Dynamic Optimal Law Enforcement with Learning

Universitat Pompeu Fabra Working Paper No. 402

12 Pages Posted: 2 Feb 2000

See all articles by Mohamed Jellal

Mohamed Jellal

Nuno Garoupa

George Mason University - Antonin Scalia Law School, Faculty

Date Written: Undated

Abstract

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

Jellal, Mohamed and Garoupa, Nuno, Dynamic Optimal Law Enforcement with Learning (Undated). Universitat Pompeu Fabra Working Paper No. 402. Available at SSRN: https://ssrn.com/abstract=199056 or http://dx.doi.org/10.2139/ssrn.199056

Nuno Garoupa (Contact Author)

George Mason University - Antonin Scalia Law School, Faculty ( email )

3301 Fairfax Drive
Arlington, VA 22201
United States

No contact information is available for Mohamed Jellal

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