Preparing for the Worst but Hoping for the Best: Robust (Bayesian) Persuasion

69 Pages Posted: 14 Feb 2020 Last revised: 2 Nov 2020

See all articles by Piotr Dworczak

Piotr Dworczak

Northwestern University - Department of Economics

Alessandro Pavan

Northwestern University

Date Written: October 7, 2020


We propose a robust solution concept for Bayesian persuasion that
accounts for the Sender's concern that her Bayesian belief about the
environment---which we call the conjecture---may be false.
Specifically, the Sender is uncertain about the exogenous sources
of information the Receivers may learn from, and about strategy selection.
She first identifies all information policies 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. Then, she uses the conjecture to pick the optimal policy among
the 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 and develop a few new applications.

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

JEL Classification: D83, G28, G33

Suggested Citation

Dworczak, Piotr and Pavan, Alessandro, Preparing for the Worst but Hoping for the Best: Robust (Bayesian) Persuasion (October 7, 2020). Available at SSRN: or

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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