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Exploring the Vast Parameter Space of Multi-Agent Based Simulation

  • Takao Terano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4442)

Abstract

This paper addresses the problem regarding the parameter exploration of Multi-Agent Based Simulation for social systems. We focus on the principles of Inverse Simulation and Genetics-Based Validation. In conventional artificial society models, the simulation is executed straightforwardly: Initially, many micro-level parameters and initial conditions are set, then, the simulation steps are executed, and finally the macro-level results are observed. Unlike this, Inverse Simulation executes these 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. Another unique point of our approach is that, using Genetic Algorithms with the functionalities of multi-modal and multi-objective function optimization, we are able to validate the sensitivity of the solutions. 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. This is the principle of Genetics-Based Validation.

Keywords

Multi-Agent Based Modeling Social Systems Verification and Validation Parameter Exploration Genetic Algorithms 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Takao Terano
    • 1
  1. 1.Department of Computational Intelligence and Systems Sciences, Tokyo Institute of Technology, 4259 Nagatsuda-Cho, Midori-ku, Yokohama 226-8502Japan

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