Robust (Bayesian) Persuasion

51 Pages Posted: 14 Feb 2020

See all articles by Piotr Dworczak

Piotr Dworczak

Northwestern University - Department of Economics

Alessandro Pavan

Northwestern University

Date Written: January 20, 2020

Abstract

We propose a robust solution concept for the model of persuasion under commitment that accounts for the Sender's ambiguity over (i) the exogenous sources of information the Receivers may learn from, and (ii) the way the Receivers play, given the available information. The Sender proceeds in two steps. First, she identifies all information structures that yield the largest payoff in the "worst-case scenario," i.e., when Nature provides information and coordinates the Receivers' play to minimize the Sender's payoff. Second, she picks an information structure that, in case Nature and the Receivers play favorably to her, maximizes her expected payoff over all information structures that are \worst-case optimal." Thus, a robust solution is an information structure that is best-case optimal among all worst-case optimal ones. We characterize properties of robust solutions, identify conditions under which robustness requires separation of certain states, and qualify in what sense robustness calls for more information disclosure than standard Bayesian persuasion. Finally, we discuss how some of the results in the Bayesian persuasion literature change once robustness is accounted for.

Keywords: persuasion, information design, robustness, worst-case optimality

JEL Classification: D83, G28, G33

Suggested Citation

Dworczak, Piotr and Pavan, Alessandro, Robust (Bayesian) Persuasion (January 20, 2020). Available at SSRN: https://ssrn.com/abstract=3523114 or http://dx.doi.org/10.2139/ssrn.3523114

Piotr Dworczak (Contact Author)

Northwestern University - Department of Economics ( email )

2003 Sheridan Road
Evanston, IL 60208
United States

Alessandro Pavan

Northwestern University

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