 2015; Curtin, email to Doreen Nelson, February 22, 1993. See chapter 1, this book.

139. “Computer Games with Principles,” New York Times, November 7, 1993.

140. Chiaradia and Yacoub, “SimCity”; McManes, “Future City”; Salvador, “When SimCity Got Serious.”

141. Chiaradia and Yacoub, “SimCity”; McCormick, interview, September 30, 2021; letter from Sally Vandershaf to David Greschler, April 20, 1992, and associated files, Computer History Museum, https://tcm.computerhistory.org/CHMfiles/Sim%20Art%20-%20SimLife%20Computer%20Games%201992.pdf.

142. Eisenhart, “Building SimEmpires.”

143. Curtin, interview, August 2, 2015; chapter 1, this book.

144. Friedman, “Making Sense of Software”; Kolson, “Politics of City Planning Simulations” and “Politics of SimCity”; Schone, “Building Rome in a Day” (cited in “Politics of SimCity”); Starr, “Seductions of Sim”; Bleecker, “Urban Crisis” (cited in Bleecker, “Reality Effect,” 180); Turkle, “Who Am We?”

145. These included Terry Winograd, whose robotics simulation had helped inspire Wright to programming (see chapter 5). For Wright’s talks at Winograd’s seminar, see W. Wright, “Interfacing to Microworlds,” “Games and Simulation,” and “Laughing Creative Communities”; Hopkins, “Will Wright on Designing User Interfaces.”

146. W. Wright, “Will Wright: A Chat.”

147. Braun, interview, May 25, 2022.

148. Janeway, Doing Capitalism; email, August 9, 2022.

149. For WP’s approach, see Kressel and Lento, Investing in Dynamic Markets, xiiv; Janeway, Doing Capitalism, 103–7; Gerson, interview, August 1, 2022.

150. Gerson, interview, August 1, 2022, who identifies Cary J. Davis as the staffer.

151. Braun, interview, May 19, 2015; Doornbos, interview, April 19, 2020.

152. Rouse, Game Design, 424–425; Curtin, interview, August 2, 2015; Harper, emails, 2022; Wright, interview, June 1, 2022.

153. Braun, interview, May 19, 2015.

154. Kelly, “Will Wright.”

155. Scirica, emails, June 24 (source of quote) and July 8, 2022.

156. Doornbos, interview, April 19, 2020 and Mackraz, interview, June 25, 2020 independently recalled this complaint.

157. Doornbos, interview, April 19, 2020.

158. “TDS” became a common acronym in The Sims 1.0 code. Mackraz and Eric Bowman, Facebook communication, July 2022; Doornbos, email, September 23, 2002.

159. Rouse, Game Design, 424. The transition to Project X likely happened in 1994. See Kelly, “Will Wright”; Kantrowitz, “Mr. Simcity.”

160. Wright, interviews, February 8, 2016, and June 1, 2022.

161. Doornbos, interview, April 19, 2020; W. Wright, “Will Wright: A Chat.” See Crane, Nelson, and The Little Computer People Research Team, “System Bugs.”

162. Doornbos, interview, April 19, 2020.

163. Kantrowitz, “Meet Mr. Simcity.”

164. Rouse, Game Design, 425–427; Spear and Wilson, Official SimCity 2000 Planning Commission, 203–204; Wright, “Interfacing to Microworlds.”

165. Doornbos, interview, April 19, 2020. The voxel prototype—described by Doornbos, interview; Wright, interview, June 1, 2022; and personal recollections of Wright’s old computers—used a Blind Watchmaker-style interface. For this interface, see Dawkins, “Evolution of Evolvability” and Blind Watchmaker. See also Kelly, Out of Control, 238. Comanche: Maximum Overkill (1992) helped popularize voxels in gaming. Although my aim is to highlight the messiness of The Sims’s development, I nonetheless simplify. Forbus, Scirica, McCormick, and Martin (interviews and emails) all gave more detail on additional wide-ranging experiments.

166. Doornbos, interview, April 19, 2020; Doornbos, emails, September 23, 2022. Hopkins, email, August 25, 2021, confirm my recollection of Wright’s motive prototype. See also Hopkins, “Soul of The Sims”; Mackraz, interview, June 25, 2020; Mackraz and Bowman, emails, September 22, 2022; Rouse, Game Design, 421, 425, 433–35.

167. Doornbos, interview, April 19, 2020. Doornbos compares this language to a RISC architecture, which experienced a revival in the 1980s and 1990s. Cf. Java (1995), which also used a bytecode interpreter.

168. I am indebted to Mick Foley, interview, July 31, 2015, for this insight.

169. To name a few approaches Wright combined.

170. Quinn, “Orinda’s Maxis.” As word of Maxis’s IPO plans spread, George Lucas approached Braun about merging LucasArts, his game company, with Maxis, and going public together. Braun declined Lucas’s unreasonable terms (Braun, interview, May 25, 2022). Maxis also reincorporated, officially changing the name of the company to “Maxis” from “Sim-Business” (Derber, “Articles of Incorporation of Sim-Business”; Maxis and Sim-Business, “Plan of Merger”), an entity established in 1990 to clean up any lack of formal compliance in prior incorporations (Braun and Derber, email, May 25, 2022).

171. Quinn, “Orinda’s Maxis”; Kressel and Lento, Investing in Dynamic Markets, 201.

172. Braun, interview, May 19, 2015.

173. Lewis, New New Thing.

174. Quinn, “Orinda’s Maxis.”

175. Braun, interview, May 19, 2015.

176. Braun, interviews, February 6, 2016, and August 5, 2022, and email, July 22, 2022; Gerson, interview, August 1, 2022; Kressel, interview, August 12, 2022. Janeway, interview, December 7, 2021, describes the desire for liquidity. Gerson recalls no dissent about going public, but wasn’t present in all board meetings and discussions.

177. Newsweek Staff, “The Net 50.”

178. Lewis, New New Thing.

179. Juba, “Reflecting on Maxis.”

180. Wright, interview, February 8, 2016.

181. Braun, interview, May 19, 2015.

182. Maxis, “Form 10-K” (1997); Kressel and Lento, Investing in Dynamic Markets, 201–203.

183. As Braun, interview, May 25, 2022, pointed out to me, Larry Probst, EA’s CEO, came from Procter & Gamble, a trailblazer in brand management. See Kleiner, Age of Heretics.

184. Hill, “Maxis, Ringling”; Dargahi and Bremer, SimCity 2000, 431–432.

185. Mackraz, interview, June 25, 2020.

186. S. L., “Maxis (1987)”; Antoniades, “It’s a Sim World”; Eisenhart, “SimEmpires”; Maxis, “Will Wright Interview”; W. Wright, “Interfacing with Microworlds,” and interviews, February 8, 2016, and June 1, 2022; Mackraz, interview, June 25, 2020; Braun, interview, May 25, 2022. This happened as OOP became mainstream with, for example, C++ and Java.

187. Doornbos, interview, April 19, 2020; Mackraz, interview, June 25, 2020.

188. Antoniades, “It’s a Sim World”; Braun, interview, May 19, 2015; Mackraz, interview, June 25, 2020; Wright, interview, June 1, 2022.

189. Gerson, interview, August 1, 2022; Gerson, LinkedIn messages, August 2, 2022. For ownership percentage, see Maxis, “Proxy Statement” (July 17, 1996), 1–2.

190. Gerson, interview, August 1, 2022; Braun, interviews, May 25 and July 21, 2022; Janeway, emails, August 8 and 9, 2022; Kressel, interview, August 12, 2022; anonymous source(s).

191. Eisenhart, “Building SimEmpires.”

192. Maxis, “Form 10-K” (1997); Maxis, “Annual Report 1996.”

193. Braun, interview, May 25, 2022; Gerson, interview, August 1, 2022. See also Kressel and Lento, Investing in Dynamic Markets, 213. According to Gerson, news of the failed secondary also depressed Maxis’s stock price.

194. Wright, interview, February 8, 2016; Mackraz, interview, June 25, 2020.

195. Donovan, “Will Wright”; Wright, interviews, February 8, 2016, and June 1, 2022.

196. Braun, interview, May 19, 2015.

197. Mackraz, interview, June 25, 2020.

198. Mackraz, interview, June 25, 2020; Martin, interview, June 11, 2020; Wright, interview, June 1, 2022; Mackraz and Bowman, emails, September 22, 2022.

199. Braun, interview, May 19, 2015.

200. Maxis, “Form 10-Q” (1996); Maxis, “Form 10-K” (1997); Braun, interview, February 6, 2016. Former board member Avram Miller, email, July 22, 2022, recalls Maxis as a poorly run company.

201. Derber, interview, November 18, 2021; Scirica, emails, 2022; Harper, email, July 13, 2022; Gerson, interview, August 1, 2022; Braun, interview, June 6, 2021.

202. Braun, interview, May 19, 2015.

203. Wright, interviews, February 2, 2016 and September 26, 2022; Scirica, emails, 2022; Electronic Arts, “Form S-4,” 25.

204. “Electronic Arts Will Buy Maxis in Swap,” New York Times, June 5, 1997; Wired Staff, “Electronic Arts to Buy Maxis”; Angwin, “EA Is Buying Maxis”; Electronic Arts, “Form S-4”; Mackraz, interview, June 25, 2020. This acquisition marked a reversal in EA’s strategy towards PC. See Sheff, Game Over, 232.

205. Electronic Arts, “Form S-4”; Mackraz, interview, June 25, 2020; Wright, interview, June 1, 2022. Wright recalls Larry Probst’s presence, and Mackraz recalls a larger delegation, contradicting “Form S-4,” 26. The competing project, Wright says, was “Keeping Up with the Joneses.”

206. Barthelet, quoted in Keighley, “Simply Divine.” See also Rouse, Game Design, 436–437.

207. Juba, “Reflecting on Maxis.”

208. Wright, interview, June 1, 2022.

209. Mackraz, interview, June 25, 2020.

210. Donovan, “Will Wright”; Curtin, interview, August 2, 2015; Mackraz, interview, June 25, 2020.

211. Martin, interview, June 11, 2020; email, September 8, 2021.

212. Seabrook, “Game Master.”

213. Donovan, “Will Wright”; Curtin, interview, August 2, 2015; Mackraz, interview, June 25, 2020.

214. Mackraz, interview, June 25, 2020.

215. Mackraz, interview, June 25, 2020.

216. Mackraz, interview, June 25, 2020. In Rouse, Game Design, 427, Wright explains the importance of Scott McCloud’s Understanding Comics to his approach to The Sims.

