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Toward Cumulative Progress in Agent-Based Simulation

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New Frontiers in Artificial Intelligence (JSAI 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2253))

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Abstract

This paper stresses the importance of focusing on modeling processes in order to make cumulative progress in agent-based approaches. In this paper, we introduce our approach to analyzing modeling processes and investigate its possibilities toward cumulative progress. The capabilities of our approach can be summarized as follows: (1) our approach has great potential to promote cumulative progress in agent-based approaches; and (2) the elements found by our approach have high possibilities of affecting the real world, being utilized as tool-kits, and supporting the KISS principle.

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References

  1. Argyris, C. and Schon, D. A. (1978): Organizational Learning, Addison-Wesley.

    Google Scholar 

  2. Axelrod, R. M. (1984): The Evolution of Cooperation, BasicBooks.

    Google Scholar 

  3. Axelrod, R. M. (1997a): “Advancing the Art of Simulation in the Social Sciences”, in R. Conte, R. Hegselmann, and P. Terna (Eds.), Simulating Social Phenomena, Springer, pp. 21–40.

    Google Scholar 

  4. Axelrod, R. M. (1997b): The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration, Princeton University Press.

    Google Scholar 

  5. Axelrod, R. M. (2000): Private communication.

    Google Scholar 

  6. Carley, K. M., Kjaer-Hansen, J., Prietula, M., and Newell, A. (1992): “A prolegomenon to Artificial Agents and Organizational Behavior”, in M. Masuch and M. Warglien (Eds.), Distributed Intelligence: Applications in Human Organization, pp. 87–118, Elsevier Science Publications.

    Google Scholar 

  7. Carley, K. M. (1995): “Computational and Mathematical Organization Theory: Perspective and Directions”, Computational and Mathematical Organization Theory, Vol. 1, No. 1, pp. 39–56.

    Article  Google Scholar 

  8. Carley, K. M. and Svoboda, D. M. (1996): “Modeling Organizational Adaptation as a Simulated Annealing Process”, Sociological Methods and Research, Vol. 25, No. 1. pp. 138–168.

    Article  Google Scholar 

  9. Carley, K. M. and Gasser, L. (1999): “Computational and Organization Theory”, in Multiagent Systems — Modern Approach to Distributed Artificial Intelligence —, G. Weiss (Ed), The MIT Press, pp. 299–330.

    Google Scholar 

  10. Cohen, M. D., March, J. G., and Olsen J. P. (1972): “A Garbage Can Model of Organizational Choice”, Administrative Science Quarterly, Vol. 17, pp 1–25.

    Article  Google Scholar 

  11. Cohen, M. D. and Sproull, L. S. (1995): Organizational Learning, SAGE Publications.

    Google Scholar 

  12. Cohen, M. D. (2000): Private communication.

    Google Scholar 

  13. Epstein, J. M. and Axtell, R. (1996): Growing Artificial Societies: Social Science form the Bottom Up, Brooking Institution Press.

    Google Scholar 

  14. Gayload, R. and D’Andira, L. J. (1998): Simulating Society: AMathematic a Toolkit for Modeling Socioeconomic Behavior, Springer-Verlag.

    Google Scholar 

  15. Krackhardt, D. and Carley, K. M. (1997): “PCANS Model of Structure in Organizations”, The 1997 International Symposium on Command and Control Research and Technology.

    Google Scholar 

  16. Kurumatani, K. and Ohuchi, A. (2001): “World Trade League: Standard Problems for Multi-agent Economics (1) — Concept and Implementation of X-Economy System”, Meeting of SIG-ICS (Special Interest Group on Intelligence and Complex System) of IPSJ (Information Processing Society of Japan), 2001-ICS-123, pp. 55–60, (in Japanese).

    Google Scholar 

  17. Levitt, R. E., Cohen, G. P., Kunz, J. C., Nass, C. I., Chirstiansen, T., and Jin, Y. (1994): “The Virtual Design Team: Simulating How Organization Structure and Information Processing Tools Affect Team Performance”, in Carley, K. M., and Prietula, J. (Eds.): Computational Organization Theory, Lawlence-Erlbaum Assoc., pp. 1–18.

    Google Scholar 

  18. March, J. G. (1991): “Exploration and Exploitation in Organizational Learning”, Organizational Science, Vol. 2, No. 1, pp. 71–87.

    Article  MathSciNet  Google Scholar 

  19. Shiozawa, Y. (1999): “Virtual Market as a Common Test Bed — for the construction of a “robo-cup” in economics—” The 1999 JAFEE (Japan Association for Evolutionary Economics) Annual Meeting, pp. 253–256, (in Japanese).5

    Google Scholar 

  20. Takadama, K., Terano, T., Shimohara, K., Hori K., and Nakasuka, S. (1999): “Making Organizational Learning Operational: Implication from Learning Classifier System”, Computational and Mathematical Organization Theory (CMOT), Kluwer Academic Publishers, Vol. 5, No. 3, pp. 229–252.

    Article  MATH  Google Scholar 

  21. Takadama, K., Terano, T., and Shimohara, K. (2000): “Interpretation by Implementation for Understanding Multiagent Organization”, The CASOS (Computational Analysis of Social and Organizational System) Conference 2000, pp. 157–160.

    Google Scholar 

  22. Takadama, K. and Shimohara, K. (2001a): “What Kinds of Properties Determine Characteristics of Multiple Learning Agents? ∼ Implications from goal and evaluation in agents ∼,” The International Workshop on Autonomy Oriented Computation (AOC’01) at the 5th International Conference on Autonomous Agents (Agents’01), pp. 21–30.

    Google Scholar 

  23. Takadama, K. and Shimohara, K. (2001b): “Exploration and Exploitation Trade-off in Multiagent Learning,” The 4th International Conference on Computational Intelligence and Multimedia Applications (ICCIMA’01), to appear.

    Google Scholar 

  24. Tesfatsion, L. (2001): “Introduction to the Computational Economics, Special Issue on Agent-Based Computational Economics”, Computational Economics, to appear.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Information Sciences Division, ATR International, 2-2-2 Hikaridai, Seika-cho, 619-0288, Soraku-gun, Kyoto, Japan

    Keiki Takadama & Katsunori Shimohara

Authors
  1. Keiki Takadama
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  2. Katsunori Shimohara
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Editor information

Editors and Affiliations

  1. Graduate School of Business Science, The University of Tsukuba, Tokyo

    Takao Terano  & Yukio Ohsawa  & 

  2. School of Engineering, The University of Tokyo, Tokyo

    Toyoaki Nishida

  3. Dept.of Computer Science, National Defense Academy, Japan

    Akira Namatame

  4. School of Medicines, Shimane Medical University, Japan

    Syusaku Tsumoto

  5. The Institute of Scientific and Industrial Research, Osaka University, Osaka

    Takashi Washio

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© 2001 Springer-Verlag Berlin Heidelberg

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Takadama, K., Shimohara, K. (2001). Toward Cumulative Progress in Agent-Based Simulation. In: Terano, T., Ohsawa, Y., Nishida, T., Namatame, A., Tsumoto, S., Washio, T. (eds) New Frontiers in Artificial Intelligence. JSAI 2001. Lecture Notes in Computer Science(), vol 2253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45548-5_13

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  • DOI: https://doi.org/10.1007/3-540-45548-5_13

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43070-4

  • Online ISBN: 978-3-540-45548-6

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