Comparison of a new expert elicitation model with the Classical Model, equal weights and single experts, using a cross-validation technique

The problem of ranking and weighting experts' performances when quantitative judgments are being elicited for decision support is considered. A new scoring model, the Expected Relative Frequency model, is presented, based on the closeness between central values provided by the expert and known values used for calibration. Using responses from experts in five different elicitation datasets, a cross-validation technique is used to compare this new approach with the Cooke Classical Model, the Equal Weights model, and individual experts. The analysis is performed using alternative reward schemes designed to capture proficiency either in quantifying uncertainty, or in estimating true central values. Results show that although there is only a limited probability that one approach is consistently better than another, the Cooke Classical Model is generally the most suitable for assessing uncertainties, whereas the new ERF model should be preferred if the goal is central value estimation accuracy.

► A new expert elicitation model, named Expected Relative Frequency (ERF), is presented.
► A cross-validation approach to evaluate the performance of different elicitation models is applied.
► The new ERF model shows the best performance with respect to the point-wise estimates.