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Where Forward-Looking and Backward-Looking Models Meet

Author

Listed:
  • Peter J. Burke

    (Washington State University)

  • Louis N. Gray

    (Washington State University)

Abstract

The present paper begins by deriving an instantaneous formulation for the backward-looking (reinforcement based learning) satisfaction balance model of Gray and Tallman (1984). This model is then used to generate interactional data from four simulated agents in a network interaction experiment. Because this initial model does not generate stable interaction structures in the network experiment, it is altered step by step in the direction of a forward-looking (agent with goals) model that has been shown to generate such stable interaction structures. The purpose of the modifications are to learn what aspects of the forward-looking model are needed to evolve a stable interaction structure, and to learn how these aspects may be incorporated into a model that remains essentially reinforcement based.

Suggested Citation

  • Peter J. Burke & Louis N. Gray, 1999. "Where Forward-Looking and Backward-Looking Models Meet," Computational and Mathematical Organization Theory, Springer, vol. 5(2), pages 75-95, July.
    • Handle: RePEc:spr:comaot:v:5:y:1999:i:2:d:10.1023_a:1009668501158
    DOI: 10.1023/A:1009668501158
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    Cited by:

    1. Yu, Biying & Zhang, Junyi & Li, Xia, 2017. "Dynamic life course analysis on residential location choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 104(C), pages 281-292.

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