Censorship as optimal persuasion

Abstract

We consider a Bayesian persuasion problem where a sender's utility depends only on the expected state. We show that upper censorship that pools the states above a cutoff and reveals the states below the cutoff is optimal for all prior distributions of the state if and only if the sender's marginal utility is quasi‐concave. Moreover, we show that it is optimal to reveal less information if the sender becomes more risk averse or the sender's utility shifts to the left. Finally, we apply our results to the problem of media censorship by a government.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jan 01, 2022
Source ID
10.3982/te4071

Entities

People

  • Andriy Zapechelnyuk
  • Anton Kolotilin
  • Timofiy Mylovanov

Organizations

  • Australian Research Council
  • Economic and Social Research Council
  • Massachusetts Institute of Technology
  • Office of Naval Research
  • University of New South Wales
  • University of Pittsburgh
  • University of St Andrews

Tags

Fields of Study

  • Economics

Readers

  • Operations Research
  • Organizational Psychology.
  • Strategic Security Studies

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms