Originally published as part of The V-A-C Foundation: Time, Forward!, 58th Venice Biennale.
In the 1950s and 1960s, amidst urban decline caused by white flight across many American cities, Detroit's city government became enraptured by the promise of "urban renewal"1. In pursuing this planned program, many maps were created — both to take stock of what existed at present (what neighborhoods were facing "blight") and to sketch out what could exist in the future (what areas could be invested in, what land could be appropriated for new development, what could be built where, and so on).
This planning process unfolded as it has in countless other places, well within the prescribed, entrenched vision of developers and capital. Crude, cruel, and wasteful, it at most aspired to imitate the redrawing of zones, boundaries, borders, and neighborhoods and consequent reshuffling of bodies that is enacted with a brutal sameness across other cities.
In Detroit, this urban remaking was a project which, again with the same rehearsed strategies as elsewhere, such as in New York City under Robert Moses, ignored or was outright hostile to the city's Black residents. The maps of the present framed Black neighborhoods as up-for-grabs, if only the present and long-term occupants could be cleared out. The maps of the future promised a city whose prosperity was predicated on the erasure and exclusion of Black residents. The authorities tasked with producing these maps could not picture, due to ingrained racism, lack of imagination or just the utmost desire to preserve their own interests at the expense of others, what present conditions were actually like or what a better city could possibly look like. The destruction wrought by these plans formed the structural foundations for the riots that occurred there in 1967.
Following the riots, the Detroit Geographical Expedition and Institute (DGEI), founded by geographer William Bunge and Detroit resident and student Gwendolyn Warren, recognized how the maps drawn in this planning process functioned as the catalyst for disaster. They realized that map-making is an act that centers how those in power — those tasked with making the maps from the top down — understand the world. Exercising this knowledge, the DGEI produced what they called "oughtness maps": "maps of how things are and maps of how things ought to be"2, with the goal of depicting exactly what the master planners' maps leave out, producing a "radical cartography of murder sites, pedestrian paths, commuter traffic, and race relations"3, as well as finding ways to assert their own visions for a better Detroit.
Map-making is conventionally understood as a neutral act of description, one which represents the contours of space and the arrangement of things within it in a way that reflects the physical world. Rather than seen as an editorial act of perspective, interpretation, emphasization, and assertion, whatever a map depicts is taken for granted as real and inevitable. This is what makes mapsmost insidious: the same surface that renders empirically measurable geographical features like forests and mountains so too are intangible abstract constructs like borders, as if they are equally natural formations. This false-equivalency leads to a deep misunderstanding that grants maps their effectiveness as a political and rhetorical tools. The maps put forward by the urban planners of Detroit were understood to express all that is and all that could be: the inevitable expansion of development rights and displacement, as if it were a force of nature. The DGEI knew that this inevitability was manufactured, and with their oughtness maps, they saw that if maps prefigured the material cityscape — and it was ultimately arbitrary which one it prefigured — then it should prefigure one that responded to their hopes and needs. Bunge was on the nose with his perspective: in the DGEI's first publication, Field Notes I, he notes: "Afterall, it is not the function of geographers to merely map the earth, but to change it."
Simulation and its abuses
Map-making and simulation are closely related: whereas maps are primarily concerned with the organization of space, simulations are instead primarily concerned with the organization of time. Simulations, of course, may also be concerned with space, but in general, they are designed to emulate dynamical processes that play out over milliseconds, minutes, months, years, eons.
As a practice, simulation is broadly concerned with reconstructing a system or phenomenon in a simplified form, so as to reproduce its approximate behavior over time. The most familiar simulations are computer simulations, which can include for example fluid dynamics simulations to analyze the effect of turbulence on an aircraft wing, or a model of how crowds form stampedes when trying to escape a stadium in panic or to model evacuation of urban centers during natural disasters or chemical attacks. Video games like the SimCity franchise, the FIFA series, and Roller Coaster Tycoon let players simulate daily urban life, sports, and business ventures. Beyond computer simulations, nurses are also trained through simulations, where actors play out likely scripted scenarios in a clinical setting. And there’s the Monetary National Income Analogue Computer (MONIAC), which models the UK economy as a system of hydraulic stocks and flows. Of course, the convenience, affordability, and raw power of computing means that we very rarely see mechanical simulations like MONIAC. Simulation nowadays is popularly understood to mean computer simulations.
Computer simulations play out phenomena far more complex (and in much greater detail) than we humans can handle in our working memory. To program a simulation, we specify the rules we believe govern a subject system, then we run the simulation to see the consequences of those rules delivered in the form of data or visualization. In "A Third Way of Doing Science", the political scientist Robert Axelrod frames simulation as a third scientific methodology: a generative method as opposed to the familiar inductive and deductive approaches4. The general idea is to run simulations thousands or hundreds of thousands of times to collect vast amounts of data, and see if the results statistically match real-world observations of the system in question.