217. Parkin, “The Kiss.” See also Phipps, “Will Wright.”

218. Mackraz, interview, June 25, 2020, draws this connection. See Bella, “Politicians Suggest Video Games Are to Blame.”

219. Mackraz, interview, June 25, 2020.

220. Doornbos, interview, April 19, 2020. For revenue, see Chilton, “The Sims 20th Anniversary.” And EA didn’t have to pay licensing fees to anyone; it owned the IP. For Wright’s confidence and commercial expectations, see also Rouse, Game Design, 428.

221. Turner, Counterculture to Cyberculture. See this book’s Introduction.

222. Electronic Arts, “Form S-4.”

223. Bowker, “How to Be Universal,” 116, quoted in F. Turner, Counterculture to Cyberculture, 25.

224. Turner and Larson, “Network Celebrity.” Note, also, how Wright complements Miyamoto’s design sensibility using the language of complexity science—“emergence.”

225. Wright, interview, September 26, 2022.

226. Hayles, How We Became Posthuman, xi.

227. See Helmreich, Silicon Second Nature; Hayles, How We Became Posthuman; L. Kay, Who Wrote the Book of Life; F. Turner, Counterculture to Cyberculture; E. Baker, “Ultimate Think Tank”; chapter 4, this book.

228. F. Turner, Counterculture to Cyberculture; E. Baker, “Ultimate Think Tank.”

229. F. Turner, Counterculture to Cyberculture; E. Baker, “Ultimate Think Tank.” See Light, “Taking Games Seriously.”

230. Latour, Aramis. Cf. “mutual orientation” (Edwards, Closed World, 81–83) and “mutual accommodation” (Redmond and Smith, Project Whirlwind, 102).

231. Janeway, interview, December 7, 2021.

232. Greco, “Looking for Mr. Right”

233. O’Connor, “Maxis’s Anticipated SimAnt.”

234. Maxis, “10-K” (1997), 36, notes this had been his title since 1987, but “10-K” (1996), gave his title as “chief technical officer.” See also Wright, interview, September 26, 2022.

235. Wright, interview, September 26, 2022.

236. Braun, interview, May 19, 2015; Wright, interview, February 8, 2016. Kressel and Lento, Dynamic Markets, 203, argues that Maxis lacked the financial resources to complete The Sims.

237. Donovan, “Will Wright.”

238. Braun, interview, May 19, 2015; Wright, interview, February 8, 2016; Mackraz, interview, June 25, 2020; email, September 23, 2021.

239. Wright, interview, February 8, 2016.

240. Forbus, interview, September 17, 2021. See Forbus, “Why Computer Modeling”; “CS 395–22”; Forbus and Wright, “Programming Objects.” Forbus and Wright’s friendship goes back to the early 1990s, when he invited Wright to visit Northwestern University. Forbus’s extensive professional associations—which include PARC, DARPA, and NASA—helped him broker a relationship between NASA and Maxis for (the aborted) SimMars.

241. Wright, interview, June 1, 2022. Wright describes his ambitions for such tools in “A Chat”; Markoff, “Something Is Killing the Sims.” For the released tools, see Barba, “The Sims,” 169–178.

Chapter 8

1. Kidder, Soul of a New Machine, 30–32.

2. Mahoney, “Histories of Computing(s).”

3. Mahoney, “History of Technology” and “History of Software.”

4. Turkle, “Computer as Rorschach” and Second Self; Mahoney, “History of Technology” and “Histories of Computing(s).”

5. Friedman, “Making Sense of Software, “The Semiotics of SimCity,” and “Civilization and Its Discontents”; Chiaradia and Yacoub, “SimCity”; Schone, “Building Rome in a Day”; Starr, “Seductions of Sim”; Kolson, “City Planning Simulations” and “Politics of SimCity”; Turkle, “Seeing through Computers”; Bittanti, SimCity; Homeless; Bleecker, “Reality Effect”; Pedercini, “SimCities and SimCrises”; K. Baker, “Model Metropolis” and “World Processors.” Critical analyses of SimCity seem to begin following SimCity 2000’s release.

6. As the origin for later platform conversions, I focus on the Macintosh version, played in the SheepShaver emulator. Its code, alongside that of some other ports, has been made available to me. I sometimes look to the PC, UNIX, and OLPC code (open sourced by Electronic Arts) for guidance. My analysis is indebted to Don Hopkins for collating SimCity’s character mnemonics, which earlier versions of SimCity’s code lacked.

7. Spear and Wilson, Official SimCity 2000 Planning Commission, 201–202. Wright was surprised to find that nobody had built a fully integrated city simulation before, conjecturing that specialization prevented the necessary synthesis (202). Holistic simulations, in fact, had become a scientifically moribund and taboo enterprise. See D. Lee, “Requiem”; Starr, “Seductions of Sim.”

8. This holds true for SimCity’s target platforms.

9. This clock tracks a hardware rhythm of the Macintosh (the screen’s vertical retrace interrupt) and is exposed to programs as ticks, sixty of which make a second. This clock drives the mouse cursor and OS events. See Apple Computer, Inside Macintosh, vol. 1.

10. Early versions of SimCity used preemptive multitasking (interrupts) to interleave simulation and user interface processing. For portability and compatibility (e.g., Mac OS MultiFinder), this was replaced with a cooperative multitasking scheme. Remnants of the preemptive scheme, and possibly support for Amiga’s native concurrency, can be found in the form of obsolete mutex guards, synchronization flags, and testing for input within the simulator (that cause it to stop). This is explained by Mick Foley, interview, July 31, 2015, and email, July 6, 2020; Brian Conrad, interview, March 21, 2016; Wright, interview, February 8, 2016.

11. Like many other PC games of the era, the exact gearing between real-time and simulation clocks depends on a host of factors.

12. For the C64’s influence on SimCity, see chapter 6. SimCity’s code refers to map elements as both a “tile” and “chr” (character). User interface code prefers tile, while the simulation code prefers character. This reflects the division of labor behind SimCity’s creation: Wright programmed the simulator, and others programmed the user interface. I try to follow a similar convention, preferring character for the number (and graphic) represented within a tile, a term I use to refer to a higher-level entity: the overall user-facing representation that occupies a discrete map location (which encompasses a character and its status bits).

13. I elide, in this treatment, the logic that handles the player’s map editing.

14. Playback pattern and the next frame are given in lookup tables. Most animations are looped, but not the tiny explosion. To make one, the simulator places a tiny explosion on the map and turns on its Animate bit. Animate Tiles then plays the flipbook animation. Once the animation approaches completion, Map Scan converts the tile to rubble and turns Animate off. It’s a choreographed dance between two simulation processes.

15. Scott, Seeing Like a State, describes nation states as employing analogous processes.

16. Pearce, “Sims, BattleBots.”

17. For more on such linked representations, see Victor, “Media for Thinking the Unthinkable.”

18. SimCity does save additional nonmap information, such as the player’s budget. Secondary layers are also created when instantiating a new city.

19. See chapter 6.

20. Spear and Wilson, Official SimCity 2000 Planning Commission, 201–202.

21. Buildings encode a fixed population density and land value, but the Land Value map evolves as it responds to additional factors like crime.

22. In keeping with this stochastic approach, more densely populated zones are evaluated more often. For more on the relation of the term valve to Forrester, see chapters 3 and 6.

23. To further help players see what is going on inside the simulation, population changes are also registered in ancillary maps: Rate of Growth and Population Density (which the simulator also consults).

24. Spear and Wilson, Official SimCity 2000 Planning Commission, 203.

25. Wright, interview, February 8, 2016.

26. Wright, interview, February 8, 2016. The variable “EMarket” is evidence of a vestigial model. It occupies some of the lowest (and presumably oldest, in terms of design history) space in the miscellaneous data stored when saving a city. The simulator never consults it.

27. It can also indicate city planning issues, as when demand is high but zones remain undeveloped.

28. Forrester prefers the term rate, but he uses valve to describe the diagrammatic figure for a rate (chapter 3) and uses the imagery of a faucet in some explanations (e.g., Forrester, “Some Basic Concepts”). Wright’s code calls these “valves,” and he has referred to these as a “spigot” (Wright, interview, February 8, 2016).

29. Zones have varying population densities, so this population refers not to the number of zones but to the cumulative population represented by those zones.

30. As in Forrester’s Urban Dynamics model, deaths are modeled implicitly in the birth rate. As written, Set Valves corroborates Wright, interview, February 8, 2016, that the external market was originally modeled differently, but is now modeled by Industrial zones.

31. If the messaging system (Send Messages, discussed later) determines that one of these facilities is needed, then it sets one of three flags (ResCap, ComCap, IndCap), which Set Valves then uses to prevent demand for that sector from ever going positive. This conditional logic is not very systems dynamics-like and so represents another hybridization.

32. Wright, interview, February 8, 2016.

33. Unlike Urban Dynamics, SimCity has no class-based representation of labor, housing, or business.

34. While all of SimCity’s objects can be active at once, only one of each can exist at a time. Lacking the C64’s specialized hardware, the Macintosh version made up for it with software and more powerful general-purpose hardware.

The eighth sprite generator was likely put to other purposes. According to Foley, email, July 6, 2020, Raid’s helicopter used two sprites, one for the blades and one for the helicopter itself, and these two sprites were then repurposed (with an interrupt) to draw the mini-map.

35. Wright, interview, February 8, 2016.

36. Messages are typically shown at the top of the Edit Window. (Some present in an illustrated dialog box.) Some messages are accompanied by sounds, and those that occur at a particular place on the map are accompanied by a GoTo button.

37. This schedule is synchronized to the simulation clock.

38. Foley, interview, July 31, 2015, notes that simulation bugs are a tricky category. Are they bugs? What is a simulation bug, anyway? It’s all artifice, after all. Compare to Wardrip-Fruin, Expressive Processing, and what he calls the “SimCity effect.”

39. In addition, fire and police stations don’t need proper connections to the transport network to function properly. As long as they are adjacent to one transport cell, they function at full capacity.

40. Wright’s theory of player imagination builds on McCloud, Understanding Comics, as he articulates in Rouse, Game Design, 427. Don Hopkins, interview, September 17, 2013, refers to this process as “simulation by implication.” For active creation of belief, see J. Murray, Hamlet on the Holodeck, 110–112. See also J. Murray’s notion of scripting the interactor in Inventing the Medium, 82–83 and Hamlet, 79, 192, 219, 232.

