Inverse Simulation for Analyzing Emergent Behaviors in Artificial Societies

This paper proposes a new method: Inverse Simulation for analyzing emergent behaviors of agents in artificial societies, which model social interactions in the electronic mediated communication. Inverse Simulation utilizes Genetic Algorithms with tabu search to optimize a global evaluation function. The method is implemented in a simulator TRURL, which evolves artificial worlds of multi-agents to socially interact with each other. The micro-level agent activities are determined by both predetermined and acquired parameters. The former pa-rameters have constant values during one simulation cycle, however, the latter parameters change during the interactions. Unlike conventional artificial society models, TRURL evolves the societies by changing the predetermined parameters to optimize macro-level socio-metric measures, which can be observed in such real societies as e-mail oriented organizations and electronic commerce markets. Thus, using TRURL, we automatically tune the parameters up and observe both micro- and macro-level phenomena grounded in the activities of real worlds.