How Bayesian Persuasion Can Help Reduce Illegal Parking and Other Socially Undesirable Behavior†

• 成果类型: Article
• 署名作者: Hernandez, Penelope; Neeman, Zvika
• 署名单位: University of Valencia; University of Valencia; Tel Aviv University
• 刊物名称: AMERICAN ECONOMIC JOURNAL-MICROECONOMICS
• ISSN/ISSBN: 1945-7669
• DOI: 10.1257/mic.20190295
• 发表日期: 2022
• 页码: 186-215
• 关键词: colonel-blotto; risk-aversion; ENFORCEMENT; crime

摘要

We consider the question of how best to allocate enforcement resources across different locations with the goal of deterring unwanted behavior. We rely on Bayesian persuasion to improve deterrence. We focus on the case where agents care only about the expected amount of enforcement resources given messages received. Optimization in the space of induced mean posterior beliefs involves a partial convexification of the objective function. We describe inter-pretable conditions under which it is possible to explicitly solve the problem with only two messages: high enforcement and enforce-ment as usual. We also provide a tight upper bound on the total number of messages needed to achieve the optimal solution in the general case as well as a general example that attains this bound.