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State of the Art---Encoding Subjective Probabilities: A Psychological and Psychometric Review


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  • Thomas S. Wallsten

    (University of North Carolina)

  • David V. Budescu

    (University of Haifa, Israel)

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    In order to review the empirical literature on subjective probability encoding from a psychological and psychometric perspective, it is first suggested that the usual encoding techniques can be regarded as instances of the general methods used to scale psychological variables. It is then shown that well-established concepts and theories from measurement and psychometric theory can provide a general framework for evaluating and assessing subjective probability encoding. The actual review of the literature distinguishes between studies conducted with nonexperts and with experts. In the former class, findings related to the reliability, internal consistency, and external validity of the judgments are critically discussed. The latter class reviews work relevant to some of these characteristics separately for several fields of expertise. In die final section of the paper the results from these two classes of studies are summarized and related to a view of vague subjective probabilities. Problems deserving additional attention and research are identified.

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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 29 (1983)
    Issue (Month): 2 (February)
    Pages: 151-173

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    Handle: RePEc:inm:ormnsc:v:29:y:1983:i:2:p:151-173

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    Keywords: decision analysis; probability; subjective probability;


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    Cited by:
    1. Durbach, Ian N. & Stewart, Theodor J., 2011. "An experimental study of the effect of uncertainty representation on decision making," European Journal of Operational Research, Elsevier, vol. 214(2), pages 380-392, October.
    2. Stone, Eric R. & Opel, Ryan B., 2000. "Training to Improve Calibration and Discrimination: The Effects of Performance and Environmental Feedback," Organizational Behavior and Human Decision Processes, Elsevier, vol. 83(2), pages 282-309, November.
    3. Daniel G. Goldstein & David Rothschild, 2014. "Lay understanding of probability distributions," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(1), pages 1-14, January.
    4. Wang, W., 1997. "Subjective estimation of the delay time distribution in maintenance modelling," European Journal of Operational Research, Elsevier, vol. 99(3), pages 516-529, June.
    5. Brenner, Lyle A., 2003. "A random support model of the calibration of subjective probabilities," Organizational Behavior and Human Decision Processes, Elsevier, vol. 90(1), pages 87-110, January.
    6. Fischer, Ilan & Budescu, David V., 2005. "When do those who know more also know more about how much they know? The development of confidence and performance in categorical decision tasks," Organizational Behavior and Human Decision Processes, Elsevier, vol. 98(1), pages 39-53, September.
    7. Lam, K.Y. & Koning, A.J. & Franses, Ph.H.B.F., 2007. "Confidence intervals for maximal reliability of probability judgments," Econometric Institute Research Papers EI 2007-09, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. David V. Budescu & Timothy R. Johnson, 2011. "A model-based approach for the analysis of the calibration of probability judgments," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(8), pages 857-869, December.
    9. Brenner, Lyle & Griffin, Dale & Koehler, Derek J., 2005. "Modeling patterns of probability calibration with random support theory: Diagnosing case-based judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 97(1), pages 64-81, May.
    10. Lau, Hon-Shiang & Somarajan, C., 1995. "A proposal on improved procedures for estimating task-time distributions in PERT," European Journal of Operational Research, Elsevier, vol. 85(1), pages 39-52, August.
    11. López Martín, M.M. & García García, C.B. & García Pérez, J. & Sánchez Granero, M.A., 2012. "An alternative for robust estimation in Project Management," European Journal of Operational Research, Elsevier, vol. 220(2), pages 443-451.


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