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Combining Expert Judgments: A Bayesian Approach

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  • Peter A. Morris

    (Xerox Palo Alto Research Center)

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    Abstract

    This paper presents a new approach to the problem of expert resolution. The proposed analytic structure provides a mechanism by which a decision maker can incorporate the possibly conflicting probability assessments of a group of experts. The approach is based upon the Bayesian inferential framework presented in [Morris, P. A. 1974. Decision analysis expert use. Management Sci. 20 (9, May)]. A number of specific results are derived from analysis of a generic model structure. In the single expert continuous variable case, we prove that the decision maker should process a calibrated expert's opinion by multiplying the expert's probability assessment by his own prior probability assessment and normalizing. A method for subjectively calibrating an expert is also presented. In the multi-expert case, we obtain a simple multiplicative rule for combining the expert judgments. We also prove the existence of a composite probability function which measures the joint information contained in the probability assessments generated by a panel of experts. The interesting result is that composite prior should be processed as if it were the probability statement of a single calibrated expert.

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    File URL: http://dx.doi.org/10.1287/mnsc.23.7.679
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 23 (1977)
    Issue (Month): 7 (March)
    Pages: 679-693

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    Handle: RePEc:inm:ormnsc:v:23:y:1977:i:7:p:679-693

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    Cited by:
    1. Thibault Gajdos & Jean-Christophe Vergnaud, 2013. "Decisions with conflicting and imprecise information," Social Choice and Welfare, Springer, vol. 41(2), pages 427-452, July.
    2. Garratt, Anthony & Mise, Emi, 2014. "Forecasting exchange rates using panel model and model averaging," Economic Modelling, Elsevier, vol. 37(C), pages 32-40.
    3. Wolden Bache, Ida & Sofie Jore, Anne & Mitchell, James & Vahey, Shaun P., 2011. "Combining VAR and DSGE forecast densities," Journal of Economic Dynamics and Control, Elsevier, vol. 35(10), pages 1659-1670, October.
    4. Usher, Will & Strachan, Neil, 2013. "An expert elicitation of climate, energy and economic uncertainties," Energy Policy, Elsevier, vol. 61(C), pages 811-821.
    5. Anthony Garratt & James Mitchell & Shaun P. Vahey, 2009. "Measuring Output Gap Uncertainty," Birkbeck Working Papers in Economics and Finance 0909, Birkbeck, Department of Economics, Mathematics & Statistics.
    6. Irene Valsecchi, 2013. "The expert problem: a survey," Economics of Governance, Springer, vol. 14(4), pages 303-331, November.
    7. Victor Zarnowitz & Louis A. Lambros, 1983. "Consensus and Uncertainty in Economic Prediction," NBER Working Papers 1171, National Bureau of Economic Research, Inc.
    8. Anne Sofie Jore & James Mitchell & Shaun Vahey, 2008. "Combining Forecast Densities from VARs with Uncertain Instabilities," Reserve Bank of New Zealand Discussion Paper Series DP2008/18, Reserve Bank of New Zealand.
    9. Kangaspunta, Jussi & Liesiƶ, Juuso & Salo, Ahti, 2012. "Cost-efficiency analysis of weapon system portfolios," European Journal of Operational Research, Elsevier, vol. 223(1), pages 264-275.
    10. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
    11. 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.

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