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Evaluation Criteria for Learning Mechanisms applied to Agents in a Cross-Cultural Simulation

  • Yutaka I. Leon Suematsu
  • Keiki Takadama
  • Katsunori Shimohara
  • Osarnu Katai
  • Kiyoshi Arai
Conference paper
Part of the Agent-Based Social Systems book series (ABSS, volume 1)

Summary

In problems with non-specific equilibrium, common in social sciences, the processes involved in learning mechanisms can produce quite different outcomes. However, it is quite difficult to define which of the learning mechanisms is the best. When considering the case of a cross-cultural environment, it is necessary to evaluate how adaptation to different cultures occurs while keeping, at some level, the cultural diversity among the groups. This paper focuses on identifying an evaluation criterion using a comparison of various learning mechanisms that can manage the trade-off between adaptation to a new culture and the preservation of cultural diversity. Results show that: (a) For small and gradual accuracy from a less accurate learning mechanism, there is a tiny reduction in the diversity while the convergence time drops rapidly. For an accuracy level close to the most accurate learning mechanism, a reduction of the convergence time can be minor, while the diversity drops rapidly; (b) The evaluation of learning mechanism that performs better for fast converging while simultaneously keeping a good diversity before the convergence was performed graphically.

Key words

Agent-based model learning mechanism cross-cultural environments gaming simulation BARNGA 

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References

  1. 1.
    Axelrod, R. M. (1997) The complexity of cooperation: Agent-based models of competition and collaboration. Princeton University PressGoogle Scholar
  2. 2.
    Axelrod, R. M. and Cohen M. D. (2000) Harnessing complexity: Organizational implications of a scientific frontier. The Free PressGoogle Scholar
  3. 3.
    Greenblat, C. S. (1987) Designing games and simulations: An illustrated handbook. New-bury Park: Saga PublicationsGoogle Scholar
  4. 4.
    Leon, Y., Takadama, K., Nawa, N., Shimohara, K. and Katai, O. (2003) Analyzing the agent-based model simulation and its implications. Advances in Complex Systems, Vol.6, No. 3: 331–348CrossRefGoogle Scholar
  5. 5.
    Leon, Y., Takadama, K., Shimohara, K., Katai, O. and Arai, K. (2003) Analyzing BARNGA Gaming Simulation Using an Agent-Based Model. In: Proceeding of The 34th Annual Conference of the International Simulation and Gaming Association (ISAGA), pp. 817–876Google Scholar
  6. 6.
    Moss, S. and Davidsson, P. (2001) Multi-Agent-Based Simulation. Lecture Notes in Artificial Intelligence. Springer-Verlag, Vol. 1979Google Scholar
  7. 7.
    Sutton, R. S., Barto A.G. (1998) Reinforcement Learning: An Introduction, MIT PressGoogle Scholar
  8. 8.
    Thiagarajan, S. and Steinwachs B. (1990) Barnga: A Simulation Game on Cultural Clashes, Intercultural PressGoogle Scholar

Copyright information

© Springer-Verlag Tokyo 2005

Authors and Affiliations

  • Yutaka I. Leon Suematsu
    • 1
    • 2
  • Keiki Takadama
    • 1
    • 3
  • Katsunori Shimohara
    • 1
    • 2
  • Osarnu Katai
    • 2
  • Kiyoshi Arai
    • 4
  1. 1.ATR International - Network Informatics Labs.KyotoJapan
  2. 2.Graduate School of InformaticsKyoto UniversityKyotoJapan
  3. 3.Interdisciplinary Graduate School of Science and Engineering, Department of Computational Intelligence and Systems ScienceTokyo Institute of TechnologyYokohama, KanagawaJapan
  4. 4.School of Project ManagementChiba Institute of TechnologyJapan

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