Dynamic Law Enforcement with Learning
研究了执法机构通过经验积累降低边际成本的学习效应，发现最优罚款可能低于最高额、最优侦破概率和监禁刑期可能高于传统模型。
This paper modifies a standard model of law enforcement to allow for learning by doing.We incorporate the process of enforcement learning by assuming that the agency's current marginal cost is a decreasing function of its past experience of detecting and convicting.The agency accumulates data and information (on criminals, on opportunities of crime) enhancing the ability of future apprehension at a lower marginal cost.We focus on the impact of enforcement learning on optimal compliance rules.In particular, we show that the optimal fine could be less than maximal and the optimal probability of detection could be higher than otherwise.It is also suggested that the optimal imprisonment sentence could be higher than otherwise.