Eliciting Informative Priors by Modelling Expert Decision Making

TL;DR

This paper presents a novel Bayesian approach to derive informative prior distributions by modeling expert decision-making processes, especially useful for rare events, without requiring experts to have statistical expertise.

Contribution

It introduces a new method that infers priors from decision data, improving prior elicitation by leveraging real decision processes and reducing expert statistical knowledge requirements.

Findings

Method successfully infers priors from historical decision data.

Application to recidivism demonstrates practical utility.

Potential to enhance decision-making accuracy.

Abstract

This article introduces a new method for eliciting prior distributions from experts. The method models an expert decision-making process to infer a prior probability distribution for a rare event $A$. More specifically, assuming there exists a decision-making process closely related to $A$ which forms a decision $Y$, where a history of decisions have been collected. By modelling the data observed to make the historic decisions, using a Bayesian model, an analyst can infer a distribution for the parameters of the random variable $Y$. This distribution can be used to approximate the prior distribution for the parameters of the random variable for event $A$. This method is novel in the field of prior elicitation and has the potential of improving upon current methods by using real-life decision-making processes, that can carry real-life consequences, and, because it does not require an expert to have statistical knowledge. Future decision making can be improved upon using this method, as it highlights variables that are impacting the decision making process. An application for eliciting a prior distribution of recidivism, for an individual, is used to explain this method further.