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Cognitive Heuristics and Feedback in a Dynamic Decision Environment

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  • Don N. Kleinmuntz

    (Department of Management, University of Texas, Austin, Texas 78712)

Abstract

Research on cognitive processes in decision making has identified heuristics that often work well but sometimes lead to serious errors. This paper presents an investigation of the performance of heuristics in a complex dynamic setting, characterized by repeated decisions with feedback. There are three components: (1) A simulated task resembling medical decision problems (diagnosis and treatment) is described. (2) Computer models of decision strategies are developed. These include models based on cognitive heuristics as well as benchmark strategies that indicate the limit of the heuristic strategies' performance. The upper benchmark is based on statistical decision theory, the lower one on random trial and error. (3) Selected task characteristics are systematically varied and their influence on performance evaluated in simulation experiments. Results indicate that task characteristics often studied in past research (e.g., symptom diagonosticity, disease base-rates) have less influence on performance relative to feedback-related aspects of the task. These dynamic characteristics are a major determinant of when heuristics perform well or badly. The results also provide insights about the costs and benefits of various cognitive heuristics. In addition, the possible contribution of this research to the design and evaluation of decision aids is considered.

Suggested Citation

  • Don N. Kleinmuntz, 1985. "Cognitive Heuristics and Feedback in a Dynamic Decision Environment," Management Science, INFORMS, vol. 31(6), pages 680-702, June.
    • Handle: RePEc:inm:ormnsc:v:31:y:1985:i:6:p:680-702
    DOI: 10.1287/mnsc.31.6.680
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    Citations

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    Cited by:

    1. Boatsman, James R. & Moeckel, Cindy & Pei, Buck K. W., 1997. "The Effects of Decision Consequences on Auditors' Reliance on Decision Aids in Audit Planning," Organizational Behavior and Human Decision Processes, Elsevier, vol. 71(2), pages 211-247, August.
    2. Pasaoglu, Güzay, 2011. "Using the decentralized and liberalized electricity market microworld (LEMM) as an educational tool," Energy Policy, Elsevier, vol. 39(1), pages 187-199, January.
    3. Christian Erik Kampmann & John D. Sterman, 2014. "Do markets mitigate misperceptions of feedback?," System Dynamics Review, System Dynamics Society, vol. 30(3), pages 123-160, July.
    4. Sterman, John. & Kampmann, Christian Peter Erik., 1992. "Do markets mitigate misperceptions of feedback in dynamic tasks?," Working papers 3421-92., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    5. Radboud J. Duintjer Tebbens & Kimberly M. Thompson, 2009. "Priority Shifting and the Dynamics of Managing Eradicable Infectious Diseases," Management Science, INFORMS, vol. 55(4), pages 650-663, April.
    6. Bragger, Jennifer DeNicolis & Bragger, Donald & Hantula, Donald A. & Kirnan, Jean, 1998. "Hyteresis and Uncertainty: The Effect of Uncertainty on Delays to Exit Decisions," Organizational Behavior and Human Decision Processes, Elsevier, vol. 74(3), pages 229-253, June.
    7. Lurie, Nicholas H. & Swaminathan, Jayashankar M., 2009. "Is timely information always better? The effect of feedback frequency on decision making," Organizational Behavior and Human Decision Processes, Elsevier, vol. 108(2), pages 315-329, March.
    8. Onur Özgün & Yaman Barlas, 2015. "Effects of systemic complexity factors on task difficulty in a stock management game," System Dynamics Review, System Dynamics Society, vol. 31(3), pages 115-146, July.
    9. Hines, James H., 1987. "A behavioral theory of interest rate behavior," Working papers 1951-87., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    10. Pomerol, Jean-Charles, 1997. "Artificial intelligence and human decision making," European Journal of Operational Research, Elsevier, vol. 99(1), pages 3-25, May.
    11. Goodman, Jodi S., 1998. "The Interactive Effects of Task and External Feedback on Practice Performance and Learning," Organizational Behavior and Human Decision Processes, Elsevier, vol. 76(3), pages 223-252, December.
    12. Miragliotta, Giovanni & Brun, Alessandro & Soydan, Ilker A., 2009. "Coordinating multi-business sales through management simulators," International Journal of Production Economics, Elsevier, vol. 121(2), pages 533-549, October.
    13. Hämäläinen, Raimo P. & Luoma, Jukka & Saarinen, Esa, 2013. "On the importance of behavioral operational research: The case of understanding and communicating about dynamic systems," European Journal of Operational Research, Elsevier, vol. 228(3), pages 623-634.
    14. Robin L. Dillon & Vicki M. Bier & Richard Sheffield John & Abdullah Althenayyan, 2023. "Closing the Gap Between Decision Analysis and Policy Analysts Before the Next Pandemic," Decision Analysis, INFORMS, vol. 20(2), pages 109-132, June.
    15. Josef Frysak & Edward W. N. Bernroider & Konradin Maier, 2017. "An Effort Feedback Perspective on Persuasive Decision Aids for Multi-Attribute Decision-Making," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(01), pages 161-181, January.
    16. Burmeister, Katrin & Schade, Christian, 2007. "Are entrepreneurs' decisions more biased? An experimental investigation of the susceptibility to status quo bias," Journal of Business Venturing, Elsevier, vol. 22(3), pages 340-362, May.
    17. Cronin, Matthew A. & Gonzalez, Cleotilde & Sterman, John D., 2009. "Why don't well-educated adults understand accumulation? A challenge to researchers, educators, and citizens," Organizational Behavior and Human Decision Processes, Elsevier, vol. 108(1), pages 116-130, January.

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