This paper proposes a new method: inverse simulation for analyzing emergent behaviors of agents in artificial societies, which aims at modeling social interactions in electronic mediated communication. Unlike conventional computational society models, inverse simulation executes simulation steps in the reverse order: set a macro-level objective function, evolve the worlds to fit to the objectives, then observe the micro-level agent characteristics. Genetic algorithms with tabu search attain this. The proposed method is able to optimize multi-modal functions. This means that, from the same initial conditions and the same objective function, we can evolve different results, which we often observe in real world phenomena.
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