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.
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References
Axelrod, R.: The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press, Princeton (1997)
Axtell, R.: Why Agents? On the Varied Motivation for Agent Computing in the Social Sciences. Brookings Institution CSED Technical Report No. 17 (November 2000)
Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York (2002)
Goldberg, D.E.: The Design of Innovation, Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Boston (2002)
Yuji, K., Kurahashi, S., Terano, T.: We Need Multiple Solutions for Electric Equipments Configuration in a Power Plant - Applying Bayesian Optimization Algorithm with Tabu Search –. In: Proc. 2002 IEEE World Congress on Computational Intelligence, pp. 1402–1407 (2002)
Kurahashi, S., Terano, T.: A Genetic Algorithm with Tabu Search for Multimodal and Multiobjective Function Optimization. In: Proc. the Genetic and Evolutionary Computation Conference (GECCO-2000), pp. 291–298 (2000)
Richiardi, M., Leombruni, R., Saam, N., Sonnessa, M.: A Common Protocol for Agent-Based Social Simulation. Journal of Artificial Societies and Social Simulation 9(1) (2006), http://jasss.soc.surrey.ac.uk/
Terano, T., Kurahashi, S., Minami, U.: TRURL: Artificial World for Social Interaction Studies. In: Proc. 6th Int. Conf. on Artificial Life (ALIFE VI), pp. 326–335 (1998)
Kurahashi, S., Terano, T.: Analyzing Norm Emergence in Communal Sharing via Agent-Based Simulation. Systems and Computers in Japan, (Translated from Denshi Joho Tsushin Gakkai Ronbunshi, Vol. J84-D-I(8), August 2001, pp. 1160.1168) 36(6) (2005)
Takahashi, H., Terano, T.: Agent-Based Approach to Investors’ Behavior and Asset Price Fluctuation in Financial Markets. Journal of Artificial Societies and Social Simulation 6(3) (2003)
Takahashi, H., Terano, T.: Exploring Risks of Financial Markets through Agent-Based Modeling. In: Proc. SICE-ICASS Joint Conference 2006 (to appear)
Terano, T., Naitoh, K.: Agent-Based Modeling for Competing Firms: From Balanced-Scorecards to Multi-Objective Strategies. In: Proceedings of the 37th Annual Hawaii International Conference on System Sciences 2004 (HICCS 2004), pp.1–8 (January 5-8, 2004)
Terano, T., Deguchi, H., Takadama, K. (eds.): Meeting the Challenge of Social Problems via Agent-Based Simulation. Springer, Heidelberg (2003)
Terano, T., Kita, H., Kaneda, T., Arai, K., Deguchi, H.: Agent-Based Simulation – From Modeling Methodologies to Real World Applications. Springer, Heidelberg (2005)
Kaplan, R.S., Norton, D.P.: The Balanced Scorecard - Translating Strategy into Action. Harvard Business School Press, Massachusetts (1996)
Kaplan, R.S., Norton, D.P.: The Strategy Focused Organization: How Balanced Companies Thrive in the New Business Environment. Harvard Business School Press, Massachusetts (2001)
Treacy, M., Wiersema, F.: The Discipline of Market Leaders: Choose Your Customers, Narrow Your Focus, Dominate Your Market. Addison-Wesley, Readings, Massachusetts (1997)
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Terano, T. (2007). Exploring the Vast Parameter Space of Multi-Agent Based Simulation. In: Antunes, L., Takadama, K. (eds) Multi-Agent-Based Simulation VII. MABS 2006. Lecture Notes in Computer Science(), vol 4442. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76539-4_1
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DOI: https://doi.org/10.1007/978-3-540-76539-4_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76536-3
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