Eliciting Informative Priors by Modeling Expert Decision Making

Julia R. Falconer (), Eibe Frank (), Devon L. L. Polaschek () and Chaitanya Joshi ()
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Julia R. Falconer: Department of Mathematics, University of Waikato, Hamilton 3216, New Zealand; New Zealand Institute of Security and Crime Science, Hamilton 3216, New Zealand
Eibe Frank: Department of Computer Science, University of Waikato, Hamilton 3216, New Zealand
Devon L. L. Polaschek: New Zealand Institute of Security and Crime Science, Hamilton 3216, New Zealand; School of Psychology, New Zealand Institute of Security and Crime Science, University of Waikato, Hamilton 3216, New Zealand
Chaitanya Joshi: New Zealand Institute of Security and Crime Science, Hamilton 3216, New Zealand; Department of Statistics, University of Auckland, Auckland 1142, New Zealand

Decision Analysis, 2024, vol. 21, issue 2, 77-90

Abstract: There are significant limitations to current methods for eliciting the prior beliefs of experts. To combat some of these limitations, this paper proposes an alternative approach that infers an expert’s prior beliefs about an uncertain event, A , from the expert’s past decisions. We show that an analyst can use past information on an expert’s decision-making task, contingent on an expert’s prior of A , to model the decision-making process and infer an approximation of the prior for A . This concept is illustrated by an application to recidivism. We conclude this work by highlighting important directions for future research.

Keywords: Bayesian methods; prior elicitation; subjective; prior distribution; uncertainty (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ordeca:v:21:y:2024:i:2:p:77-90

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