Dynamic Non-Bayesian Persuasion

TL;DR

This paper investigates how dynamic persuasion strategies can benefit a sender in non-Bayesian settings, showing that under certain conditions, static and dynamic persuasion are equivalent.

Contribution

It characterizes receiver updating rules under which dynamic persuasion offers no advantage over static persuasion, extending the understanding of persuasion in non-Bayesian environments.

Findings

Dynamic persuasion can outperform static persuasion when the receiver is not Bayesian.

Divisibility characterizes receiver updating rules where static and dynamic persuasion are equivalent.

Restricting to static persuasion is without loss under divisible updating rules.

Abstract

If a sender in a persuasion game can use a sequence of experiments rather than a single experiment, does this change the sender's value? We show that the sender can benefit more from dynamic persuasion than from static persuasion when the receiver is not Bayesian. Our main result shows that, under mild regularity conditions, divisibility, introduced in Cripps (2018), characterizes the receiver's updating rules under which the sender is indifferent between static and dynamic persuasion in any environment. Consequently, restricting attention to static persuasion is without loss precisely under divisible updating rules.