Running a simulation is a relatively straightforward process. The process of developing a simulation, however, requires that we articulate our particular understanding and theories of how the subject system operates. Because this articulation needs to legible to a computer, this whole process is less forgiving of ambiguity than other ways we can describe how a system works — in an essay, a conversation, a tweet, a map, and so on. The stricter requirement for explicit, detailed theories of what governs a system is what makes simulation so different.
In general, we structure computer simulations by defining a relatively small set of rules or equations that dictate the subject system's dynamics. Though the number of rules may be relatively small, their interaction produces an emergent complexity that can sufficiently model the target system. We follow the same principle of simplification when we render bodies of water in a uniform shade of blue, or a mountain range as a row of triangles on a map — for most uses it's unnecessary for nature to be rendered in photo-realistic detail to be legible. With simulations, these simplifications are necessary for computational tractability — too much detail and your model can take years to finish running. Simulations also need to be simplified versions of the system so that the model has meaningful explanatory power. If we were somehow capable of exactly reproducing the subject system computationally, down to the smallest detail, then we'd have gotten nowhere in making its inordinate complexity more manageable (which is the whole purpose of making a model and theorizing how systems work in the first place.) Much like in other areas of science, the quality of a simulation is based on how accurately it is able to reproduce the target system's dynamics (its predictive power) against the simplicity of its rules.
As a technique, simulation has wide applications: nearly every field can find some use for it. Simulation encompasses applications in the natural sciences, where comparatively simple sets of rules govern but still may be immensely dense, whether at the level of the climate or even at the scale of folding proteins, and in the social sciences, where nuance and context and specificity conspire to add complexity that can never fully be captured by a model. This brings up some of the hard limits to simulation: there are limits to sensor precision, there are limits to projection accuracy over time (the "horizon of predictability", which is why weather forecasts are increasingly unreliable the further out they go), and there are epistemological limits which prevent us from knowing enough about the internal mental models and experiences of individuals to model them in a detailed way.
But still, the promise of simulation is tempting. For businesses, institutions, and policymakers, there is tremendous appeal. In its idealized form, simulation essentially promise a way to "predict" the future, under the guise of computational objectivity and rigor. Like so many popular clairvoyance fantasies, the chance to glimpse the future is valuable often only because it provides an edge in exploiting knowledge of that future.
In the 1973 miniseries World on a Wire (Welt am Draht), the fictional Institute for Cybernetics and Future Science (IKZ) develops a rich simulated world, something akin to The Matrix. Its creators see it as a technological achievement to be explored and further understood, but the IKZ’s benefactor, United Steel, pressures IKZ researchers to squander this new technology on predicting steel demand and prices to beat out competitors. In our current world, the US military seeks a tight grip on the ebbs and flows of geopolitics and global conflict: Lockheed Martin's ICEWS (Integrated Crisis Early Warning System) assembles data to this effect (Lockheed Martin probably takes less issue with this than the IKZ researchers did). Being able to simulate how state and non-state actors respond and react would further exaggerate the already egregious asymmetry between the US military and the rest of the world’s.
Of course in domains like high-frequency trading, the timescales simulation is used for is a matter of nanoseconds, but the fundamental conception of simulation as a tool for making decisions about the future is the same. The impulse in both finance's nanoscale and the military's more glacial pace is to spatialize the future so that it can be carved up and doled out like land on a map, territories to be contested, claimed, and extracted from. This framing of simulation serves only to propagate today's dominant values and principles forward into the future.
Simulation as rhetoric
The truth is, unquestioningly projecting forward values or understandings of the present world is standard in designing simulations today. The process of simplifying a system by whittling it down to a relatively small set of rules necessarily means that details are smoothed out and nuance is ignored. For many parts of a simulation this is a conscious process of design and deliberation. But for many other parts — often the most foundational ones — certain details, such as those about human behavior or motivation or values, are kept in without any examination. These details are taken for granted because they are often ideological — not something to be seen but something that is seen through, like the air we breathe. The company that seeks to forecast its consumer demand and market changes takes as a given that they should and will continue to exist in the future. The military that seeks to foretell geopolitical swings takes as a given that the crises of today might resurface tomorrow, and that they are the best means of addressing them.
Simulations present a working model of the world, but the explanations they provide are bounded by the ideological commitments of their creators. Because the popular ideology of computation is one of perceived rigor, correctness, and mathematical infallibility, these foundational assumptions are often taken to be natural law.
Video games are home to some of the most egregious examples of these naturalizing premises. “Civilization” is a series of strategy simulation games where players embody a civilization from past eras of human history. The game, perhaps unsurprisingly, privileges the nation-state as the only legitimate form of human society. As Chris Franklin (Errant Signal) points out, nomadic or “stateless” peoples are unambiguously labeled as "barbarians"5. In some sequels they are explicitly labeled as primitive savages, sharing the same banner as wild animals. These “barbarian” people are presented as backwards nuisances to be dealt with (i.e. exterminated) or ignorant heathens to be assimilated into the “correct” form of human organization. In parallel, the simulated civilizations race to conquer all the others, to achieve total cultural hegemony, to establish a global theocracy, or even to leave the planet behind. You can't establish a completely new way of living: in "Civilization", the nation-state remains eternal.
