Dynamic Optimal Law Enforcement with Learning
Universitat Pompeu Fabra Working Paper No. 402
12 Pages Posted: 2 Feb 2000
Date Written: Undated
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 to apprehend in the future at a lower marginal cost. We focus on the impact of enforcement learning on optimal stationary compliance rules. In particular, we show that the optimal stationary fine could be less-than-maximal and the optimal stationary probability of detection could be higher-than-otherwise.
JEL Classification: K4
Suggested Citation: Suggested Citation