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Mathematical models for systems of interacting agents using simple localrules have been proposed and shown to exhibit emergent swarming behavior. Mostof these models are constructed by intuition or manual observations of realphenomena, and later tuned or verified to simulate desired dynamics. Incontrast to this approach, we propose using a model that attempts to follow anaveraged rule of the essential distance-dependent collective behavior of realpigeon flocks, which was abstracted from experimental data. By using a simplemodel to follow the behavioral tendencies of real data, we show that our modelcan exhibit emergent self-organizing dynamics such as flocking, patternformation, and counter-rotating vortices. The range of behaviors observed inour simulations are richer than the standard models of collective dynamics, andshould thereby give potential for new models of complex behavior.

Generating self-organizing collective behavior using separation dynamics from experimental data