Using the Data Agreement Criterion to Rank Experts' Beliefs

Abstract

Experts’ beliefs embody a present state of knowledge. It is desirable to take this knowledge into account when making decisions. However, 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. We evaluate these priors by extending an existing prior-data (dis)agreement measure, the Data Agreement Criterion, and compare this approach to using Bayes factors to assess prior specification. We 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.

Publication
entropy. 20(8)
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Duco Veen
PhD Candidate

I am a PhD Candidate at the department of Methodology & Statistics of Utrecht University in the Netherlands. My research interests lie primarily in the area of Bayesian statistics. My docteral projects under the supervision of Rens van de Schoot concerns the elicitation of expert judgements in the social sciences and we investigate expert-data (dis)agreements.