41. See Levin, “Painterly Interfaces,” 57.

42. Withholding information and detail catalyzes empathy and imagination (McCloud, Understanding Comics), and fosters a sense of autonomous behavior (Zhu, “Intentional Systems”).

43. Pearce, “God and Go.” See also Rouse, Game Design, 427, where Wright describes the influence of McCloud, Understanding Comics, on his work, and Wright, “Lessons in Game Design.”

Chapter 9

1. Quote from an internal Sun Microsystems email, July 24, 1991. My account of SimCity’s open sourcing draws on Don Hopkins, emails, September 2, 2013, which included substantial forwarded correspondences; Hopkins, interview, September 17, 2013, and email, February 15, 2015; Charles Norman, email, September 3, 2013. The port was begun at Sun and eventually completed under the aegis of an external vendor, Dux Software, by Hopkins, who learned about the opportunity (and began working on it) while employed by Sun. Sun often supported the development of applications for specific market verticals to help sell their computers. Perkins, “Bedlam in SimCity,” reviews the original Unix release.

2. For the SimCity network, see the Introduction and chapter 7.

3. For an authoritative account and critique of OLPC, see Ames, Charisma Machine.

4. In November 2006, John Gilmore, a cofounder of the EFF and early Sun Microsystems employee, contacted Don Hopkins about asking Will Wright, whose prestige was at a zenith within Electronic Arts, about open sourcing or buying the original SimCity. Hopkins was a logical contact: he had worked at Sun, ported SimCity to Unix, and worked with Wright on The Sims. After hearing from Hopkins, Wright introduced the idea to EA executives Rod Humble and Scott Evans, who assigned Chuck Norman to the project. EA counsel worked with Eben Moglen, Columbia Law School professor, OLPC adviser, and general counsel to the FSF. This was aided by Hopkins’s copying of the original Maxis/DUX licensing agreement while working on The Sims. Hopkins performed the conversion. EA stipulated that their QA approve any release titled “SimCity,” which motivated the project’s release as “Micropolis” (a name Wright suggested), and, as a post-9/11 consideration, removal of the airplane crash disaster.

5. Electronic Arts, “EA Donates.”

6. For the SimCity network, see the Introduction, chapter 7, and F. Turner, Counterculture to Cyberculture.

7. Galison, Image and Logic, 752–771.

8. Sutton-Smith, Ambiguity of Play. Sutton-Smith considers the rhetorics used to advance a particular conception of play.

9. Turkle, Second Self.

10. Juul, “The Game, the Player.”

11. For SimCity’s game-liminality with respect to judgment, see Juul, “The Game, the Player,”

12. Garvey, Play, 7, 103–106. Garvey’s formulation helps explain why we tend to refer to computer play phenomena as games: “explicit rules” are “precisely communicated” through software. I have spent considerable time immersed in the scholarly literature on play, and my opinion is that Garvey’s book is one of the best (and most underappreciated) works available.

13. Rouse, Game Design, 413. Note how this statement resonates with the objectives of Maxis’s aborted SimWorld initiative. When I asked Wright about his professional identity as a game designer, he explained that “for me games are more like an opportunity independent of the medium” of interactive computing (interview, September 26, 2022).

14. Pearce, “Sims, BattleBots”; Rouse, Game Design; Kushner, “Will Wright’s Artificial Intelligence”; Donovan, “Replay Interviews”; Foley, interview, July 31, 2015; Will Wright, interview, February 8, 2016.

15. Rouse, Game Design; Wright, “Postmortem.”

16. This characteristics are drawn from Bateson, “Theory of Play and Fantasy”; Huizinga, Homo Ludens; Caillois, “Unity of Play”; Vygotsky, Mind in Society, 92–104; Nicholson, “How Not to Cheat”; Garvey, Play; Suits, Grasshopper; Sutton-Smith, Folkgames of Children, xiii–xvi; Toys as Culture, especially 138–143; “Consilience of Play Definitions”; Schechner, “Playing”; Salen and Zimmerman, Rules of Play; Burghardt, Genesis of Animal Play. For a more detailed survey see Gingold, “Play Design.” See also Masek and Stenros, “Meaning of Playfulness.”

17. The most nonconforming views can be found in the subjective aspects of play—for example, Malaby, “Beyond Play”; Schechner, “Playing.” Malaby dismisses play’s positive subjective aspects while attending to the powerful subjective experience of gambling. If anything, his argument foregrounds the importance of both subjective experience and attitude. Schechner points out that play can be risky and antisocial—“dark” (see also Sutton-Smith and Kelly-Byrne, Masks of Play, 187–189, and “Idealization of Play”), but agrees that a feeling of safety is a necessary precondition. Schechner’s antisocial play examples nonetheless call attention to the shared attitude of play, a focus of Garvey, Play, and Suits, Grasshopper, 44–48 (“Triflers, Cheats, and Spoilsports”). Play can also have an obligatory quality (Sutton-Smith, “Evolving a Consilience of Play Definitions”).

18. American Football is a good example of risky but buffered play-fighting. For canid play bows, see Bekoff, “Social Play” and “Social Play in Coyotes, Wolves, and Dogs.” For more on play-fighting, paradox, and metacommunication, see Bateson, “Play and Fantasy.” For toys, see Sutton-Smith, Toys as Culture.

19. In Toys as Culture, 140, Sutton-Smith writes that “plays [sic] own induction of schematic rules is itself a violation of everyday rules, and this state of affairs appears to generate within play a constant succession of bipolar and disequilibrating structures which are recurrently equilibrated.” See also Nicholson, “How Not to Cheat.”

20. For the collaborative pretense that underwrites competition, see Huizinga, Homo Ludens; Suits, Grasshopper, 38–41. For games as institutionalized play, see Garvey, Play, 7, 103–106. For playmate scaffolding, see Bateson, “Play and Fantasy”; Garvey, Play; Bekoff, “Social Play ” and “Social Play in Coyotes, Wolves, and Dogs”; Bekoff and Allen, “Intentional Communication.” For self-handicapping, see Watson and Croft, “Playfighting Strategies,” or consider handicap stones in Go. For nongame examples of institutionalized play, see Huizinga Homo Ludens; V. Turner, “Liminal to Liminoid.” For play as engendering “secret societies,” see Huizinga, 12–13, 20; Sutton-Smith, Toys as Culture, 139. For antisocial “dark” play, see the footnote for Subjective. For the unity of make-believe and rules, see Vygotsky, Mind in Society, 94–96.

21. Inefficient means is characteristic of play, games, and leisure activity. See Suits, Grasshopper; Gelber, Hobbies; Maines, Hedonizing Technologies.

22. See Rouse, Game Design; chapter 6, this book.

23. For more on parental and sibling scaffolding see Garvey, Play, 123–143. See also Janet Murray’s notion of scripting the interactor in Hamlet and Inventing the Medium.

24. Spear and Wilson, The Official SimCity 2000 Planning Commission, 202.

25. Langton, “Computation at the Edge of Chaos.” For more on Langton, an artificial life pioneer, see chapter 4, which also links this aesthetic with Conway’s Life. The “edge of chaos” idea originates with Norman Packard, “Adaptation Toward the Edge of Chaos”; see Keller, “Organisms, Machines, and Thunderstorms,” 17–18.

26. Chapter 8.

27. J. Murray, Inventing the Medium, 82–83, and Hamlet, 79, 192, 219, 232.

28. For the power of such linked representations see Victor, “Media for Thinking the Unthinkable,” and chapter 2.

29. Juul, “Heart of Gameness.”

30. A code comment calls this “punish for cheating.”

31. Parlett, History of Board Games, 3; Juul, “Heart of Gameness.”

32. Juul, “The Game, the Player.”

33. Cf. Forrester, Urban Dynamics.

34. Spear and Wilson, The Official SimCity 2000 Planning Commission, 203.

35. See chapter 6.

36. For “loose parts” see Nicholson, “How Not to Cheat,” and “Theory of Loose Parts.”

37. Gingold, “Miniature Gardens and Magic Crayons,” 52–71.

38. Gingold, “Miniature Gardens and Magic Crayons.”

39. Cf. “SimCity effect” in Wardrip-Fruin, Expressive Processing.

40. My account of this contest is indebted to Koebler, “Real Mayors.” Quotes are from Joseph Braude’s Providence Journal article, as quoted by Koebler.

41. See chapter 2.

Conclusion: The World in a Machine

1. Pearce, “God and Go”; Rouse, Game Design, 427; W. Wright, “Lessons in Game Design.”

2. On the unity of pretense and earnestness, see Huizinga, Homo Ludens, 8, 22–26.

3. Maxwell, “Address.”

4. See Mahoney, “Histories of Computing(s).”

5. Starr, “Seductions of Sim.”

6. Chiaradia and Yacoub, “SimCity,” 88–89. See also Friedman, “Semiotics of SimCity,” which makes a similar argument; Wright’s comments in Bierend, “SimCity”; Myers, “Simulation as Play”; Bateson, “Play and Fantasy.” See Winsberg, “Computer Simulation,” which brings different yet related concerns (epistemology of science) to the table.

7. Turkle, “Seeing Through Computers.”

8. For a similarly spirited critique, see the goals of Ohshima et al., “STEPS,” which ventures an approach to some of the underlying issues.

9. Redmond and Smith, Project Whirlwind, 3; Aspray, John von Neumann; Light, “When Computers Were Women”; Campbell-Kelly et al., Computer; Haigh et al., ENIAC in Action.

10. Herman Goldstine, quoted in Light, “When Computers Were Women.”

11. L. Owens, “Vannevar Bush”; Campbell-Kelly et al., Computer; Haigh et al., ENIAC in Action. For the origin of the verb “to program” with ENIAC, see Grier, “To Program.”

12. Campbell-Kelly et al., Computer; Haigh et al., ENIAC in Action; chapter 4, this book.

13. John Tukey, who also coined the word bit, is credited with the term. See Tukey, “Concrete Mathematics”; Leonhardt, “John Tukey.” For an analysis that fruitfully complicates the stored program concept, see Hugh et al., ENIAC in Action.

14. Light, “When Computers Were Women”; Ensmenger, Computer Boys Take Over.

15. Hopper and Mauchly, “Programming Techniques.” For the genesis and context of the language metaphor, see Nofre, Priestley, and Alberts, “When Technology Became Language.”

