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Combining the Opinions of Experts Who Partition Events Differently

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  • Robert F. Bordley

    (General Motors Research Laboratories and University of Michigan, Ann Arbor, Michigan 48109)

Abstract

This paper focuses on updating a client's beliefs about an event based on information about the different probabilities that various experts assess for that event. A substantial literature solves this problem when all experts assess their probabilities over the same partitioning of the possible outcomes of an event. But different experts often think about the same problem in quite different ways. This can lead to differences in how experts prefer to partition the possible outcomes of an event. Forcing the experts to use a common partition could lead to less informative probability assessments. Thus, this paper presents a new approach for combining probability assessments from different experts, which allows experts to assess their probability assessments across different partitionings.

Suggested Citation

  • Robert F. Bordley, 2009. "Combining the Opinions of Experts Who Partition Events Differently," Decision Analysis, INFORMS, vol. 6(1), pages 38-46, March.
    • Handle: RePEc:inm:ordeca:v:6:y:2009:i:1:p:38-46
    DOI: 10.1287/deca.1080.0127
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    References listed on IDEAS

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    Cited by:

    1. Jason R. W. Merrick & Fabrizio Ruggeri & Refik Soyer & L. Robin Keller, 2012. "From the Editors---Games and Decisions in Reliability and Risk," Decision Analysis, INFORMS, vol. 9(2), pages 81-85, June.
    2. L. Robin Keller, 2009. "From the Editor..," Decision Analysis, INFORMS, vol. 6(1), pages 1-3, March.
    3. Erin Baker & Olaitan Olaleye, 2013. "Combining Experts: Decomposition and Aggregation Order," Risk Analysis, John Wiley & Sons, vol. 33(6), pages 1116-1127, June.
    4. L. Robin Keller & Ali Abbas & J. Eric Bickel & Vicki M. Bier & David V. Budescu & John C. Butler & Enrico Diecidue & Robin L. Dillon-Merrill & Raimo P. Hämäläinen & Kenneth C. Lichtendahl & Jason R. W, 2012. "From the Editors ---Brainstorming, Multiplicative Utilities, Partial Information on Probabilities or Outcomes, and Regulatory Focus," Decision Analysis, INFORMS, vol. 9(4), pages 297-302, December.
    5. Yucheng Dong & Yao Li & Ying He & Xia Chen, 2021. "Preference–Approval Structures in Group Decision Making: Axiomatic Distance and Aggregation," Decision Analysis, INFORMS, vol. 18(4), pages 273-295, December.
    6. L. Robin Keller, 2011. "From the Editor ---Multiattribute and Intertemporal Preferences, Probability, and Stochastic Processes: Models and Assessment," Decision Analysis, INFORMS, vol. 8(3), pages 165-169, September.
    7. Robert F. Bordley, 2011. "Using Bayes' Rule to Update an Event's Probabilities Based on the Outcomes of Partially Similar Events," Decision Analysis, INFORMS, vol. 8(2), pages 117-127, June.
    8. L. Robin Keller & Kelly M. Kophazi, 2012. "From the Editors ---Copulas, Group Preferences, Multilevel Defenders, Sharing Rewards, and Communicating Analytics," Decision Analysis, INFORMS, vol. 9(3), pages 213-218, September.
    9. Robert F. Bordley & Elena Katok & L. Robin Keller, 2010. "Honoring Michael H. Rothkopf's Legacy of Rigor and Relevance in Auction Theory: From the Editors," Decision Analysis, INFORMS, vol. 7(1), pages 1-4, March.
    10. L. Robin Keller & Kelly M. Kophazi, 2011. "From the Editors---Deterrence, Multiattribute Utility, and Probability and Bayes' Updating," Decision Analysis, INFORMS, vol. 8(2), pages 83-87, June.
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