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Interpretation by Implementation for Understanding a Multiagent Organization

Author

Listed:
  • Keiki Takadama

    (Tokyo Institute of Technology & ATR Human Information Science Labs)

  • Takao Terano

    (University of Tsukuba)

  • Katsunori Shimohara

    (ATR Human Information Science Labs)

Abstract

This paper stresses the importance of focusing on the modeling process of computational models for precisely understanding a complex organization and for solving given problems in the organization. Based on our claim, we proposes a method of interpretation by implementation (IbI), which explores factors that drastically change simulation results through an investigation on the modeling process of computational models. A careful investigation on the capabilities of the IbI approach, which comprises the three methods of (a) breakdown and representation, (b) assumption or premise modification, and (c) layer change investigation, derives the following conclusions: (1) the IbI approach has the potential of finding underlying factors that determine the characteristics of an organization; (2) the IbI approach can specify points of attention at necessary levels when analyzing an organization; and (3) the IbI approach has suchadvantages as wide applicability, the effective use of employed models, and KISS principle support.

Suggested Citation

  • Keiki Takadama & Takao Terano & Katsunori Shimohara, 2003. "Interpretation by Implementation for Understanding a Multiagent Organization," Computational and Mathematical Organization Theory, Springer, vol. 9(1), pages 19-35, May.
    • Handle: RePEc:spr:comaot:v:9:y:2003:i:1:d:10.1023_b:cmot.0000012307.23179.cb
    DOI: 10.1023/B:CMOT.0000012307.23179.cb
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    References listed on IDEAS

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    1. Tesfatsion, Leigh, 2001. "Introduction to the special issue on agent-based computational economics," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 281-293, March.
    2. John H. Miller, 1998. "Active Nonlinear Tests (ANTs) of Complex Simulation Models," Management Science, INFORMS, vol. 44(6), pages 820-830, June.
    3. W. Brian Arthur & Paul Tayler, "undated". "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Computing in Economics and Finance 1997 57, Society for Computational Economics.
    4. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, December.
    5. James G. March, 1991. "Exploration and Exploitation in Organizational Learning," Organization Science, INFORMS, vol. 2(1), pages 71-87, February.
    6. Keiki Takadama & Takao Terano & Katsunori Shimohara & Koichi Hori & Shinichi Nakasuka, 1999. "Making Organizational Learning Operational: Implications from Learning Classifier Systems," Computational and Mathematical Organization Theory, Springer, vol. 5(3), pages 229-252, October.
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