Persuasion and Welfare

TL;DR

This paper develops a framework to analyze how information policies like ratings and recommendations impact societal welfare, using Bayesian persuasion to identify optimal policies and welfare improvements.

Contribution

It introduces the Bayes welfare set and characterizes its Pareto frontier, linking welfare analysis to Bayesian persuasion problems and providing conditions for welfare-enhancing information policies.

Findings

Pareto frontier of welfare feasible profiles characterized via Bayesian persuasion.

Conditions identified for existence of welfare-improving information policies.

Applications demonstrate implications for data leakage, price discrimination, and credit ratings.

Abstract

Information policies such as scores, ratings, and recommendations are increasingly shaping society's choices in high-stakes domains. We provide a framework to study the welfare implications of information policies on a population of heterogeneous individuals. We define and characterize the Bayes welfare set, consisting of the population's utility profiles that are feasible under some information policy. The Pareto frontier of this set can be recovered by a series of standard Bayesian persuasion problems, in which a utilitarian planner takes the role of the information designer. We provide necessary and sufficient conditions under which an information policy exists that Pareto dominates the no-information policy. We illustrate our results with applications to data leakage, price discrimination, and credit ratings.