16. Donald Knuth, quoted in Woehr, “Donald Knuth.”

17. Warren Weaver, quoted in L. Owens, “Vannevar Bush.” Of this opacity, Mahoney writes in “Histories of Computing(s),” 132, “It is the nature of a technological system. It is the price of computing power.” See also Mindell, Between Human and Machine, 3, 11, 15–16, 163; Hagen “Style of Sources,” 10.

18. Mahoney, “History of Software,” 15, writes that “the historian inherits the problems of software maintenance: the farther the program lies from its creators, the more difficult it is to discern its architecture and the design decisions that inform it.”

19. Mahoney, “History of Technology,” “Histories of Computing(s),” and “History of Software”; Ensmenger, “Digital Construction.”

20. As in platform studies, software studies, and critical code studies.

21. Mahoney, “Histories of Computing(s)” and “History of Software”; this book’s Introduction.

22. The appendix offers more background on the technique behind the reverse diagrams that explain SimCity’s programming.

23. McDermott, “Natural Stupidity”; Hayles, Posthuman, 227–246; Agre, Computation and Human Experience, 2, 14–18, 89–102, 234–235; Mateas, “Interactive Drama,” 131–132; Mahoney, “Histories of Computing(s),” 129; Zhu, “Intentional Systems.” See also Latour, Science in Action, 13–15, 29–.

24. Hutchins, Cognition in the Wild.

25. Whitson, “Studio Studies.” For counterexamples, see O’Donnell, Developer’s Dilemma; P. Owens, Double Fine Adventure; Schreier, Blood, Sweat, and Pixels and Press Reset; Whitson, Simon, and Parker, “Missing Producer”; and, to an extent, Parker, Whitson, and Simon, “Megabooth.” Mahoney, “Histories of Computing(s),” 127, makes a similar point about the history of software.

26. Latour, Aramis.

27. Galison, Image and Logic. For example, see Nelson and Sundt, “Architecture of Teachers’ Minds,” 169; SFI, “1992 Annual Report,” 1; chapters 1–9, this book.

28. F. Turner, Counterculture to Cyberculture. See the introduction for more background on my use of network forum, which I also apply in chapters 1, 3–5, 7, and 9, this book.

29. For scholarship on how software reflects and reconfigures expertise, practice, and social relations, see, for instance, Llach, Builders of the Vision; K. Baker, “World Processors.” Cf. Mahoney, “Histories of Computing(s),” 127.

30. Mahoney, “Histories of Computing(s)”; “History of Software”

Appendix: Reverse Diagrams

1. Librande, “One-Page Designs.”

2. Frustrated by the unwieldy and long-winded texts of a typical game design document, Librande set out to find a concise visual alternative. Although such diagrams are labor intensive to construct, Librande argues that they are worth the effort given that the prime function of a design document is efficient communication. Creating informative and appealing diagrams that stakeholders can communicate with and draw on, consequently, should be seen as central to a designer’s job. See Librande, “One-Page Designs.”

3. This approach can be seen to reconcile multiple approaches toward studying software. Some scholars favor attending to the architecture and processes of software, while others make the source code itself the object of analysis, as in Marino, “Critical Code Studies.” Wardrip-Fruin, Expressive Processing, favors the study of architecture and process, and notes that these two approaches complement one another. Reverse diagrams allow us to hybridize these approaches, flattening code and architecture into unified representations.

4. This is why diagrams sometimes contain cryptic-seeming names, which are in fact symbols referencing code entities.

5. Latour, “Visualization and Cognition.”

6. Tufte, Envisioning Information.

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Index

Page numbers followed by f indicate references to figures.

Abstraction, 305–306

Abstract symbolic systems, 44–45, 221, 305

Active creation of belief, 285–286

Activision, 243

Adaptation in Natural and Artificial Systems (Holland), 142, 143f

Affordances, 46–47, 61–62

Aircraft Stability and Control Analyzer (ASCA), 75

Airplanes, 185

Albrecht, Bob, 161–162, 165

Alexander, Christopher, 221

Allen, Paul, 162, 179

Altair, 153, 154, 162, 179

Alto, 170–171, 170f

Ambiguity, 12, 288

“America: Alternative Futures,” 25

American Mathematical Society, 44

Amiga 1000, 200

Analog concretization, 57, 100

Analog-digital dichotomy, 3, 10, 58–59, 60f, 61

Analog electronic computers, 63

Analogies, 50–51f, 51–52, 57–61, 58f, 301–302

Analogs, 47, 48f, 49, 50–51f, 51–52, 56

Analogy (concept)

Bush and, 60, 63, 64

concretization and, 57, 100

culture of, 55–57, 159, 166

definition and theory of, 58–60

diverse minds and, 52, 62, 64

domain mapping and, 60–61

Forrester and, 78–79, 100

hazards of, 149

Maxwell and, 51, 52

Shannon and, 57–58, 58f, 60, 63, 64, 114

simulation and, 61, 301–302

understanding and, 60, 61

Andreessen, Marc, vii, 236

Animate Tiles, 265, 265f

Antiaircraft problem, 74–75

Apple Computer, 9, 30, 143, 174. See also Macintosh

Apple Computer 1, 153

Apple Computer’s Advanced Technology Group, 217

Apple II, 11, 153–154, 159, 161, 166, 174–175, 179, 182, 186, 188–189, 191

“Application of System Dynamics to the Study of a Religious Experience” (Wolpert), 100, 101f

Apprenticeship, 4, 62

Arakawa, Minoru, 222

Arcade games, 154, 200

Army Corps of Engineers, 337n32

Ars Electronica, 377n75

Artifacts, 307

Artificial intelligence, 143, 162–164, 186, 220–221

Artificial Intelligence Laboratory, 163

Artificial life, 6, 11, 108, 142, 143, 150–151, 210, 217, 219, 220–221

Artificial Life (Levy), 151, 217

Artificial Life III conference, 217, 218

“Artificial Living Plants” (Moore), 123–124, 124f

Artists in Schools Program, 29

Asimov, Isaac, 69, 98–99

Asteroids, 175

“As We May Think” (Bush), 64, 76

Atari, 153, 225, 334n55

Atkinson, Bill, 176f, 179

Atom bomb, 103, 109–110, 303–304

Autodesk, 150, 151, 212

Automata, 117–118, 117f, 120, 350n57. See also Cellular automata; Self-reproducing automata

