Incentive-Compatible Elicitation of Quantiles

TL;DR

This paper introduces incentive-compatible mechanisms for eliciting expert quantiles, ensuring truthful reporting in both prior and posterior contexts, which is crucial when data is scarce or unreliable.

Contribution

It proposes novel incentive-compatible elicitation methods using external randomization for both prior and posterior quantile reporting, enhancing accuracy in expert information gathering.

Findings

Mechanism encourages truthful reporting of quantiles.

Applicable to prior and posterior elicitation scenarios.

Improves accuracy of expert-provided information.

Abstract

Incorporation of expert information in inference or decision settings is often important, especially in cases where data are unavailable, costly or unreliable. One approach is to elicit prior quantiles from an expert and then to fit these to a statistical distribution and proceed according to Bayes rule. Quantiles are often thought to be easier to elicit than moments. An incentive-compatible elicitation method using an external randomization is available. Such a mechanism will encourage the expert to exert the care necessary to report accurate information. A second application might be called posterior elicitation. Here an analysis has been done and the results must be reported to a decision maker. For a variety of reasons (possibly including the reward system in the corporate hierarchy) the modeler might need the right incentive system to report results accurately. Again, eliciting posterior quantiles can be done with an incentive compatible mechanism.