Incentives for self-extremized expert judgments to alleviate the shared-information problem.

Abstract

Simple average of subjective forecasts is known to be effective in estimating uncertain quantities. However, benefits of averaging could be limited when forecasters have shared information, resulting in overrepresentation of the shared information in average forecast. This article proposes a simple incentive-based solution to the shared-information problem. Experts are grouped with nonexperts in forecasting crowds and they are rewarded for the accuracy of crowd average instead of their individual accuracy. In equilibrium, experts anticipate the overrepresentation of shared information and extremize their forecasts toward their private information to boost crowd accuracy. The self-extremization in individual expert forecasts alleviates the shared-information problem. Experimental evidence suggests that incentives for crowd accuracy could induce self-extremization even in small crowds where winner-take-all contests (another incentive-based solution) are not effective.

Introduction to the special issue on judgment and decision research on the wisdom of the crowds.

“…The articles in this special issue on the wisdom of the crowds contribute to outstanding questions regarding what to aggregate (Feng & Budescu, 2024; Hasan et al, 2024; Summerville et al, 2024), optimal weighting schemes (Huang et al, 2024; Collins et al, 2024; Powell et al, 2024), incentives (Peker, 2024), establishing expertise (Howe et al, 2024), social processes (Beauchamp et al, 2024; Mayer & Heck, 2024), and beliefs about the efficacy of crowd wisdom (Schultze et al, 2024). One way to group the articles relates to an overarching question: To what extent should aggregation be left to the crowd members themselves?…”