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A Comparison of Optimal and Adaptive Decision Mechanisms in an Organizational Setting

Author

Listed:
  • Edwin H. Neave

    (Queen's University)

  • Edward R. Petersen

    (Queen's University)

Abstract

This paper uses simulation experiments to compare optimal and adaptive decision mechanisms for firms modelled as operating in a duopoly. Optimal decision policies are derived using a dynamic programming approach based on the Cyert-DeGroot (Cyert, R. M., M. H. DeGroot. 1970. Muhiperiod decision models with alternating choice as a solution to the duopoly problem. Quart J. Econom. 84 410--429.) duopoly models, while the adaptive decision mechanism uses the Cyert-March behavioral theory of the firm (Cyert, Richard, James March. 1963. A Behavioral Theory of the Firm. Prentice-Hall, Englewood Cliffs, New Jersey.). Using a cetera paribus approach that unequivocally establishes causality, the simulations develop conditions under which adaptive decision processes generate profit performance comparable to that generated by normative models. The simulations suggest that good organizational performance requires both good decision making processes and efficient operation of the organization, that good performance can be achieved in at least three ways: by employing optimal decision processes; by the use of adaptive processes in a highly competitive environment; or by the use of adaptive processes when management has high aspirations. The implications of these findings for implementing management science studies of organization decision-making are considered.

Suggested Citation

  • Edwin H. Neave & Edward R. Petersen, 1980. "A Comparison of Optimal and Adaptive Decision Mechanisms in an Organizational Setting," Management Science, INFORMS, vol. 26(8), pages 810-822, August.
  • Handle: RePEc:inm:ormnsc:v:26:y:1980:i:8:p:810-822
    DOI: 10.1287/mnsc.26.8.810
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    Cited by:

    1. John W. Boudreau, 2004. "50th Anniversary Article: Organizational Behavior, Strategy, Performance, and Design in Management Science," Management Science, INFORMS, vol. 50(11), pages 1463-1476, November.

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