Valuing Climate Damages Updating Estimation of the Social Cost of Carbon Dioxide (2017) / Chapter Skim
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Appendix C: Elicitation of Expert Opini
Appendix C: Elicitation of Expert Opini
Pages 221-228
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are typically used to describe a formal process in which multiple experts report their individual subjective probability distributions for the quantity. This usage is distinct from less formal methods in which someone provides a best guess or other estimate of the quantity.
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For example, the expected fraction of realizations that fall outside the ranges defined by the expert's 10th and 90th percentiles for the corresponding quantities is 20 percent; the probability that the actual number of realizations outside these intervals could have arisen by chance if the expert were well calibrated can be calculated using conventional statistical methods. Expect elicitation can be conducted using more or less elaborate methods.
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The third step is in-person interviews with each expert, during which the expert provides subjective probability distributions for the relevant quantities. These interviews often take several hours.
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During the elicitation process, it is common to help the expert address the quantity from multiple perspectives, to help in reporting her or his best judgment. For example, the same concept could be framed alternatively as a growth rate or a growth factor (or a future level conditional on a specified current level)
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. Commonly advocated methods of expert selection include inviting people whose work is most often cited or asking such people whom they would nominate as well qualified.
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In principle, a Bayesian approach in which the experts' distributions are interpreted as data and used to update some prior distribution seems logical, but it is problematic. Such an approach requires a joint likelihood function, that is, a joint conditional distribution that describes the probability that each expert will provide each possible subjective distribution, conditional on the true value of the quantity.
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have shown that the performance-weighted average of distributions usually outperforms the simple average, where performance is again measured again by calibration and informativeness (and is often evaluated on seed variables not used to define the weights, because the value of the quantity of interest in many expert elicitation studies remains unknown)
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Washington, DC: U.S. Environmental Protection Agency.
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