Information Elicitation from Teams of Privacy-Conscious Experts
23 Pages Posted: 20 Oct 2022
Date Written: October 14, 2022
Firms' decision making commonly relies on processes that elicit information from teams of experts. Yet such processes perform poorly when experts fear their participation might reveal information that could be used against them. We address this problem---via a mechanism that protects the privacy of experts' information---to construct a parsimonious game-theoretic model that explores a firm's and its experts' incentives under this mechanism. In our model, the firm employs experts to predict the unknown state of the world and then makes a decision based on that prediction. The experts receive independent and informative signals about the state of the world, signals that the firm seeks to elicit. A key aspect of this model is that the privacy concerns of experts may render them unwilling to report their signals truthfully. Our analysis reveals that it may be optimal for the firm to intentionally garble (i.e., add noise to) experts' reports before they are made public and used for decision making. This garbling encourages the experts to report their signals truthfully because it addresses their privacy concerns by making their public reports differentially private and thus providing each expert with plausible deniability. We find that the conventional wisdom on judgment aggregation (which does not account for privacy concerns) is overturned when experts are privacy conscious. For example: a larger team of experts may actually perform worse than a smaller one; and the presence on the team of a more capable expert may, in fact, be detrimental to the team's performance.
Keywords: differential privacy, information elicitation, privacy preservation
JEL Classification: C72, D81, D82, M20, M10
Suggested Citation: Suggested Citation