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Combining Experts’ Judgments: Comparison of Algorithmic Methods using Synthetic Data

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  • Hammitt, James K.
  • Zhang, Yifan

Abstract

Expert judgment (or expert elicitation) is a formal process for eliciting judgments from subject-matter experts about the value of a decision-relevant quantity. Judgments in the form of subjective probability distributions are obtained from several experts, raising the question how best to combine information from multiple experts. A number of algorithmic approaches have been proposed, of which the most commonly employed is the equal-weight combination (the average of the experts’ distributions). We evaluate the properties of five combination methods (equal-weight, best-expert, performance, frequentist, and copula) using simulated expert-judgment data for which we know the process generating the experts’ distributions. We examine cases in which two well-calibrated experts are of equal or unequal quality and their judgments are independent, positively or negatively dependent. In this setting, the copula, frequentist, and best-expert approaches perform better and the equal-weight combination method performs worse than the alternative approaches.

Suggested Citation

  • Hammitt, James K. & Zhang, Yifan, 2012. "Combining Experts’ Judgments: Comparison of Algorithmic Methods using Synthetic Data," TSE Working Papers 12-293, Toulouse School of Economics (TSE).
  • Handle: RePEc:tse:wpaper:25761
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    References listed on IDEAS

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    1. Robert T. Clemen & Gregory W. Fischer & Robert L. Winkler, 2000. "Assessing Dependence: Some Experimental Results," Management Science, INFORMS, vol. 46(8), pages 1100-1115, August.
    2. Stephen C. Hora, 2004. "Probability Judgments for Continuous Quantities: Linear Combinations and Calibration," Management Science, INFORMS, vol. 50(5), pages 597-604, May.
    3. Robert L. Winkler & Roy M. Poses, 1993. "Evaluating and Combining Physicians' Probabilities of Survival in an Intensive Care Unit," Management Science, INFORMS, vol. 39(12), pages 1526-1543, December.
    4. repec:eee:reensy:v:93:y:2008:i:5:p:657-674 is not listed on IDEAS
    5. Robert T. Clemen & Terence Reilly, 1999. "Correlations and Copulas for Decision and Risk Analysis," Management Science, INFORMS, vol. 45(2), pages 208-224, February.
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    1. repec:eee:reensy:v:121:y:2014:i:c:p:72-82 is not listed on IDEAS

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