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Making Organizational Learning Operational: Implications from Learning Classifier Systems

Author

Listed:
  • Keiki Takadama

    (ATR Human Information Processing Research Labs)

  • Takao Terano

    (Univ. of Tsukuba)

  • Katsunori Shimohara

    (ATR Human Information Processing Research Labs)

  • Koichi Hori

    (Univ. of Tokyo)

  • Shinichi Nakasuka

    (Univ. of Tokyo)

Abstract

The concepts of organizational learning in organization and management science cover a very wide range of organization-related activities in organization. Since socially situated intelligence is one of such activities, this paper makes the concept of organizational learning operational from the computational viewpoint for investigating socially situated intelligence. In particular, this paper focuses on the characteristics of multiagent learning as one kind of socially situated intelligence, and analyzes them using four operationalized learning mechanisms in organizational learning. A careful investigation on the characteristics of multiagent learning has revealed the following implications: (1) there are two levels in the learning mechanisms for multiagent learning (the individual level and organizational level) and each mechanism is divided into two types (single- and double-loop learning). The integration of these four learning mechanisms improves socially situated intelligence; and (2) the following properties support socially situated intelligence: (a) different dimensions in learning mechanisms, (b) interaction among various levels and types of learning mechanisms in addition to interaction among agents, and (c) combination of exploration at an individual level and exploitation at an organizational level.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:comaot:v:5:y:1999:i:3:d:10.1023_a:1009638423221
    DOI: 10.1023/A:1009638423221
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    References listed on IDEAS

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    1. 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.
    2. James G. March, 1991. "Exploration and Exploitation in Organizational Learning," Organization Science, INFORMS, vol. 2(1), pages 71-87, February.
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    Cited by:

    1. 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.

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