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Aggregating Probabilistic Forecasts from Incoherent and Abstaining Experts

Author

Listed:
  • Joel B. Predd

    (RAND Corporation, Pittsburgh, Pennsylvania 15213)

  • Daniel N. Osherson

    (Department of Psychology, Princeton University, Princeton, New Jersey 08544)

  • Sanjeev R. Kulkarni

    (Department of Electrical Engineering, Princeton University, Princeton, New Jersey 08544)

  • H. Vincent Poor

    (Department of Electrical Engineering, Princeton University, Princeton, New Jersey 08544)

Abstract

Decision makers often rely on expert opinion when making forecasts under uncertainty. In doing so, they confront two methodological challenges: the elicitation problem, which requires them to extract meaningful information from experts; and the aggregation problem, which requires them to combine expert opinion by resolving disagreements. Linear averaging is a justifiably popular method for addressing aggregation, but its robust simplicity makes two requirements on elicitation. First, each expert must offer probabilistically coherent forecasts; second, each expert must respond to all our queries. In practice, human judges (even experts) may be incoherent, and may prefer to assess only the subset of events about which they are comfortable offering an opinion. In this paper, a new methodology is developed for combining expert assessment of chance. The method retains the conceptual and computational simplicity of linear averaging, but generalizes the standard approach by relaxing the requirements on expert elicitation. The method also enjoys provable performance guarantees, and in experiments with real-world forecasting data is shown to offer both computational efficiency and competitive forecasting gains as compared to rival aggregation methods. This paper is relevant to the practice of decision analysis, for it enables an elicitation methodology in which judges have freedom to choose the events they assess.

Suggested Citation

  • Joel B. Predd & Daniel N. Osherson & Sanjeev R. Kulkarni & H. Vincent Poor, 2008. "Aggregating Probabilistic Forecasts from Incoherent and Abstaining Experts," Decision Analysis, INFORMS, vol. 5(4), pages 177-189, December.
    • Handle: RePEc:inm:ordeca:v:5:y:2008:i:4:p:177-189
    DOI: 10.1287/deca.1080.0119
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    References listed on IDEAS

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    Cited by:

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      • L. Robin Keller & Manel Baucells & John C. Butler & Philippe Delquié & Jason R. W. Merrick & Gregory S. Parnell & Ahti Salo, 2008. "From the Editors..," Decision Analysis, INFORMS, vol. 5(4), pages 173-176, December.
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    11. L. Robin Keller, 2009. "From the Editor..," Decision Analysis, INFORMS, vol. 6(1), pages 1-3, March.
    12. Christopher W. Karvetski & David R. Mandel & Daniel Irwin, 2020. "Improving Probability Judgment in Intelligence Analysis: From Structured Analysis to Statistical Aggregation," Risk Analysis, John Wiley & Sons, vol. 40(5), pages 1040-1057, May.
    13. Guanchun Wang & Sanjeev R. Kulkarni & H. Vincent Poor & Daniel N. Osherson, 2011. "Aggregating Large Sets of Probabilistic Forecasts by Weighted Coherent Adjustment," Decision Analysis, INFORMS, vol. 8(2), pages 128-144, June.
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    17. Yuyu Fan & David V. Budescu & David Mandel & Mark Himmelstein, 2019. "Improving Accuracy by Coherence Weighting of Direct and Ratio Probability Judgments," Decision Analysis, INFORMS, vol. 16(3), pages 197-217, September.
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    20. David R. Mandel & Robert N. Collins & Evan F. Risko & Jonathan A. Fugelsang, 2020. "Effect of confidence interval construction on judgment accuracy," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(5), pages 783-797, September.

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