Simulating allows for a level of predictability and repeatability, a rigor that is rarely possible in built architectural or design work. For example, an urban planner considering alternate traffic signals could hardly expect to implement and empirically test dozens of different intersection designs on actual city streets. Instead, through a careful modeling process, the planner could compare various designs in a simulated environment to observe their effects. It is important to underline the fact that simulations are not substitutes for real, lived experience. Rather, through considered computational modeling, simulations based on mechanism-independent components of emergent properties might provide evidence to make arguments about possible interventions in systems. Simulations need not be (and can never be) comprehensive. Like the map in Jorge Luis Borges’ short story “On Exactitude in Science,” which becomes more and more detailed until it grows to be the size of the territory it charts, a simulation which corresponds 1-to-1 with reality is a fiction. Certain parameters must be selected as the key elements of the simulation, and the rest is noise. As DeLanda writes, “The process [of simulation modeling] may... change in an infinite number of irrelevant ways, the art of mathematical modeling being based in part on the ability to judge what changes do, and what changes do not, make a difference.” A simulation being plausible or completely impossible when compared against reality might hinge on a single parameter held to an inflexible degree of precision. One dangerous pitfall is that both realistic and unrealistic simulations might operate based off of the same internal logic (they might even differ by a single number), a logic that is easily mistaken for completeness and accuracy.

Technology and psychology researcher Sherry Turkle, echoing architect Louis Kahn’s question, “What does a brick want?” asks, “What does simulation want?” In her critical 2009 book Simulation and Its Discontents, she offers a simple answer: Immersion. A computer model based on and resembling reality offers its own simplified reality with an internally coherent logic. A facility with simulating might lead one to forget about outside dimensions that are left unmodeled, and the ways in which they could come into play. Turkle writes, “In simulation, architects feel an initial exhilaration because of the ease of multiple iterations. But at a certain point… possibilities can feel like inevitabilities."1 Simulations of scientific phenomena often model in order to predict the performance of, for example, a building’s energy efficiency, and immersion in such simulations to the neglect of outside factors would be a misstep. In a computational design context, however, the immersiveness of simulations can be helpful. Rather than predictive, decision-making tools, simulations can be interpreted as discursive fictions on how the world might be, as opposed to how it is. If a designer can maintain an interrogative, skeptical stance while, at the same time, suspending disbelief about the simplifications necessary in order to model certain phenomena, immersion in a simulation can lead to new ideas and understandings of reality.

For some of my interview subjects, immersion in simulation is desirable in order to see possible futures from potentially unrealistic (non-normative) emergent patterns. A simple reframing of simulations on the part of the observer can lead to novel interpretations. In the case of Ken’s party simulation described above, with partygoers constantly going to the bathroom, one interpretation is as a glitch in the simulation code. Another is that Ken has inadvertently created a world where, perhaps, personal hydration has become a cultural imperative and recurrent bathroom-going is one logical side effect of this norm.

Max, the artificial intelligence researcher, told me about a project he worked on to model labor economics. The simulation uses census and American Community Survey data to model a large U.S. American city around the turn of the 21st century. The individual citizens, agents in the simulation, are motivated by abstracted economic behavior, like looking for a job and buying food for themselves and dependents, and in turn the economy of the simulation is affected by the millions of actions taken at the individual level. However, unlike a project undertaken by a government or a think tank, Max describes this work as speculative and exploratory: “We wanted to push it in this direction where it was a simulation where these other ways of addressing problems or even defining what problems are is a lot more open-ended.” For example, a viewer of the simulation can adjust city-wide parameters such as healthcare cost (or universal healthcare) to see the potential impact on the agents and the system. But despite the software’s numeric encoding and outputs, it is not meant to provide solutions to problems, but to explore possible futures.

Unlike a centralized, predictive model that tells us what the future will look like, Max’s project is best viewed as a provocation, asking what the future might look like. Higher levels of technological automation coupled with universal healthcare might lead to an egalitarian, post-work utopia, while other simulation runs end in a late-capitalist societal collapse. On top of this, in Max’s project, a viewer can return to the level of the agent, experiencing futures through the eyes of an individual, and see how various scenarios affects the lives, jobs, and health of simulated citizens. In fact, he had originally planned to “partner with various science fiction authors… We would give them a simulation run, and let them develop a richer narrative around that.” Simulation space acts as a means of envisioning and extending the possibilities of real, physical and social spaces. Similar to his generative music project (described in the Generating chapter), Max sees the outcome of his work not necessarily as a finished product, but a jumping off point. After setting the environment, one can “see how the behavior of these simulated agents evolves and unfolds and from that. Maybe you can develop interesting narratives… out of the configurations that seem the most interesting.” A simulation itself can act as a generative device, sparking the imagination and leading to the creation of new realities.

1. Turkle, Sherry, William J. Clancey, Stefan Helmreich, Yanni A. Loukissas, and Natasha Myers. Simulation and its discontents. Cambridge, MA: MIT Press, 2009.

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