Optimal information censorship

Ginzburg, Boris

doi:10.1016/j.jebo.2019.05.016

Cited by 13 publications

(5 citation statements)

References 11 publications

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“…There is a diverse literature where censorship policies emerge as optimal signals in specific instances of the linear persuasion problem, starting from the prosecutor–judge example, as well as lobbying and product advertising examples, in Kamenica and Gentzkow (2011). Other contexts where censorship is optimal include grading policies (Ostrovsky and Schwarz (2010)), media control (Gehlbach and Sonin (2014), Ginzburg (2019), Gitmez and Molavi (2020)), clinical trials (Kolotilin (2015)), voter persuasion (Alonso and Câmara (2016a,b)), transparency benchmarks (Duffie, Dworczak, and Zhu (2017)), stress tests (Goldstein and Leitner (2018), Orlov, Zryumov, and Skrzypach (2021)), online markets (Romanyuk and Smolin (2019)), attention management (Lipnowski, Mathevet, and Wei (2020), Bloedel and Segal (2021)), quality certification (Zapechelnyuk (2020)), and relational communication (Kolotilin and Li (2021)).…”

Section: Introductionmentioning

confidence: 99%

Censorship as optimal persuasion

Kolotilin

Mylovanov

Zapechelnyuk

2022

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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.

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Section: Introductionmentioning

confidence: 99%

Censorship as optimal persuasion

Kolotilin

Mylovanov

Zapechelnyuk

2022

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“…There are also a few works that combine mechanism design and information design (See [7,20,31,44]). In these papers, the principal chooses both the game form and the information structure so as to persuade the agent to follow the recommendations.…”

Section: Dynamic Information Design Problemsmentioning

confidence: 99%

Dynamic Information Design: A Simple Problem on Optimal Sequential Information Disclosure

Farhadi

Teneketzis

2021

Dyn Games Appl

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We study a dynamic information design problem in a finite-horizon setting consisting of two strategic and long-term optimizing agents, namely a principal (he) and a detector (she). The principal observes the evolution of a Markov chain that has two states, one “good” and one “bad” absorbing state, and has to decide how to sequentially disclose information to the detector. The detector’s only information consists of the messages she receives from the principal. The detector’s objective is to detect as accurately as possible the time of the jump from the good to the bad state. The principal’s objective is to delay the detector as much as possible from detecting the jump to the bad state. For this setting, we determine the optimal strategies of the principal and the detector. The detector’s optimal strategy is described by time-varying thresholds on her posterior belief of the good state. We prove that it is optimal for the principal to give no information to the detector before a time threshold, run a mixed strategy to confuse the detector at the threshold time, and reveal the true state afterward. We present an algorithm that determines both the optimal time threshold and the optimal mixed strategy that could be employed by the principal. We show, through numerical experiments, that this optimal sequential mechanism outperforms any other information disclosure strategy presented in the literature. We also show that our results can be extended to the infinite-horizon problem, to the problem where the matrix of transition probabilities of the Markov chain is time-varying, and to the case where the Markov chain has more than two states and one of the states is absorbing.

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“…We instead focus on elections under simple majority rule and assume voting to be voluntary and costly. Kolotilin et al (2017) and Ginzburg (2019) consider persuasion problems in an environment similar to ours. We use their insights in deriving the media outlet's equilibrium behavior.…”

Section: Related Literaturementioning

confidence: 99%

A Theory on Media Bias and Elections

2019

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We develop a tractable theory to study the impact of biased media on election outcomes, voter turnout and welfare. News released by media allows voters to infer the relative appeal of the two candidates, and the closeness of elections. In large elections, the former determines the election outcome, whereas the latter drives voter turnout. With a single media outlet, a rise in media bias affects the election outcome in a non-monotonic way, and reduces voter welfare by decreasing the probability of electing the efficient candidate and increasing aggregate turnout costs. Introducing extra media outlets can systematically shift the election outcome and voter turnout in either direction, but it weakly improves voter welfare. The impact of other ways to strengthen media competition-such as increased polarization and prevention of collusion-critically depends on whether media have commitment power; if not, they can worsen information transmission and voter welfare.

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Optimal information censorship

Cited by 13 publications

References 11 publications

Censorship as optimal persuasion

Censorship as optimal persuasion

Dynamic Information Design: A Simple Problem on Optimal Sequential Information Disclosure

A Theory on Media Bias and Elections

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