Calvino on Models

“The construction of a model […] was for him a miracle of equilibrium between principles (left in shadow) and experience (elusive), but the result should be more substantial than either.  In a well-made model, in fact, every detail must be conditioned by the others, so that everything holds together in absolute coherence, as in a mechanism where if one gear jams, everything jams.  A model […] is that in which nothing has to be changed, that which works perfectly; whereas reality, as we see clearly, does not work and constantly falls to pieces; so we must force it, more or less roughly, to assume the form of the model.”

“A delicate job of adjustment was then required, making gradual corrections in the model so it would approach a possible reality, and in reality to make it approach the model.  In fact, the degree of pliability is not unlimited […]; even the most rigid models can show some unexpected elasticity.  In other words, if the model does not succeed in transforming reality, reality must succeed in transforming the model.”

“Mr. Palomar’s rule had gradually been changing: now he needed a great variety of models, whose elements could be transformed in order to arrive at one that would best fit reality, a reality that, for its own part, was always made up of many different realities, in time and in space.”

– Italo Calvino, Mr. Palomar, (translated from Italian by William Weaver), Harcourt Brace Jovanovich) pgs. 109 -110

Agents, Crowds, Architectures

picture-1“For seeing life is but a motion of limbs, the beginning whereof is in some principal part within, why may we not say that all automata (engines that move themselves by springs and wheels as doth a watch) have an artificial life?”
-Thomas Hobbes, Leviathan, 1660.

For sheer uncanny sci-fi weirdness, nothing tops reading the abstracts for the funded projects on the Defense Science Board’s website.  The DSB is the public face of funding for academic research relevant to DARPA – the Defense Advanced Research Projects Agency.  You may know DARPA from such projects as directed heat-ray weaponry, sub-vocalization detection, passive radar, the Internet, etc.  DARPA is not as cloak-and-dagger as it may seem from its ominous name; in the defense world, it’s the Pentagon’s way of funding those wacky little “off the wall” projects that might not otherwise receive support.  You know, those cute little defenseless defense projects?  DARPA likes to call those “strategic technology vectors.”   This year, one of the main strategic vectors being pushed forward by the Pentagon is in a field called “agent modeling” or “crowd dynamics.”  DARPA has various terms for this line of research, from crowd theory to “human terrain mapping” to “social simulation.”  You can think of this broadly as the science of individual and collective behavior situated in an environment.

Shortly after the US-led invasion of Iraq, members of the US military entrusted with coordinating crowd control and counter-insurgency measures were met with the problem of navigating and intervening in unknown social and political territory.  Models of collective human behavior were thought critical to effective planning and “logistics.”   This is not the first time that the Pentagon has decided to focus on quantitative social sciences of crowds and collective behavior.  During the Vietnam War, DARPA launched an ambitious endeavor called “Project Camelot.”  DARPA’s director, R.L. Sproul, testified before congress that “it is [our] primary thesis that remote area warfare is controlled in a major way by the environment in which the warfare occurs; by the sociological and anthropological characteristics of the people involved in the war.” (McFate, 2005).  Project Camelot was tested in Chile, but was met with such local resistance and negative press domestically that Secretary of Defense Robert McNamara cancelled the program.  Much has happened since Sproul’s time and, as of 2003, this line of research is back, with new tools and new funding.

What is in question here is simulation, more specifically the simulation of people and crowds.  Simulation of physical systems is now making in-roads into architectural practice.  The facility to simulate natural processes is greatly aided by the low cost and ubiquity of computation.  The ability to simulate lighting conditions, thermal properties, acoustic effects, and structural stability, are all becoming part of sustainable design practice.  Thermal simulation (employing software such as Ecotect) can give us an accurate picture of how a space will behave under different heating/cooling and seasonal conditions, with varying numbers of bodies occupying a space.  Physically-based rendering (using Radiance) can produce images which actually contain data about light.  But behavior of light in a space is fundamentally different from the behavior of people…right?

Simulating human behavior is nothing terribly novel.  Recently, the notion of computational simulated agents has made its way into popular culture.  The Sims by Electronic Arts – the best selling computer game of all time – casts the player as the omniscient controller of a family of simulated agents.  Massive – a software tool developed for the film trilogy The Lord of the Rings – allows the user to simulate a “massive” crowd of autonomous agents who interact and exhibit complex emergent behavior.  However, these games, tools and visualizations are making their way into design practice.  What does this mean for architecture?  Imagine your SketchUp model populated with hundreds of animated characters.  Instead of the little outlined silhouettes frozen in mid-stride, exploratory agents walk through, inhabit, use, abuse and dwell in your design.

How does this work?  Just how predictable are you?  So many theories, so little time.  Biologists have long been fascinated with collective behavior in the animal kingdom.  Flocks, swarms, schools, and herds all display the hallmarks of collective emergent organization springing from the application of simple rules to large systems.  Consider two models of your behavior: “top-down” and “bottom-up.”  The top-down view sees your behavior as a direct result of the layout of an environment.  The bottom-up approach casts an person’s behavior as the result of a variable-juggling cognitively calculated reaction to input about an environment.  Both represent you as a convenient computational abstraction.  You are not you. You are an agent, within a system.  How you behave is entirely up to the system.

This brings up a number of theoretical and technical questions: What behavior should the agent simulate?  Does the agent exhibit this behavior?  Do humans behave in the same way?  How do groups of humans behave?  Do models exhibit these group behaviors?  Can models capture something beyond simply behavior? Can they capture emotion?  Mood?  Cognitive process?    What does this have to do with architecture?  Just how predictable are people?  Should we model agents and crowds at all?  Putting aside the final normative questions for the moment, let’s first consider the top-down approach.

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