Automatic coding, 116, 305

Automatic curve follower, 343n15

Automatic fire control computers, 111

Automaticity, 297

Automatic machine, 112–113

Automation, 62

Autopilot, 72

Babbage, Charles, 45

“Back-coupling” technique, 72

Background editors, 176

Bagel, Richard, 203f, 205, 371n102

Baker, Erik, 246

Banks, Edwin, 352n92

Barricelli, Nils, 37n180, 146–147, 355n141

Barthelet, Luc, 243

BASIC, 11, 141, 148, 153–155, 159–162, 165, 171, 174–175, 182, 188

BASIC games, 345n67

Bayless, Don, 203–204

BBN, 162

Beer Game, 87

Behaviorism, 163

Bender, Walter, 288

Bennett, Richard, 80

Binary logic, 58

Biosim/BioSim, 376n59, 376–377n69. See also SimLife

Biosphere 2, 151, 214–215

Bit (term), 395n13

Bitmap displays, 156, 171

Bitmap editing, 156, 176, 207

Blatt, Roberta (Bobbi), 333n53

Blind Watchmaker program, 150

Boids, 210. See also Reynolds, Craig

Bollée, Léon, 45

Boorstin, Jon, 29, 34f

Bostrom, Nick, vii, 358n194

“Bouncing Ball,” 156–157, 156f

Bradbury Building, 40f

Brand, Stewart, 25, 148, 214

Braun, Jeff. See also Maxis

background of, 199–202

context of simulation and, 309

CTG and, 242

education market and, 16, 232

IPO and, 236, 237

Lucas and, 383n170

Maxis and, 11, 213, 246, 247

Nintendo and, 222

photos of, 203f, 206f, 223f, 231f

secondary stock offering and, 241

sequel and, 229–230

SimCity and, 202–204

SimEarth and, 215

SimLife and, 218

The Sims and, 248

studio strategy and, 231

venture capitalists and, 1, 225, 227

on Wright, 212

Wright and, 6, 7

Breakout, 153, 154, 174–175

Bremer, Michael, 203f, 205, 206f, 216–217, 329n6, 371n102, 376n63

Brockton Avenue Elementary, 332n43

Brøderbund

City Planner and, 198–199

Curtin and, 232, 329n6

education market and, 16

liminality and, 289

Maxis and, 225, 227, 236, 237, 247

Raid on Bungeling Bay and, 191–192

SimCity and, 6, 201–202, 207, 212

Brown, Gordon, 73, 78, 100

Brown, John Seely, 380n108

Bruner, Jerome, 23, 30, 31, 38, 363n90

Bruner’s “Man: A Course of Study” curriculum, 333n53

Budge, Bill, 168, 174–177, 179f, 180–181, 182, 189, 195, 284

BudgeCo, 175, 177, 177f, 179f, 181

Budget Window, 281f

Bulldozer tool, 197

Bulletin of the Santa Fe Institute, 218–219

Bungeling empire, 191, 367n31

Burks, Arthur, 125, 134, 143, 218

Bush, Vannevar

analogs and, 10, 52, 60

anti-aircraft problem and, 73

art and technique and, 83

atom bomb and, 103

calculus and, 54, 57, 311

circuits and, 50f, 51

differential analyzer and, 52, 54, 62–65, 109, 111, 166, 304

Engelbart and, 76

Hazen and, 72–73

interactive simulation and, 47, 49

multimodal thinking and, 155

philosophies of mind and, 164

physical aids and, 44–45

SimCity and, 3

Business, urban dynamics and, 90–92

Business simulation games, 85–87, 232

“Button Box,” 167f

C (programming language), 202, 313

Caggiano, David, 203f, 206f

CA Lab, 141, 150, 212, 218

CalArt’s School of Music, 30

Calculus, 45, 311

California Museum of Science and Industry, 199

California Pacific Computer company, 175

Calligrapher, 200, 204

Cape Cod Canal, 49

Carlston, Cathy, 207, 329n6

Carmen Sandiego, 16, 329n6

Carnegie Institution, 73

“Carrier Patrol,” 190–191

CASim, 141, 218

CD-ROMs, 230–231, 236

Cellular automata

analogizing and, 301

CASim and, 218

community of practice for, 10–11

context of simulation and, 309

diagrams regarding, 137f, 138f, 142f, 145f

legitimation and, 108, 135–152

in SimCity, 6, 276f

SimCity and, 71

simulation and, 108, 127–135

style of simulation and, 308–309

system dynamics and, 105–106, 196–197

visualization and, 107, 147

Wright and, 5, 102, 195

Center for City Building Educational Programs, 333n51

Central place theory, 194, 195, 275

Chaos (Gleick), 150

Chaos theory, 136, 143–146, 150

Chaos: The Software, 150

Character editor (Chedit), 191

Character library, 317, 259–266f. See also Tiles

Characters, 190, 256–258, 258f, 259–266f. See also Tiles

Chedit, 191

Chess, 105, 136, 221, 296

Chevron, 6, 225, 226, 245, 299

Children, evolution of computers and, 162–168

Children’s Television Workshop, 192

Christaller, Walter, 194, 275

Circuit analogies, 49, 50–51f

Circuit-logic analogy, 58, 114

City Building Education, 16, 18, 30, 31f, 35

City Building Education (Nelson), 332–333n46

“City of the Future,” 27

City Planner, 5, 6, 193–199, 198f, 201, 212, 272. See also Micropolis; SimCity

Civil rights movement, 87

Classic game model, 288

Clinton, Bill, 232

Clocks, 254–256, 265, 282

Closed-loop figure, 83f

Closed-system approach, 89–90

Club of Rome, 69–70, 98

Codd, E. F., 126–127, 126f, 128, 132, 352n92, 353n95, 369n69

Code, 112, 114, 305, 313–318. See also Automatic coding

CoEvolution Quarterly, 214

“Cognition in the wild,” 309–310

Cognitive science, 163–164, 220–221

Cognitivism, 173

Collectivity, 3

Collins, John F., 87–88, 94

Comanche: Maximum Overkill, 383n165

Commodore 64, 190, 195, 197, 198f, 204, 207, 237, 256, 282

Compilers, 80, 82, 116, 305

Complex adaptive systems, 142, 211

Complexity science, 106, 108, 386n224

Complex systems, 93–94, 136, 195, 219

Compton, 22f, 26f,

“Computable Numbers” (Turing), 350n57

Computer-aided design (CAD), 157–158

Computer Chronicles, 215

Computer Gaming World, 209, 212

Computer mouse, 171

Computer Recreations, 147, 148, 150, 151

Concreteness

bias against, 45–46

digital computing and, 64, 159

Connection diagrams, 54–55, 55f, 56f, 83

Conrad, Brian, 371n107

Constructionism, 166, 168, 287

Construction play, 18

Constructivism, 166

Contact languages, 5, 102, 132, 140, 152, 219, 244–245, 288, 328n12. See also Network forums

Context, 309

Continuous models, 351–352n77

Control theory/systems, 72, 78–80, 92

Conway, John, 106, 108, 130–132, 137, 143, 145–146, 148, 189, 212, 307

Conway’s Game of Life

cellular automata and, 106, 108, 134, 145–146, 148–149

description of, 105, 130–132

diagrams regarding, 131f, 132f

Langton and, 143

popularity of, 212, 307, 308

simulation community of practice and, 106

Wolfram and, 137, 140

Wright and, 189

Core Technology Group (CTG), 239–240, 242

Costume activities, 23, 24f, 25

Counterintuitivity, 94–95

Crime Map, 270, 271f

Curtin, Claire, 16, 232, 243, 329n6

Cyberiad, The (Lem), 196, 197

Cybernetics, 71, 117, 211

Daisyworld, 214

Data flow, diagram of, 318

Data General, 251

Davidson, Gordon, 333n51

Davis, Cary J., 382n150

Davis, Martin, 111

Dawkins, Richard, 150, 218, 377n70

Decay Maps, 271f

Decentering the teacher, 187

Decision problem, 112

Delta Logic, 225, 227

Dennett, Daniel, 148–149

Derber, Bob, 225

Descartes, René, 116, 117

Desegregation, 30

Design in the wild, 9, 309–310

Dewdney, A. K., 150

Dewey, John, 23, 38, 43, 166

Dick, Philip K., 200

Differential analyzer

abstraction and, 305–306

analogy and, 59, 61

as artifact, 307

Bush and, 47, 52, 54, 62–65, 109, 111, 166, 304

connection diagrams and, 53f, 54–55, 55f, 56f

ENIAC and, 110

Logo compared to, 166

Owens and, 339n54

photo and diagram of, 53f

power of, 304

precursor to, 72

Shannon and, 57, 58, 158

Sketchpad versus, 159

use of, 54–55

Digital (term), 58, 59, 61

Digital computing, 63–65

Digital Equipment Corporation (DEC), 84, 161, 251, 360n20. See also PDP; VAX

Disaster menu, 198

Disasters, 198, 205, 221, 255f, 283f, 291–292

Discover, 141, 148

Discursive practices, 307–308

Disney, 174, 227, 370n89

Disney Software, 227

DNA, 104, 124, 351n69

Dollhouse, 32, 221–222, 228, 229, 233–235, 238f, 239, 240f, 242, 243–244, 325n p.240. See also Home Tactics; Jefferson; Project X; Sims, The

Domain mapping, 61

Doner, Chris, 201

Doom, 234, 244

Doornbos, Jamie, 234, 240, 242

Dr. Dobbs Journal, 161–162

Dr. Wright, 223, 224, 224f, 245

Duke, Richard, 25, 27, 86, 87, 228, 302

Dungeons & Dragons, 188

Dunnigan, Jim, 366n11

Durrett, Brett, 371n107

Dux Software, 391n1

Dynabook, 30, 169, 169f, 171, 172, 173

Dynamica, 342n7

Dynamical systems theory, 11, 135, 136, 139–140

Dynamic models/simulations, 43–44, 46, 47

Dynamism, 129

DYNAMO compiler, 82, 84, 90, 307

Dyson, Esther, 225, 226

Dyson, Freeman, 225

Eames, Charles, 9, 19f, 24f, 27, 28f, 29, 39f, 199, 332n45, 333n46, 333n49

Eames, Ray, 9, 27, 30, 199, 332n41

Eames Office, 19f, 20f, 22f, 24f, 26f, 28f, 29, 33f, 35, 36f, 37f, 39f, 40f

Eames Office “Kid City” slide show, 19f, 20f, 22f, 24f, 28f, 29, 39f, 40f

Earth First!, 374n38

Easter eggs, 1

Edge of chaos, 143, 145–146, 294, 393n25

EDSAC, 63, 115

Education market, 16

EDVAC, 63, 110–111, 114–115, 121, 304, 352n78

Eisenhower, Dwight, 103

Electromagnetism, 51

Electronic Arts (EA), 1–2, 7–9, 179–181, 180f, 213, 239, 243–244, 245–246, 249, 287, 313

Electronic Frontier Foundation, 288

Electronics Boutique, 213

Elementary units, 118–119, 123

El-Fish, 217, 228, 381n126

Eliza, 200

Ellington, Duke, 29

Ellis, Cliff, 205

Emergence, 106, 149, 151, 222–223, 386n224

Embodied (concept), 3, 52, 61–62, 64, 165–166, 168, 221, 244

Empathy, 197, 198, 391n42

Endogenous systems, 92

Engelbart, Douglas, 64, 76, 169

England, Nick, 333n51

ENIAC (Electronic Numerical Integrator and Computer), 63, 109, 110, 114, 119, 120f, 304

Eno, Brian, 149

Ensmenger, Nathan, 306

Entscheidungsproblem, 112

Environmental Simulation Laboratory, 25

Evans, Scott, 391n4

Evocative objects, 4

Evolution, 116, 118, 134, 142, 146–147

“Exploring the Nature of Simulation versus Reality” (Levy), 210–211

Failure, 198–199, 233, 284

Famicom, 192

“Fantastic Cities,” 27

Feedback systems, 71–72, 79–80

Feurzeig, Wallace, 162, 168

Finite subsystems, 123

Fire Analysis, 272

Firepower pinball machine, 175

Fire Radius Map, 272

Fire Station Map, 270, 271f, 272

Flight Simulator, 189, 284

Flight simulators, 75–76, 157, 303

Flow diagrams, 83

Flows, 82, 90, 92

Foley, Mick, 201, 221–222, 229, 325n p.240, 371n107, 390n34, 390n38

Folklore, 159–162

Football, American, 393n18

Forbes, 225

Forbus, Ken, 248–249

Ford Foundation, 82, 86, 87, 331n31, 345n74

Ford Motor company, 76

Forrester, Jay

on art of simulation, 83–84

business simulation games and, 85, 86–87

Collins and, 88, 94

documentation and, 316

industrial dynamics and, 78–85

intention of, 302

intuitive decision making and, 87

Joffe and, 194

Kahn and, 347n106

MIT School of Industrial Management and, 77

MIT Servomechanism Laboratory and, 73–74

new analogy from, 77–79

prose narratives of, 308

rate versus valve and, 389n28

reception of simulations by, 212

Schelling versus, 96–97

servomechanisms and, 10, 72–74, 78–79, 90, 91

SimCity and, 100, 253, 273

simulation practice of, 70

on social systems, 69

spatial relations and, 275

system dynamics and, 70–71, 72, 98, 99–100, 102, 126, 276f, 309

urban dynamics and, 5, 88–90, 92–95, 185, 276

Whirlwind and, 63, 75–76

Wright and, 5, 194

Forrester’s Law, 95

FORTRAN, 160, 161

Foundation novels (Asimov), 69

Fox, Phyllis, 90, 344n50

Fox, Teri, 321

Fractals, 149–150

Fredkin, Edward, 134, 352n92

Fuller, Buckminster, 25, 30

Full Tilt! Pinball, 231

Future Cities design contest, 232

“Future City,” 26f

Gaia hypothesis, 213–214

Galactic Empire, 69

Galison, Peter, 288, 310, 328n12

Game of Life. See Conway’s Game of Life

Game sprites, 71

Game theory, 86, 96

Gardner, Martin, 129–132, 134

Garvey, Catherine, 392n12

Gates, Bill, 161, 162, 179

Gebelli, Nasir, 189

Gehry, Frank, 9, 18, 27, 29, 30, 34f

General Electric, 77–78

Gentner, Dedre, 59–60

Geographical information systems (GIS), 287, 299

Gerson, Fred, 233, 236, 241

Gilmore, John, 288

Glatt, Ruth, 21

Gleick, James, 150, 217

“Gliders,” 131, 131f, 132f, 134, 149

Global Business Network (GBN), 214, 246

Global War, 187

Go (game), 130, 194–195, 296, 393n20

Goal-seeking behavior, 92, 96–97

Gödel, Kurt, 112

Godzilla, 253, 282, 283f

Goldberg, Adele, 172

Golding, William, 214

Goldman, Daniel, 206f, 371n107

Goldstine, Herman, 109, 110

Goodland, John, 333n51

Google PageRank algorithm, 342n104

Graphical drawing, 55–56

Graphical thinking, 54–55, 55f, 57, 319

Graphical user interface (GUI), 11, 57, 64, 154–155, 168, 173, 174, 175–178, 181, 195–196, 297

