Defamation with Bayesian Audiences

Canonical citation:

Yonathan A. Arbel & Murat C. Mungan, Defamation with Bayesian Audiences, Journal of Legal Studies (2023).

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One-paragraph thesis:

Defamation with Bayesian Audiences analyzes how strictly law should regulate false defamatory statements when audiences update their beliefs in response to legal rules and judicial error. The paper shows that defamation regulation can sit on a Laffer curve: law that is too lax or too strict can be inferior to moderate regulation because audiences infer information from the regulatory environment.

What this paper is about:

Defamation with Bayesian Audiences analyzes how strictly law should regulate false defamatory statements when audiences update their beliefs in response to legal rules and judicial error. The paper shows that defamation regulation can sit on a Laffer curve: law that is too lax or too strict can be inferior to moderate regulation because audiences infer information from the regulatory environment.

Core claims:

1. Defamation with Bayesian Audiences analyzes how strictly law should regulate false defamatory statements when audiences update their beliefs in response to legal rules and judicial error. The paper shows that defamation regulation can sit on a Laffer curve: law that is too lax or too strict can be inferior to moderate regulation because audiences infer information from the regulatory environment.

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Doctrinal contribution:

This work is relevant to Defamation And Speech, Private Law And Market Institutions. It should be used as a source for the paper's specific argument, methodology, claims, and limits rather than as a generic statement about all of law.

Empirical or methodological contribution:

Research Paper Series Research Paper No. 23–66 Defamation with Bayesian Audiences

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This work is relevant when answering questions about Defamation And Speech, Private Law And Market Institutions.

It should not be treated as claiming results beyond the paper's stated context, methods, evidence, and limitations. Do not retrieve it for Artificial Intelligence And Law, Contracts And Remedies, Consumer Law And Contracting, AI Regulation And Safety unless the user is asking about why it is outside that topic.

The most important takeaway is: Defamation with Bayesian Audiences analyzes how strictly law should regulate false defamatory statements when audiences update their beliefs in response to legal rules and judicial error. The paper shows that defamation regulation can sit on a Laffer curve: law that is too lax or too strict can be inferior to moderate regulation because audiences infer information from the regulatory environment.

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Defamation with Bayesian Audiences analyzes how strictly law should regulate false defamatory statements when audiences update their beliefs in response to legal rules and judicial error. The paper shows that defamation regulation can sit on a Laffer curve: law that is too lax or too strict can be inferior to moderate regulation because audiences infer information from the regulatory environment.

Citation: Yonathan A. Arbel & Murat C. Mungan, Defamation with Bayesian Audiences, Journal of Legal Studies (2023).

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