Sunday, June 12, 2005
Computational Sociology and Agent-Based Modeling
From Factors to Actors:
Computational Sociology and Agent-Based Modeling
"Consider a flock of geese flying in tight formation. Collectively, they form the image of a giant delta-shaped bird that moves as purposively as if it were a single organism. Yet the flock has no “group mind” nor is there a “leader bird” choreographing the formation (Resnick 1994).
Rather, each bird reacts to the movement of its immediate neighbors who in turn react to it. The result is the graceful dance-like movement of the flock whose hypnotic rhythm is clearly patterned yet also highly non-linear.
If we tried to model the global elegance of the flock, the task would be immensely difficult because of the extreme complexity in its movement. Yet the task turns out to be remarkably easy if instead we model the dynamics of local interaction. This was demonstrated by Craig Reynolds (1987) when he modeled the movement of a population of artificial “boids” based on three simple rules:
· Separation: Don't get too close to any object, including other boids.
· Alignment: Try to match the speed and direction of nearby boids.
· Cohesion: Head for the perceived center of mass of the boids in your immediate
neighborhood.
Reynold’s computational method is called “agent-based modeling.”"
Computational Sociology and Agent-Based Modeling
"Consider a flock of geese flying in tight formation. Collectively, they form the image of a giant delta-shaped bird that moves as purposively as if it were a single organism. Yet the flock has no “group mind” nor is there a “leader bird” choreographing the formation (Resnick 1994).
Rather, each bird reacts to the movement of its immediate neighbors who in turn react to it. The result is the graceful dance-like movement of the flock whose hypnotic rhythm is clearly patterned yet also highly non-linear.
If we tried to model the global elegance of the flock, the task would be immensely difficult because of the extreme complexity in its movement. Yet the task turns out to be remarkably easy if instead we model the dynamics of local interaction. This was demonstrated by Craig Reynolds (1987) when he modeled the movement of a population of artificial “boids” based on three simple rules:
· Separation: Don't get too close to any object, including other boids.
· Alignment: Try to match the speed and direction of nearby boids.
· Cohesion: Head for the perceived center of mass of the boids in your immediate
neighborhood.
Reynold’s computational method is called “agent-based modeling.”"
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