Graphics, 156–159

Greene, Suzie, 228, 242

Groundhog Day, 234

Hacker culture, 165, 174

Hales, Steve, 204

Halprin, Lawrence, 30

Hamilton, Susan, 333n51

Hammurabi, 345n67

Hanks, Nancy, 29, 333n47

Hansen, Jim, 375n42

“Happy trash,” 18

Harmonic analyzer, 45

Harper, Robin, 229, 376n63

Haslam, Fred, 215, 229

Hawkins, Trip, 179–181

Hazen, Harold, 49, 72–73, 78

“Helium City,” 26f

Hewlett-Packard, 161

Hexagonal game boards, 187–188

Hilbert, David, 111–112

Hiles, John, 376n63

Holland, John, 125–126, 141–143, 142f, 143f, 147, 218, 376n69

Homebrew Computer Club, 154, 162

Homelessness, 95

Home Tactics, 238f

Hopkins, Don, 287–288, 317, 391n1

Horn, Bruce, 174

Hospital demand, 277, 277f

Housing, urban dynamics and, 90–92

Human computers, 109, 110, 112–113, 114, 303–304

Humble, Rod, 391n4

Hutchins, Edwin, 9, 309

Hypertext, 64

Ice Bag (Oldenburg), 199–200, 202

Ice Bag–Scale C (Oldenburg), 40f

Image and Logic (Galison), 328n12

Imagination

children’s city planning and, 21

role of, 2–3

simulation and, 285–286

Imagineer, 378n87

Industrial dynamics, 88, 98

Industrial Dynamics (Forrester), 82–83, 83f

“Industrial Dynamics: A Major Breakthrough for Decision Makers” (Forrester), 78, 81f

Infinite lattice, 119–121, 120f, 123, 127–128, 135, 142

Infogrames, 243

Information, as element in industrial dynamics, 82

InfoWorld, 209, 224–225

Initial public offering (IPO), 207, 213, 227, 233, 236

Integraph, 72

Intel, 226

Interactive computer graphics, 156–157, 159

Interactive computing, 64

Interactive Softworks, 200

Interleaving, 254–256

International Conference of the System Dynamics Society, 100

Internet search engines, 64

Interval Research, 226

Iwata, Satoru, 378n88

Jacobs, Jane, 368n46

Janeway, Bill, 225–226, 227, 232–233, 241, 247

Jefferson, 242, 243. See also Dollhouse; Project X; Sims, The

Jobs, Steve, 153, 154, 174

Joffe, Bruce, 194

Johnson, Lyndon B., 29, 76, 87, 94, 194

Johnson, Tim, 206f

Jones, James Earl, 29

Jones, Joell, 189

Jurassic Park, 151

Juul, Jesper, 288, 296

Kafai, Yasmin B., 378–379n93

Kahn, Herman, 347n106

Karakotsios, Ken, 217–218, 219

Kay, Alan, 30–31, 31f, 37–38, 168–170, 169f, 171–174, 182, 287, 302, 334n55

Kelly, Kevin, 150, 211, 214, 217, 377n70

Kelvin, Lord, 45, 111

Kemeny, John, vii, 121, 122f, 123, 160–161, 162, 165, 169

Kennecott Copper, 82

Kepler, Johannes, 43

Keynes, John Maynard, 226

Kid City (documentary), 29, 34f

“Kid City” (slide show), 29. See also Eames Office “Kid City” slide show

Kidder, Tracy, 251

Kilham, Edward, 200–201

Kinematic model, 124

Kodama, Aki, 378n87

Kono, Shintaro, 378n87

Kressel, Henry, 225, 226–227, 232, 236, 241, 247, 376n63

Krishna, 103

Kurtz, Thomas, 160–161, 162, 165, 169

LA Goals Program, 18, 21, 330n18

Lampson, Butler, 170

Land Value Map, 269f, 270, 271f, 272, 273

Land value model, 194

Langton, Christopher, 142–145, 144f, 151, 217, 218, 219, 294, 309, 377n70

Language communities, 55–56

Latour, Bruno, 8, 247, 310, 316

Lattice abstraction, 119–121, 120f, 123, 127f, 135, 142

Learning, as sociocultural process, 4

“Learning by doing,” 155, 165, 168, 181–182, 232, 292

Learning Company, The, 236

Learning Research Group (LRG), 170, 173–174

Learning theory, 23

Le Corbusier, 185, 210

Lee, Harper, 29

Legitimacy exchange, 5, 40, 140, 152, 168, 245, 288. See also Network forums

Lego, 178

Lego–LOGO lab, 150, 217

Leibniz, Gottfried, 45, 111, 115

Lem, Stanisław, vii, 196, 197

Lemonade Stand, 345n67

Levels, 80, 82, 88, 301

Levy, Stephen, 151, 210–211, 217, 218, 354n119

Lewin, Kurt, 88

Librande, Stone, 314–315

Licklider, J. C. R., 159, 181

Liddle, David, 108n380

Life (game). See Conway’s Game of Life

“Life and Death on a Computer Screen,” 148

Lifeline, 132, 308

Light, Jennifer, 87

“Light gun,” 156, 156f

Limitless environments, 89–90

Limits to Growth (Meadows et al.), 69–70, 98, 99

Lincoln, Howard, 222

Lisa (Apple project), 175

Lisp, 162

Little Computer People, 234

LOGiN, 378n87

Logo, 155, 162, 165–166, 167f, 169–170, 172, 173, 182

Los Alamos National Laboratory, 140

Los Alamos National Laboratory Center for Nonlinear Studies, 217

Los Angeles City Planning Board, 25

Los Angeles County Museum of Modern Art, 199

Los Angeles Open School, 30, 31f, 217

Los Angeles Unified School District, 29

Lovelock, James, 214

Lucas, George, 383n170

LucasArts, 383n170

Luria, A. R., 163

Machine language, 189

Machine models of biological organisms, 5

Macintosh, 154–155, 168, 174, 176f, 203, 204, 208f, 218. See also Apple Computer

Mackraz, Jim, 239, 242, 385n205

MacPaint, 176f, 204

MacWorld, 210, 218

Mahoney, Michael, 251–252, 310, 395n17, 395n18

Malaby, T. M., 392n17

Mandelbrot set, 149–150

Manhattan Project, 76, 110, 121

Manutius, Aldus, 169

Map data flow, 270–272, 271f, 315, 318

Mapping(s), 60, 315–316

Map Scan, 256, 265, 266f, 267, 271f, 272, 273, 274, 276–277, 318

Mapstone, Bobbi (Robina), 333n49

Maps Window, 268, 268f, 270, 281–282

Map title, anatomy of, 258f

Margulis, Lynn, 146, 214

Marion, Ann, 334n55

Mark Taper Forum, 30, 331n26

Martin, Jenny, 228, 229, 242

Maslow’s pyramid of needs, 221

Mathematica, 219

Mathematical Games column, 105, 129–130, 147, 151

Mathematical model of a human being, 111, 113

Mattrick, Don, 243

Maxis. See also Braun, Jeff; Wright, Will; individual Sim titles

ambiguity and, 211–212

art department of, 228

branding and, 229, 238

business simulation games and, 224–225, 227–228

cellular automata and, 151–152

at crossroads, 11, 212, 246

education market and, 16, 227, 232

IPO of, 213, 227, 233, 236

contact language of simulation and, 5, 152, 219, 245

llamas and, 229, 247

LucasArts and, 383n170

naming of, 205

Nelson and, 16, 31–32, 232

Nintendo and, 222–224

sale of, 1–2, 246

secondary stock offering and, 240–241

SFI and, 218–219

share price for, 241f

SimCity and, 6, 213–230

SimLife and, 217–218

studio strategy and, 230–236

unraveling of, 6–7, 237–249, 310

venture capitalists and, 225–227

Wright and, 207, 213, 234, 247

Maxis Business Solutions, 227, 232

Maxwell, James Clerk, 51, 52, 57

Maze War, 201

McCormick, Justin, 216, 218, 221, 228, 366n13, 376n63

McCulloch, Warren, 114–115, 116, 118, 135, 140, 163

McLuhan, Marshall, 169, 364n104

McNamara, Robert, 76

Meadows, Dennis, 342n5

MECC, 236

Meccano construction toys, 63

Media, evolution of computers and, 168–174

Meier, Richard, 86

Memex, 64

Memory, 304

Mental models, 99–102

Messaging system, 284–285

Metacognitive reflection, 100

Metamathematics, 111–112

Metropolis, Nicholas, 218. See also Monte Carlo (simulation)

