Working with computation, designers can more easily generate complex systems (as opposed to creating individual objects), explore spaces of possibility, and engage with their work at new levels. But while generating often implies the formulation of an entirely new system, the creative work of designers always exists within an exterior environment (technical, social, economic, material, etc.). Even the most speculative, implausible architecture or design projects presuppose a cultural context, perhaps also imagined, in which they take shape. A work, perhaps presented as purely formal or aesthetic, that ignores its context, lies through omission. Generative techniques give form to complex systems; simulating allows designers to model existing systems, to experiment through interventions in those systems, and to imagine from them new structures, goals, and paradigms.

Reacting against the computational paradigm described in the literature review, the term ‘simulation’ should be clarified. A computer simulation is a representation of some aspects of reality — a computational, mathematical model — usually with a temporal dimension and rules for how the model changes over time. Processes of simulation are generating systems, giving rise to holistic properties that, to varying degrees, align with those in the referent of the simulation. However, in the context of computational design, simulations are perhaps less valuable as purely analytical, predictive tools, than as spaces in which to explore and understand systems. Especially when visualized or embodied in order to tangibly observe the holistic properties of systems, simulations (like automated generative techniques) become powerful allies that allow the designer to operate at a level of abstraction. Coupled with an interface that allows for intervention into the system, a designer can experiment and speculate practically ad infinitum, testing their theories in a miniature world before implementing them in the real world.

All this does rest on the notion that a computer simulation can, to some degree, precisely and accurately represent aspects of reality. Few would argue that physical equations, say, for velocity over time (resulting from acceleration due to gravity), don’t resemble and predict phenomena observed in the real world. But when it comes to simulations of large, socio-technical systems, which are far more complex and unpredictable, the output of simulations should be met with skepticism. As artist and game designer Paolo Pedercini points out in his keynote talk at the 2017 International City Gaming Conference, simulations can be wielded as instruments of propaganda. Pedercini says:

“[Simulations] for city planning are often presented with a neutral technocratic language: ‘Let’s try to explain to common people the complexities of urban development’... But I swear, I can design you a [simulation] that subtly leads people to whatever “solution” you want… You can easily create formal mathematical relationships that reinforce your agenda. The more complex a simulation is, the more obfuscated is the data it is based on, the harder is to analyze it, fact check it, and criticize it.”1

Through a lens of technological neutrality, simulations are promoted as descriptions of reality used to provide recommendations for future action. But as Pedercini notes, they are always constructed by individuals with a (perhaps unconscious) agenda, and contain the biases of their creators. As digital media researcher Yanni Loukissas further describes in Co-Designers: Cultures of Computer Simulation in Architecture, simulations are also prone to differing or conflicting interpretations. They act as “spaces of exchange… open[ing] up zones in which design participants can coordinate… without sharing the same conceptions about those designs.”2 Additionally, the technical infrastructure and specifics of how a simulation is encoded shapes a space of possible interactions. Any simulation of a socio-technical system put forward as purely objective is an outright lie. However, whether a simulation is ‘right’ or ‘wrong’ is not the salient point; it’s what we can learn from it about ourselves and the world that is. The simulating processes described by my interview subjects and in the case study at the end of this chapter appear less like a Nostradamus predicting the future than an Octavia Butler or Ursula K. Le Guin imagining one of many possibilities. For designers, this happens through a modeling process that allows for unexpected patterns of behavior emerging in the simulation, and through a mindset of futuring — seeking out certain unintentional, desirable outcomes as projective futures to work toward.

1. Pedercini, Paolo. “SimCities and SimCrises,” 2017.
2. Loukissas, Yanni Alexander. Co-designers: cultures of computer simulation in architecture. Routledge, 2012.

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