How Bayesian Persuasion Can Help Reduce Illegal Parking and Other Socially Undesirable Behavior
研究如何通过贝叶斯说服优化执法资源分配，以威慑非法停车等不良行为，发现仅需“高执法”和“常规执法”两条信息即可在特定条件下实现最优威慑。
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 interpretable conditions under which it is possible to explicitly solve the problem with only two messages: “high enforcement” and “enforcement 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.