CENSORSHIP AS OPTIMAL PERSUASION Anton Kolotilin, Tymofiy Mylovanov, Andriy Zapechelnyuk 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. JEL Classification: D82, D83, L82 Keywords: Bayesian persuasion, information design, censorship, media Date: May 30, 2021. Kolotilin: UNSW Business School, School of Economics, UNSW Sydney, NSW 2052, Australia. E-mail:
[email protected]. Mylovanov: University of Pittsburgh, Department of Economics, 4714 Posvar Hall, 230 South Bou- quet Street, Pittsburgh, PA 15260, USA. E-mail:
[email protected]. Zapechelnyuk: University of St Andrews, School of Economics and Finance, Castlecliffe, the Scores, St Andrews KY16 9AR, UK. E-mail:
[email protected]. We are grateful for discussions with Ming Li with whom we worked on the companion paper Kolotilin et al. (2017). An early version of the results in this paper and the results in the companion paper were presented in our joint working paper Kolotilin et al. (2015). We thank the anonymous refer- ees, Ricardo Alonso, Dirk Bergemann, Simon Board, Patrick Bolton, Alessandro Bonatti, Steven Callander, Odilon C^amara,Rahul Deb, P´eterEs¨o,Florian Hoffman, Johannes H¨orner,Roman In- derst, Emir Kamenica, Navin Kartik, Daniel Kr¨aehmer,Hongyi Li, Marco Ottaviani, Mallesh Pai, Andrea Prat, Larry Samuelson, Ilya Segal, Joel Sobel, Konstantin Sonin, Christopher Teh, as well as many conference and seminar participants, for helpful comments.