Working Paper: CEPR ID: DP18104
Authors: Laura Doval; Alex Smolin
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 agents. 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 extend our results to the case in which information policies are restricted in the data they can use and show that ``blinding" algorithms to sensitive inputs is welfare decreasing. We illustrate our results with applications to privacy, recommender systems, and credit ratings.
Keywords: Bayesian persuasion; Information design; Welfare economics; Algorithms; Information policies
JEL Codes: No JEL codes provided
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| information policies (L86) | welfare outcomes (I38) |
| Bayes welfare set (D69) | welfare profiles (I38) |
| effective information policies (D83) | improved welfare outcomes (I38) |
| blinding of sensitive inputs (C90) | decreased welfare (I30) |
| information structure (L15) | welfare outcomes (I38) |