Information Elicitation from Teams of Privacy-Conscious Experts

23 Pages Posted: 20 Oct 2022

See all articles by Ruslan Momot

Ruslan Momot

University of Michigan, Stephen M. Ross School of Business

Marat Salikhov

Yale School of Management

Date Written: October 14, 2022

Abstract

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

Momot, Ruslan and Salikhov, Marat, Information Elicitation from Teams of Privacy-Conscious Experts (October 14, 2022). Available at SSRN: https://ssrn.com/abstract=4248487 or http://dx.doi.org/10.2139/ssrn.4248487

Ruslan Momot (Contact Author)

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States

HOME PAGE: http://www.ruslanmomot.info

Marat Salikhov

Yale School of Management ( email )

165 Whitney Ave
New Haven, CT 06511

HOME PAGE: http://maratsalikhov.com

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