Using the Data Agreement Criterion to Rank Experts' Beliefs

TL;DR

This paper introduces a method to rank experts' beliefs by assessing their predictive accuracy and certainty using a Bayesian framework and an extended prior-data conflict measure, enabling comparison of expert opinions with actual data.

Contribution

It presents a novel approach to evaluate and compare experts' beliefs based on their predictive performance and certainty, extending existing conflict measures to multiple priors.

Findings

The method successfully ranked financial experts based on turnover predictions.

It identified which experts' beliefs aligned best with observed data.

The approach facilitates assessing agreement between expert judgments and data.

Abstract

Experts' beliefs embody a present state of knowledge. It is desirable to take this knowledge into account when doing analyses or making decisions. Yet ranking experts based on the merit of their beliefs is a difficult task. In this paper we show how experts can be ranked based on their knowledge and their level of (un)certainty. By letting experts specify their knowledge in the form of a probability distribution we can assess how accurately they can predict new data, and how appropriate their level of (un)certainty is. The expert's specified probability distribution can be seen as a prior in a Bayesian statistical setting. By extending an existing prior-data conflict measure to evaluate multiple priors, i.e. experts' beliefs, we can compare experts with each other and the data to evaluate their appropriateness. Using this method new research questions can be asked and answered, for instance: Which expert predicts the new data best? Is there agreement between my experts and the data? Which experts' representation is more valid or useful? Can we reach convergence between expert judgement and data? We provided an empirical example ranking (regional) directors of a large financial institution based on their predictions of turnover.