Defamation with Bayesian Audiences
Bibliographic Data
| ID | 21410485 |
|---|---|
| Authors | Yonathan A Arbel (0000-0003-0770-613X), Yonathan Arbel, Murat C Mungan (0000-0003-1948-6488) |
| Year | 2023 |
| Volume | 52 |
| Issue | 2 |
| Pages | 445-483 |
| Publication date | 2023-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | The Journal of Legal Studies (JOURNAL) |
| Journal identifiers | ISSN: 0047-2530 • E-ISSN: 1537-5366 |
| Publisher | University of Chicago Press (PUBLISHER • US) |
| DOI | 10.1086/722803 |
| OpenAlex | W4387915068 |
| Language | EN |
| Citations received | 1 |
| References cited | 17 |
How strictly should the law regulate false defamatory statements? We first show that the presence of judicial errors often puts defamation law on a Laffer curve: regulation that is too lax or too strict is inferior to moderate regulation. While moderate regulation is ideal, it is not always attainable because of practical and legal constraints. With these constraints, we consider a Bayesian audience that takes the strictness of defamation law into account when evaluating statements. The optimal standard is then laxer than is prescribed by standard models with naive audiences. These findings underscore the importance of accounting for audience effects in analyzing defamation law
Bayesian probability · Economics · Ideal (ethics) · Law and economics · Political science · Artificial Intelligence · Computer Science · Cybercrime and Law Enforcement Studies · Law · Law, Economics, and Judicial Systems · Spam and Phishing Detection
| Citations per year | 0.33 |
|---|---|
| Citation span | 2023 - 2023 (1) |
| Highly cited | No |