  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.
