Bayesian Persuasion for Containing SIS Epidemics with Asymptomatic Infection

TL;DR

This paper models how individuals decide to adopt protection during an SIS epidemic when they receive noisy signals about their infection status, analyzing the impact of information disclosure on infection levels.

Contribution

It introduces a Bayesian persuasion framework to epidemic games, characterizing equilibrium behavior with asymmetric information and analyzing effects of information disclosure.

Findings

Partial information disclosure can reduce infection rates compared to full disclosure.

Agents' decision-making is influenced by noisy signals about their infection status.

The paper characterizes equilibrium conditions under asymmetric information.

Abstract

We investigate the strategic behavior of a large population of agents who decide whether to adopt a costly partially effective protection or remain unprotected against the susceptible-infected-susceptible epidemic. In contrast with most prior works on epidemic games, we assume that the agents are not aware of their true infection status while making decisions. We adopt the Bayesian persuasion framework where the agents receive a noisy signal regarding their true infection status, and maximize their expected utility computed using the posterior probability of being infected conditioned on the received signal. We characterize the stationary Nash equilibrium of this setting under suitable assumptions, and identify conditions under which partial information disclosure leads to a smaller proportion of infected individuals at the equilibrium compared to full information disclosure, and vice versa.