The notion here is that decisions and actions form identities, and identities influence other identities. An agent's decision to maintain a current identity or activate an alternative one -- stay home or join a Taliban faction, for example -- unfolds after it bumps into agents in its immediate neighborhood, agents whose own characteristics have been activated as the simulation evolves.
"The trick in this type of programming is to construct a basic framework for each character, to program who they are fundamentally, and then set them in motion," says Michael Zyda, director of the Naval Postgraduate School's Modeling, Virtual Environments and Simulation Institute (Moves). "Interesting things will emerge. The characters interact and generate whole sets of future, often unexpected, scenarios."
GI Agent, a model designed by a member of Zyda's team at Moves, illustrates how unanticipated results can emerge. A blue army faces off against a red army. The variables are not only psychological but physical: Each soldier is programmed for proficiency with weapons, type of arms carried, physical strength, and personality traits, such as a tendency to be self-reliant or overly self-protective, and a willingness to take on the enemy.
In one experiment, GI Agent designer Cap. Joel Pawloski and his programmer colleagues wanted to find the most effective way to sprinkle nine snipers throughout a blue army company made up of nine 10-soldier squadrons. In the first scenario, the programmers grouped all nine snipers together into a separate, tenth squadron. The results were dismal: The blue army was successful only about half the time.
But when the programmers instead inserted one sniper into each of the company's nine squadrons, the blue army was victorious 96 percent of the time. Why? The sniper within each squadron served as the advance guard, disabling key enemy positions at the start of a maneuver and thereby protecting the soldiers around him. Communication also improved, because the snipers, equipped with superior technology, were able to see farther than their comrades and so
conveyed more useful intelligence to company commanders.
"As in real life," says Zyda, "some agents hold more cards than others, and when they are at their greatest strength, they can overwhelm or at least neutralize agents around them."
Agent-based modeling is a child of complexity theory, which holds that the organization of complex systems hinges on the interplay of seemingly haphazard individual events. Complicated patterns -- how ants behave collectively, how terrorists choose targets -- emerge from what appears to be randomness. Bottom-up analysis begins with the small events, the unseen interactions of agents that influence the whole
system, and seeks to connect the local to, in political terms, the regional, national and international. It's not all about bad guys; there are broad applications: Complexity theorists say, for example, that traffic flow on a freeway can only be
predicted with models that simulate the behavior of the thousands of drivers on the road: their hard-braking, tailgating, rubbernecking, road rage.
"The behavior of a group or system is not preorganized and predetermined; it emerges from the collective interactions of all of its individuals," says Eric Bonabeau, chairman and chief scientific officer at Icosystem Corp. in Cambridge, Massachusetts, which designs agent-based models for companies. "Solutions presented by agent-based models are emergent and unanticipated." Answers arise to questions that weren't even asked.
Studying the tinderbox of racism yielded the first agent-based model of note. Developed in the late 1960s by distinguished Harvard professor of economics Thomas Schelling, the model was a manual, noncomputerized affair, almost a board game of the Reversi stripe. Schelling was curious about how segregated neighborhoods were formed. He had an inkling that more than absolute racism -- "there is no way I will live anywhere near a person not of my color" -- was responsible for the stark color divides between neighborhoods. Schelling made a grid with coins -- some representing blacks, others whites -- distributed randomly. He posited a simple rule: Each coin is happy if at least one-third of its neighbors are its own kind, the idea being that a person would tolerate living in a neighborhood in which only one-third -- but not fewer -- of nearby residents were the same color.
By any American standard, probably any standard in the world, such a neighborhood would be considered integrated.