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Exceptional Paper--Probability Encoding in Decision Analysis

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  • Carl S. Spetzler

    (Stanford Research Institute)

  • Carl-Axel S. Staël Von Holstein

    (Stanford Research Institute)

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    Abstract

    This paper presents the present philosophy and practice used in probability encoding by the Decision Analysis Group at Stanford Research Institute. Probability encoding, the process of extracting and quantifying individual judgment about uncertain quantities, is one of the major functions required in the performance of decision analysis. The paper discusses the setting of the encoding process, including the use of sensitivity analyses to identify crucial state variables for which extensive encoding procedures are appropriate. The importance of balancing modeling and encoding techniques is emphasized and examples of biases and unconscious modes of judgment are reviewed. A variety of encoding methods are presented and their applicability is discussed. The authors recommend and describe a structured interview process that utilizes a trained interviewer and a number of techniques designed to reduce biases and aid in the quantification of judgment.

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    File URL: http://dx.doi.org/10.1287/mnsc.22.3.340
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 22 (1975)
    Issue (Month): 3 (November)
    Pages: 340-358

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    Handle: RePEc:inm:ormnsc:v:22:y:1975:i:3:p:340-358

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    Cited by:
    1. Durbach, Ian N. & Stewart, Theodor J., 2012. "Modeling uncertainty in multi-criteria decision analysis," European Journal of Operational Research, Elsevier, vol. 223(1), pages 1-14.
    2. 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.
    3. Björkqvist, Olof & Idefeldt, Jim & Larsson, Aron, 2010. "Risk assessment of new pricing strategies in the district heating market: A case study at Sundsvall Energi AB," Energy Policy, Elsevier, vol. 38(5), pages 2171-2178, May.
    4. Cerroni, Simone & Notaro, Sandra & Raffaelli, Roberta & Shaw, Douglass W., 2013. "The incorporation of subjective risks into choice experiments to test scenario adjustment," 2013 Second Congress, June 6-7, 2013, Parma, Italy 149894, Italian Association of Agricultural and Applied Economics (AIEAA).
    5. Zuhair, Sugu M.M. & Taylor, Daniel B. & Kramer, Randall A., 1992. "Choice of utility function form: its effect on classification of risk preferences and the prediction of farmer decisions," Agricultural Economics: The Journal of the International Association of Agricultural Economists, International Association of Agricultural Economists, vol. 6(4), April.
    6. Petersen, Elizabeth H. & Fraser, Rob W., 2000. "Grower perceptions of the impact of protein premiums and discounts for wheat," 2000 Conference (44th), January 23-25, 2000, Sydney, Australia 123725, Australian Agricultural and Resource Economics Society.
    7. Anderson, Barry F. & Settle, John W., 1996. "The influence of portfolio characteristics and investment period on investment choice," Journal of Economic Psychology, Elsevier, vol. 17(3), pages 343-358, June.
    8. Cerroni, Simone & Notaro, Sandra & Shaw, W. Douglass, 2013. "How many bad apples are in a bunch? An experimental investigation of perceived pesticide residue risks," Food Policy, Elsevier, vol. 41(C), pages 112-123.
    9. 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.
    10. Bhattacharyya, Siddhartha & Troutt, Marvin D., 2003. "Genetic search over probability spaces," European Journal of Operational Research, Elsevier, vol. 144(2), pages 333-347, January.
    11. Cerroni, Simone & Notaro, Sandra & Shaw, W. Douglass, 2012. "Eliciting and estimating valid subjective probabilities: An experimental investigation of the exchangeability method," Journal of Economic Behavior & Organization, Elsevier, vol. 84(1), pages 201-215.
    12. Cerroni, Simone & Notaro, Sandra & Shaw, W. Douglass, 2012. "Do monetary incentives and chained questions affect the validity of risk estimates elicited via the Exchangeability Method? An experimental investigation," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 125468, International Association of Agricultural Economists.
    13. Jiang, R. & Zhang, W. J. & Ji, P., 2003. "Required characteristics of statistical distribution models for life cycle cost estimation," International Journal of Production Economics, Elsevier, vol. 83(2), pages 185-194, February.
    14. Gillenwater, Michael, 2013. "Probabilistic decision model of wind power investment and influence of green power market," Energy Policy, Elsevier, vol. 63(C), pages 1111-1125.
    15. Goodwin, Paul, 2005. "Providing support for decisions based on time series information under conditions of asymmetric loss," European Journal of Operational Research, Elsevier, vol. 163(2), pages 388-402, June.
    16. Browne, Glenn J. & Pitts, Mitzi G., 2004. "Stopping rule use during information search in design problems," Organizational Behavior and Human Decision Processes, Elsevier, vol. 95(2), pages 208-224, November.
    17. Dewispelare, Aaron R. & Herren, L. Tandy & Clemen, Robert T., 1995. "The use of probability elicitation in the high-level nuclear waste regulation program," International Journal of Forecasting, Elsevier, vol. 11(1), pages 5-24, March.
    18. 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.
    19. Nadkarni, Sucheta & Shenoy, Prakash P., 2001. "A Bayesian network approach to making inferences in causal maps," European Journal of Operational Research, Elsevier, vol. 128(3), pages 479-498, February.
    20. 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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