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Learning in Dynamic Decision Making: The Recognition Process

Author

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  • Cleotilde Gonzalez

    (Carnegie Mellon University)

  • Jose Quesada

    (University of Colorado, Boulder)

Abstract

The apparent difficulty that humans experience when asked to manage dynamic complexity might be related to their inability to discriminate among familiar classes of objects (i.e., flawed recognition). In this study we examined the change in individuals' recognition ability, as measured by the change in the similarity of decisions they made when confronted repeatedly with consistent dynamic situations of varying degrees of similarity. The study generated two primary findings. First, decisions became increasingly similar with task practice, a result that suggests gradually improving discrimination by the participants. Second, the similarity was determined by the interaction of many task features rather than individual task features. The general principles highlighted by this study are applicable to dynamic situations. For example, with practice, decision makers should be able to learn to identify the time at which to intervene to achieve the maximal effect during dynamic decision making.

Suggested Citation

  • Cleotilde Gonzalez & Jose Quesada, 2003. "Learning in Dynamic Decision Making: The Recognition Process," Computational and Mathematical Organization Theory, Springer, vol. 9(4), pages 287-304, December.
    • Handle: RePEc:spr:comaot:v:9:y:2003:i:4:d:10.1023_b:cmot.0000029052.81329.d4
    DOI: 10.1023/B:CMOT.0000029052.81329.d4
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    References listed on IDEAS

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    1. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    2. Gibson, Faison P. & Fichman, Mark & Plaut, David C., 1997. "Learning in Dynamic Decision Tasks: Computational Model and Empirical Evidence," Organizational Behavior and Human Decision Processes, Elsevier, vol. 71(1), pages 1-35, July.
    3. Diehl, Ernst & Sterman, John D., 1995. "Effects of Feedback Complexity on Dynamic Decision Making," Organizational Behavior and Human Decision Processes, Elsevier, vol. 62(2), pages 198-215, May.
    4. Sterman, John., 1994. "Learning in and about complex systems," Working papers 3660-94., Massachusetts Institute of Technology (MIT), Sloan School of Management.
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    Cited by:

    1. Gonzalez, Cleotilde, 2005. "Decision support for real-time, dynamic decision-making tasks," Organizational Behavior and Human Decision Processes, Elsevier, vol. 96(2), pages 142-154, March.

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