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

Hammitt, James K. and Zhang, Yifan (2012) Combining Experts’ Judgments: Comparison of Algorithmic Methods using Synthetic Data. TSE Working Paper, n. 12-293

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Official URL: http://tse-fr.eu/pub/25761

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.

Item Type: Monograph (Working Paper)
Language: English
Date: March 2012
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 09 Jul 2014 17:24
Last Modified: 07 Mar 2018 13:22
OAI ID: oai:tse-fr.eu:25761
URI: http://publications.ut-capitole.fr/id/eprint/15258

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