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Estimating Uncertainties Using Judgmental Forecasts with Expert Heterogeneity

Author

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  • Saurabh Bansal

    (Pennsylvania State University, State College, Pennsylvania 16803)

  • Genaro J. Gutierrez

    (University of Texas at Austin, Austin, Texas 78712)

Abstract

In this paper, we develop a new characterization of multiple-point forecasts provided by experts and use it in an optimization framework to deduce actionable signals, including the mean, standard deviation, or a combination of the two for underlying probability distributions. This framework consists of three steps: (1) calibrate experts’ point forecasts using historical data to determine which quantile they provide, on average, when asked for forecasts, (2) quantify the precision in the experts’ forecasts around their average quantile, and (3) use this calibration information in an optimization framework to deduce the signals of interest. We also show that precision and accuracy in expert judgments are complementary in terms of their informativeness. We also discuss implementation of the development and the realized benefits at a large government project in the agribusiness domain.

Suggested Citation

  • Saurabh Bansal & Genaro J. Gutierrez, 2020. "Estimating Uncertainties Using Judgmental Forecasts with Expert Heterogeneity," Operations Research, INFORMS, vol. 68(2), pages 363-380, March.
    • Handle: RePEc:inm:oropre:v:68:y:2020:i:2:p:363-380
    DOI: 10.1287/opre.2019.1938
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    References listed on IDEAS

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