Micropolis, 202–204, 205, 313, 391n4. See also City Planner; SimCity

Microsoft, 162, 174, 179

Microworlds, 168

Miller, Avram, 380n108, 385n200

Mindell, David A., 339n59, 342n10

“Mindshare,” 219

Minsky, Marvin, 30, 163, 224

MIT Media Lab, 30, 217, 224, 287, 288

MIT network analyzer, 49, 52

MIT School of Engineering, 44

MIT School of Industrial Management, 71, 77, 82

MIT Servomechanism Laboratory, 71, 73, 75, 157

MIT Sloan School of Management, 77, 98

MIT Systems Dynamics National Modeling Project, 226

MIT Urban Systems Laboratory, 345n74

Miyamoto, Shigeru, 212, 222–224, 223f, 386n224

“Models of Segregation” (Schelling), 95–96

Moglen, Eben, 391n4

Molecular biology, 104

Molyneux, Peter, 376n63

Moncreiff, Bruse, 86

Monopologs, 85–86, 85f

Monson, Ander, 375n40

Monte Carlo (simulation), 127, 129, 351n74

Montessori, Maria, 43, 187

Montessori schools, 187

Montreal Protocol, 375n42

Moore, Edward, 123–124, 124f

Moore School of Electrical Engineering, 109

Morgan Stanley, 240–241

Moses, Robert, 38, 43, 185, 210, 302

Mouse in the Maze, 157

M.U.L.E., 201

Murray, Janet, 7, 285, 294, 318

“Mutual of Omaha’s Wild Kingdom,” 367n25

My Computer Likes Me When I Speak in BASIC, 161

“My house” floor plan, 24f

NASA, 141–142, 141f, 199, 213–214, 387n240

National Defense Research Committee (NDRC), 65, 73, 103, 109

National Endowment for the Arts (NEA), 29, 333n46, 333n47, 333n51

National Science Foundation, 161, 342n107

Nature, 124

Negroponte, Nicholas, 224, 287

“Neighborhood tipping” model, 96

Nelson, Doreen Gehry

Duke and, 86

Maxis and, 16, 31–32, 232

progressive educational ideas and, 166

SimCity and, 287, 302

simulation method of, 9–10, 16–41, 17f, 19f, 34f, 43, 228

Nelson, Rolf, 25

Netscape, 236

Network analyzer, 72, 343n15, 350n45

Network celebrities, 5, 31, 102, 140, 168, 245

Network forums, 5, 7, 10, 40, 140, 212, 244, 310

“Neuron Analogy,” 114–115

“New economy,” 246

“New Frontier, The,” 377n75

New School, 186

Newsweek, 210, 217, 236

New York Times, 210, 212, 215, 217, 232

Nintendo, 6, 192, 212, 222–224, 229, 245, 299

Nixon administration, 29, 94

Nonlinearity, 79–80, 93, 105, 110, 136

Norman, Charles, 391n1, 391n4

Nuclear weapons, 103

“Object Interview” protocol, 331n26

Object-oriented programming (OOP), 64, 172, 173, 239, 384n186

Objects, 283f

Office of Naval Research, 75, 342n107

Oldenburg, Claes, 25, 40f, 199–200

101 BASIC Computer Games, 154, 161

One Laptop Per Child (OLPC) initiative, 287–288, 299

One-page design diagrams, 314–316

Online encyclopedias, 64

Open-ended sandbox, 7

Oppenheimer, Robert, 103, 110

OPUS, 172

Oscillation, 74–75, 78

Owens, Larry, 55, 339n59, 339n60

Palo Alto Research Center (PARC), 168, 170, 174, 175, 179, 181, 226, 387n240

PanzerBlitz, 187, 369n80

Papert, Seymour, 30, 38, 64, 163–170, 172, 173, 224, 287, 334n55

Paradox, 12

Pasadena Art Museum, 40f

Pascal (programming language), 189

Pascal, Blaise, 45, 111

Paterson, Michael, 206f

Pattern growth, 127–128, 127f, 128f, 129f

Pattern Language, A (Alexander), 221, 234

Pavlov, Ivan, 163

PC Forum, 225

PDP, 360n20, 361n33

Pea, Roy, 329n6

Peckham, Matt, 378n87

Penrose, Lionel, 124–125, 125f

People’s Computer Company (PCC), 161–162

Perceptual latency, 92

Perlman, Radia, 167f

“Personal Computer for Children of All Ages, A” (Kay), 169

“Personal Dynamic Media” (Kay and Goldberg), 172

Physica D, 141

Piaget, Jean, 30, 38, 44, 45, 163, 164, 166, 168, 171

Pilot ACE, 163

Pinball Construction Set

Apple and, 168

design and, 284

development and evolution of, 174–181

editing versus playing and, 197

evolution of computers and, 154

images of, 177f, 178f, 179f, 180f

legacy of, 181–182

MacPaint contrasted with, 204

Wright and, 6, 189, 195, 207, 243, 307

Pitts, Walter, 114–115, 116, 118, 135, 140

Player creativity, 203–208, 234

Player modification, 234

Play/play theory, 12, 288–290, 299–300

Play School, 44f

Pluralism, 52–57

Poincaré, Henri, 112

Pokhilko, Validimir, 376n63

Police Map, 270, 272

Pollution Map, 270

Pong, 175

Poole, Sam, 227, 231, 241, 242–243

Population, urban dynamics and, 90–92

Population Density Map, 270, 271f

Portola Institute, 161

Power Scan, 272

Pratt, Caroline, 43, 44f, 166

Primary Map, 258f, 269f, 270, 272, 276

Princeton Institute of Advanced Study (IAS), 146

Print Artist, 231

Print Shop, 370n97

“Probot,” 193, 201

Probst, Larry, 384n183, 385n205

Procter & Gamble, 227, 381n127, 384n183

Project MAC, 345n74

Project X, 234–235, 237, 239, 240, 242. See also Dollhouse; Jefferson; Sims, The

Proof theory, 111–112

Providence Journal, 210, 299

“Pseudocode,” 305

Psychohistory, 69, 70

Punch cards, 160, 188, 200, 304

Purium, 15–16, 27, 32, 35

Quantum theory, 103–104

Racial segregation, 95–96, 96f

Raid on Bungeling Bay, 5, 6, 185–186, 190–193, 190f, 207, 272, 282, 297, 390n34

RAND Corporation, 76, 85–86, 87, 95, 187–188, 360n25

Raskin, Jef, 174

Raster Blaster, 175–176

Rates, 82, 88, 90–91, 279, 301, 389n28

Rayleigh, Lord, 49

Real-time clock, 254, 256, 265, 282

Reasoning aids, 44–45

Redlining, 95

Redmer, A. J., 201

Relative attractiveness, 92, 280

“Remediation” concept, 364n104

Representation, concept of, 46–47

Residential Valve, 279

Resnick, Mitchel, 150

Restless Clock (Riskin), 350n57

Reusable technical infrastructure, 80

Reverse diagrams, 252, 313–319

Reynolds, Craig, 217

Ringling Bros., 239

Riskin, Jessica, 350n57

Rockefeller Differential Analyzer, 55, 57–58, 63, 110

Rogers, Henk, 381n126

Role-playing, 27, 38

Rucker, Rudy, 150–151, 218

Rusch, Chuck W., 333n51

Russell, Bertrand, 112

SABRE, 76

Sagan, Carl, 213

Salk, Jonas, 30

Salvador, Phil, 374n38

Santa Fe Institute (SFI)

cellular automata and, 146, 152

founding of, 140

Holland and, 142

Langton and, 143

Maxis and, 11, 151–152, 212, 217–219, 226, 227, 245, 246

SimCity and, 6, 108, 211, 299

Wright and, 232

Scenarios, 15, 198, 204, 205, 292, 293f, 295–296

Schechner, Richard, 392n17

Schelling, Thomas, 95–96, 96f–97, 97f, 102

Schema, 308

Schrödinger, Ernest, 103–104

Scientific American

artificial life and, 123–124

cellular automata and, 105–106, 133f, 141

Computer Recreations and, 147

fractals and, 149–150

Gardner and, 129–130

Life and, 131, 308

man and machine and, 104–105, 121

SimAnt and, 217

SimLife and, 218

Wolfram and, 148, 355n141

Scirica, Joe, 233, 242–243

Scratch, 174, 182, 379n93

“Scripting the interactor,” 294, 390n40, 393n23

Seef, Norman, 180

Segregation, models of, 95–98, 96f, 97f

Seldon, Hari, 69, 70, 98–99

Seldon Plan, 69

Self-organizing, 105–106, 108, 137, 140, 149, 246

Self-replicating robots, 141–142, 141f

Self-reproducing automata, 106, 108, 116–127, 121f, 122f, 369n67

Self-reproducing loops, 144f

Semi-Automatic Ground Environment (SAGE), 76

Send Messages, 284

Servomechanisms, 10, 71–75, 78–79, 91–92, 279–280

Set Valves, 276–277, 278f, 279–280, 285, 318

“Seventh Sally, The” (Lem), 196, 197

Shannon, Claude, 57–58, 58f, 60, 63, 64, 114, 116, 157, 158

Shazam, 172

Shneiderman, Ben, 365n128

SimAnt

agent-based model and, 235

description of, 215–217, 220–221

exhibit with, 232

images of, 215f, 220f

Martin and, 228

Maxis and, 151, 246

reception of, 229, 233

The Sims and, 244

Wright and, 8, 16

SimAntics, 235, 240

Sim-Business, 383n170

SimCircus, 239, 242

SimCity. See also Wright, Will

ambiguity and, 4, 211–212, 224, 288–289, 299–300

analogs and, 51

box design for, 291–292f

cellular automata and, 106, 151

clocks of, 254–256, 265, 282

as construction set, 297–298

context of simulation and, 309

control instabilities and, 74

critique of, 7, 302–303

description of, 2

development of, 6, 11, 202–208

economic model of, 10, 275–282

feedback from, 296f

as game, 295–297

Godzilla version, 291–292f

homelessness and, 95

honors for, 209

landscape of, 256–275

for Macintosh, 208f

maps of, 265–273

messages in, 284–285

naming of, 205

Nelson and, 38

network forum for, 244–245, 288–289, 299–300

Nintendo and, 222–224

objects in, 282–284, 283f

opacity of, 302–303

play-centric analysis of, 12

playing, 287–300, 293f, 296f

as playmate, 291–293

reception of, 209–211

release of, 1–2

sequel to, 229–230. see also SimCity 2000

simulation design of, 11–12, 251–286

simulation hybridization and, 97–98

source code for, 313–314

success of, 6, 213

system dynamics and, 70–71, 276f

system overview for, 253f

as toy, 293–295

user interface of, 11, 195, 268f, 281f, 293f, 296f, 297

Whole Earth Review and, 151

SimCity 2000, 229, 230, 230f, 232, 233, 236, 239–240, 284, 329n6

SimCity 3000, 1, 239, 242, 329n6

SimCopter, 1, 234, 237, 239–240

SimEarth, 8, 16, 151, 213, 214, 215, 215f, 229, 233, 246

SimEnergy, 227

SimEnvironment, 227

SimGolf, 231

SimHealth, 227, 232

Similitude, 47–52

SimLife, 151, 217–218, 219–220, 228, 229, 233, 246, 329n6. See also Biosim/BioSim

SimMars, 387

SIMPLE (Simulation of Industrial Management Problems with Lots of Equations), 80

“Simplified Flow Diagram Model of the Dark Night of the Soul” (Wolpert), 100, 101f

