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This article introduces a new method for eliciting prior distributions fromexperts. The method models an expert decision-making process to infer a priorprobability distribution for a rare event $A$. More specifically, assumingthere exists a decision-making process closely related to $A$ which forms adecision $Y$, where a history of decisions have been collected. By modellingthe data observed to make the historic decisions, using a Bayesian model, ananalyst can infer a distribution for the parameters of the random variable $Y$.This distribution can be used to approximate the prior distribution for theparameters of the random variable for event $A$. This method is novel in thefield of prior elicitation and has the potential of improving upon currentmethods by using real-life decision-making processes, that can carry real-lifeconsequences, and, because it does not require an expert to have statisticalknowledge. Future decision making can be improved upon using this method, as ithighlights variables that are impacting the decision making process. Anapplication for eliciting a prior distribution of recidivism, for anindividual, is used to explain this method further.

Eliciting Informative Priors by Modelling Expert Decision Making