Sequential Non-Bayesian Persuasion

TL;DR

This paper introduces a model of persuasion where the receiver updates beliefs as a convex combination of prior and Bayesian posterior, revealing that sequential persuasion can be beneficial even when classic Bayesian updating suggests otherwise.

Contribution

It demonstrates that sequential persuasion can be advantageous in strategic information transmission, even with conservative Bayesian receivers and biases.

Findings

Sequential persuasion benefits the sender in many environments.

Biases do not affect the maximal payoff under sequential persuasion.

Classic Bayesian updating does not always apply in strategic persuasion contexts.

Abstract

We study a model of persuasion in which the receiver is a `conservative Bayesian' whose updated belief is a convex combination of the prior and the correct Bayesian posterior. While in the classic Bayesian case providing information sequentially is never valuable, we show that the sender gains from sequential persuasion in many of the environments considered in the literature on strategic information transmission. We also consider the case in which the sender and receiver are both biased and prove that the maximal expected payoff for the sender under sequential persuasion is the same as in the case where neither of them is biased.