L. Antunes and K. Takadama (Eds.): MABS 2006, LNAI 4442, pp. 1–14, 2007. © Springer-Verlag Berlin Heidelberg 2007 Exploring the Vast Parameter Space of Multi-Agent Based Simulation Takao Terano Department of Computational Intelligence and Systems Sciences, Tokyo Institute of Technology 4259 Nagatsuda-Cho, Midori-ku, Yokohama 226-8502, Japan terano@dis.titech.ac.jp 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. 1 Introduction As Alan Kay stated, the best way to predict the future is to invent it. When we use Multi-agent based simulation (MABS) for social systems, we always invent a new world, or a new bird-view-like point, because we are able to design the simulation world as we would like to. Therefore, when we use MABS, we are predicting some future. After several decades of the Allan Kay’s statements, we have a new gear for predicting the future: MABS is a new modeling paradigm [1],[2]. MABS focuses from global phenomena to individuals in the model and tries to observe how individuals with individual characteristics or “agents” will behave as a group. The strength of MABS is that it stands between the case studies and mathematical models. It enables us to validate social theories by executing programs, along with description of the subject and strict theoretical development. In MABS, behaviors and statuses of individual agents are coded into prog- rams by researchers. They also implement information and analytical systems in the