SimPower, 227

Simpula, 172

SimRefinery, 6, 225, 226, 227

Sims, The. See also Dollhouse; Jefferson; Project X

box design for, 238f

Braun and, 237

context for, 247–248

Curtin and, 16, 243

development of, 235, 243–244, 310

player creativity and, 203–208

success of, 2, 8, 246

support for, 213

SimSite, 227

Sims Online, The (TSO), 8

SimTown, 232

Simula, 171

Simulate, 255–256, 318

Simulation

affordances of, 61–62

as analogy, 301–302

as cognition, 3

computing and, 116

as culture, 2–3

interdisciplinarity and, 41, 98, 102, 151–152, 310

network forums and, 5, 244, 309

reality as, vii, 139–140, 149, 196–198

software as, 3–4, 306, 310

as world building kit, 301–302

Simulation clock, 254–255, 265, 282

Simultaneity, illusion of, 254

SimWorld, 13, 239–240, 392

Sketchpad, 158, 158f, 171

Skinner, B. F., 163

Sky Chase, 207

Sloan, Alfred, 77

Smalltalk, 155, 171–174, 176f, 182

Smith, Alvy Ray, 37n180, 131, 133f, 354n119, 355n141

Smithsonian Institution, 15–16, 18, 27, 29, 329n2, 331n38, 333n49

Sniper!, 187

Sociocultural learning, 174

Software

compared to, 114, 154

history of, 304–305

network forums and, 310

as simulation, 3–4, 306, 310

study of, 3–4, 251–252, 313–316

Soleri, Paulo, 368n46

Solomon, Cynthia, 166, 168, 172

Soul of a New Machine (Kidder), 251

Space, system dynamics model of, 346n95

Space Invaders, 147–148

“Spaceship Earth,” 214–215

Space-time cellular automata diagrams, 133f, 137, 137f, 138f, 139f, 145f, 147

Spacewar!, 148, 159, 161, 166, 169, 169f, 200, 212

Speculative effects, 92

Spielberg, Steven, 151

Spore, 8

Sprites, 282

Sputnik, 161, 166

“Statistical Mechanics of Cellular Automata” (Wolfram), 106

Status bits, 257–258

Steinem, Gloria, 30

Stella, 342n7

Stibitz, George, 58, 59

Strickland, Rachel, 334n55

Strobel, Robert, 203

Strong Museum of Play, 374n38

“Structural Analysis by Electric Circuit Analogies” (Bush), 47

Stupid Fun Club, 8

Style, 308–309

Sulphur Springs School District, 22f, 333n48

Sumerian Game, 86

Sun Microsystems, 287, 299

Super Mario Bros., 282

Super Nintendo, 372n119

Sutherland, Ivan, 158, 158f, 170

Sutherland, William, 158

Sutton-Smith, Brian, 288, 392n8, 393n19

“Swarm” project, 219

Symbiogenesis, 146–147, 146f

Symbolic mnemonics, 116

Symbols, standard, 55f

Synanon, 25

System dynamics

analogizing and, 301

context of simulation and, 309

Forrester and, 70–71, 72, 98, 99–100, 102, 126, 276f, 309

history of, 106–107

Janeway and, 226

SimCity and, 280–281

space and, 346n95

Wright and, 70–71, 100, 102, 194–195, 196–197, 281, 307–308

System Dynamics Review, 342n7

Systems Dynamics Society, 342n7

Tamagotchi, 234

Teleological Society, 117

TeleSim, 227

Temp Map, 270

Tensegrity, 28f

Tetris, 7, 213, 223, 224, 296–297, 381n126

Texas Instruments, 82

Thacker, Chuck, 170

Theoretical neurophysiology, 114

“Theory of Automata” (von Neumann), 350n57

Theory of Self-Reproducing Automata (Burks), 125, 130

“Theory of Servo-Mechanisms” (Hazen), 72

Thermostat servomechanisms, 72

“Theseus” (Shannon), 157

Thinking as embodied process, 61–62, 64

Three-dimensional cam, 45

Tic-Tac-Toe, 153, 174, 199

Tide calculator, 111

Tierra (simulation), 357n179

Tiles, 256–257, 257f, 258f, 259–264f, 265f. See also Characters

Time, 215

Time-sharing, 160–161, 341n92

Toffoli, Tomaso, 134–135

Toho, 213

Traffic, 274

Traffic Density Map, 272, 274

Transformation activities, 23, 25

Truss bridge, 48f

Tucker, Eugene, 333n51

Tufte, Edward, 315

Tukey, John, 395n13

Turing, Alan

biology and, 104

cellular automata and, 139–140

EDVAC and, 304, 349n25

influence of, 64, 119

intelligence and, 357n175

philosophies of mind and, 164

Pilot ACE and, 163

universal computer and, 105

von Neumann and, 118–119, 135

work of, 4, 111–116

Turing machine, 113, 115

Turkle, Sherry, 4, 45, 64, 165, 303

Turner, Fred, 5, 246, 310, 374–375n40

“Turtle,” programmable, 165, 167f. See also Logo

TWANG, 172

Twenty-nine-state automaton, 121, 121f, 126, 126f, 132

“Twenty Things to Do with a Computer” (Papert and Solomon), 166, 172

TX computers, 360n20, 361n33

Tye, Ken, 333n51

UCLA School of Education, 30

Ulam, Stanisław, 119–120, 123, 127–128, 130, 131, 135, 147, 354n121

Universal computer, 108, 109–116, 139–140

Universal constructor, 118, 119, 121f

Universal machine, 113

University of Michigan, 25, 57, 125, 134

Unix platform, 287–288

Unnatural Selection, 217

Urban dynamics, 87–95, 89f, 185, 276

Urban Dynamics (Forrester), 5–6, 88, 90–95, 91f, 94f, 97, 98, 194, 279, 280, 308

Urban renewal, 87–88, 92, 94, 185

US Air Force, 75, 76, 85–86

US Atomic Energy Commission, 103

US Department of Defense’s Advanced Research Projects Agency (DARPA), 342n107, 387n240

US Department of Housing and Urban Development, 94

US Navy Bureau of Ordnance, 73

Vacuum-tube oscillators, 72

Valves, 279, 389n28

Van der Ryn, Sim, 30

VAX, 251

Venture capitalists, 299

Victor, Bret, 337n21

Vietnam War, 87

VisiCalc, 147–148, 154

Vivarium, 30–31, 217, 334n55

von Neumann, John

Barricelli and, 146–147

biology and, 104, 114–115, 350n50

Burks and, 125

cellular automata and, 128–129, 140

Codd versus, 126f

EDVAC and, 304

ENIAC and, 109–111

Hilbert and, 112

Holland and, 142

“infinite array of square cells” and, 120f

influence of, 64

motivations of, 309

philosophies of mind and, 164

self-reproducing automata and, 106, 116–120, 121f, 122f, 369n67

Toffoli on, 135

Voxel prototype, 377n85, 383n165

Vygotsky, Lev, 44, 45, 163

Wainwright, Robert, 132

Waldrop, M. Mitchell, 360n20

Walter, William Grey, 362n65

Warburg, Pincus (WP), 225, 227, 232, 233, 241, 245, 247

Wardrip-Fruin, Noah, 396m3

Warner Avenue Elementary, 332n43

Watts riots, 18, 37, 87

Watts Towers, 40f

“Waves of One,” 156

Weaver, Warren, 339n56, 341n95, 395n17

Wedit, 191, 193, 272–273

WELL, The, 374n37

West, Kurt, 205

West, Tom, 251

Westminster Elementary, 17f, 21, 22f, 29, 322, 332n43, 332n45

What to Do after You Hit Return, or P.C.C.’s First Book of Computer Games, 161

Where in the World Is Carmen Sandiego? 16, 329n6

Whirlwind, 63, 71, 75–77, 84, 115, 156–157, 181, 360n20

Whitehead, Alfred, 112

Whitson, Jennifer, 310

Whole Earth Catalog, 25, 161, 328n13

Whole Earth network, 6, 246

Whole Earth Review, 150, 151, 211, 217, 374n37

Wiener, Norbert, 71

Wilson, E. O., 216, 217

Wilson, Johnny, 373n12

Windows, 158, 171

Winograd, Terry, 189, 240f, 382n145

Wired, 150, 211, 217, 246, 378–379n93

Wittgenstein, Ludwig, 112

Wolfram, Stephen, 106, 108, 135–140, 139f, 143–145, 147, 148, 149, 219

Wolosenko, Roxana (Roxy), 16, 243

Wolpert, Arlen, 100

Word count analysis, 344n46

Workshop on Adaptive Computation, 218

Workshop on Urban Problems, 87

World dynamics, 98–99

World Dynamics (Forrester), 98–99

World editors, 191, 193

World War II, 109, 303–304

World Wide Web, 64

Wozniak, Steve, 153–154, 155, 159, 161, 174–175, 179, 182, 188

Wright, Cassidy, 243

Wright, Dr., 223–224, 224f, 245

Wright, Will. See also Maxis; individual games

artists and, 228

background of, 186–189

on balance, 294, 297

box design by, 238f

Braun on, 212

City Planner and, 193–199, 198f

context of simulation and, 309

CTG and, 242

design orientation of, 207–208, 288–289

development of SimCity and, 11, 193–208

Dollhouse and, 221–222, 233–235, 239, 240f

education market and, 16

Forrester and, 70, 194

on game designers, 392n13

on growth and decay, 267, 275

inspiration for, 182

interning with, 7, 8

Karakotsios and, 218, 219

Kressel and, 225

on land value, 272

Life and, 307

Maxis Business Solutions and, 232

Maxis’s sale and, 246

Maxis’s unraveling and, 237, 243, 247

messages and, 284

Nelson and, 31

Nintendo and, 222–223

open-source push and, 287

overview of work of, 5–6

photo of, 203f, 206f, 223f, 231f

Pinball Construction Set and, 154, 181

on player imagination, 286

on players, 301

Raid on Bungeling Bay and, 185–186, 190–193, 190f

Santa Fe Institute (SFI) and, 151–152, 281

sequel and, 229, 248

SFI and, 218–219

SimAnt and, 216, 220–221

SimCity and, 2, 5, 8, 202–208, 210, 244–245, 253–254

SimCity 3000 and, 1

SimEarth and, 215

The Sims and, 213, 248–249

system dynamics and, 70–71, 100, 102, 194–195, 196–197, 281, 307–308

wildfire and, 221

Wurman, Richard Saul, 30

Xerox, 168, 175, 179

Yamauchi, Hiroshi, 222, 223

Zone development, 273–276, 274f

Zork, 200

Zudowski, Leda, 203f