Similarly, SimCity, a series of city management games, embeds and informs many assumptions about how a city functions. In online forums and discussion boards, there are many lively discussions about how to get rid of homeless people in players' cities; according to players the most effective solution is to build buses to take them away. There is no way to engage with homelessness as a social problem. For players it is a nuisance that “just happens.” Like rainwater that needs to be directed into a drainage system, people without a shelter must be channeled away to somewhere else. Similarly, in "Les Simérables", writer Ava Kofman points out that criminal activity in SimCity can only be dealt with by plopping down more police stations6. There is no room to meaningfully examine the root causes of the criminal activity in a way that might lead to to explore other ways of addressing crime. Here it's simply how cities work.
But since simulations are code, modifying that code is a direct way to challenge these assumptions and assert new ones. In the case of Civilization and SimCity, there are already communities of “modders” who create their own add-ons and modifications to the base game (though none — yet — seem to the address the problems described above). These games, and computer simulations more broadly, then become a clear site of ideological contestation, where one can directly challenge the naturalizing framing of behaviors or phenomena. Players can code their own rules or tweaks to see how things play out from their new starting points. Beyond simulations’ predictive and explanatory capacities, the most exciting characteristic of simulation is exactly in this kind of counterfactual thinking — we can all ask our own "what if"s. But even then, our imagination seldom escapes the enormous gravity of our most foundational beliefs.
The expansion of possibility
Fiction often fulfills a similar counterfactual function, by carving out a space for imaginings that can be quite radical. Ursula K. Le Guin and what Peter Frase calls "social science fiction" more broadly are known for this: taking our world, rejiggering some key elements of it, and then writing out what could change as a result7. And unlike most science fiction, these writings aren't concerned with the future per se. They are concerned with the here and now, not asking "what will things be like?", but rather, "how could things be?".
It’s unfortunate that because these works are fiction, their radical imagining may be dismissed on the grounds that it is, indeed, fiction. The audience is ready to handwave its predictions as fantastical and its characterizations as unrealistic. Simulation differs because it can produce radical new possibilities roughly within the parameters people are willing to accept, by leveraging computation’s aura of accuracy and impartiality. And because it's often framed as a forward-looking tool, it's possible to make a simulation that looks like it's about how things could change in future, but is actually about how things could be right now. Since simulations themselves can be modified to produce different consequences, simulations also challenge the fantasy that the world works in just one immutable way. Simulations fundamentally challenge that things can be only one way, upending the post-hoc rationalization that things “happen for a reason” and its implicit conclusion that all things are justified by their mere existence.
The justification of capitalism is generally based on the idea that we have reached the logical end of a roughly linear historical process of unambiguous progress. Present inequities in wealth, life, and production are therefore “necessary” to achieve better standards of living in the aggregate. History, and all the dynamic processes that compose it, is basically understood to exhaustively enumerate through all viable political and economic configurations, and stops when it delivers us with the best one. Since we are always at the latest stage of this linear process, we always live in the best of all possible worlds.
Of course, there are vested interests in maintaining this sense of historical inevitability, and silencing or subduing any evidence to the contrary. Steven Pinker, a cognitive psychologist, built his popular science career around arguing that the world is actually getting better, contra what pessimists and news media would have you believe. But where Pinker is so unimaginative that he can only ask "are things better than they were?" — and of course, some things are better for some people — simulation, in its tendency for counterfactual thinking, instead asks, "how good things could have been?".
Simulation, as a mode of counterfactual thinking, explores and prioritizes possibility, helping us appreciate the branching paths of history so that we may recognize we continually exist at such a juncture in the present. In encouraging us to think of ourselves as people existing in systems made up of interlocking simple rules, and more importantly, how different things can be with small shifts to these rules, we can appreciate how close we are to so many different worlds. Simulations help us appreciate that these simple rules are often not set in stone — even though they feel that way — and helps to dispel the conservative inertia that gives these myths their longevity and apparent inescapability.
Like maps, simulations have the capacity to shift the way our priorities are framed and to call into question the most sacred axioms society is organized around. What do we take for granted? The Detroit Geographical Expedition and Institute recognized that powerful interests took for granted that a city's rehabilitation necessitated the eviction of its Black inhabitants, and they used maps to literally reshape the material landscape to make it so. Their maps presented an inevitability to how space was structured, and limited view on how space could be used. The DGEI's maps needed to first upend that common sense. So too can simulation and its counterfactual tendency be used to upend the common ideology of our time, to help us appreciate, to paraphrase the old refrain, "other worlds are possible." This certainly isn't the best one.
The Detroit Geographic Expedition and Institute: A Case Study in Civic Mapping, Catherine D'Ignazio ↩
"A Third Way of Doing Science" (Axelrod, 1997) ↩