Dynamic optimal law enforcement with learning
AbstractWe 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.
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Bibliographic InfoPaper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 402.
Date of creation: Jun 1999
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Fine; probability of detection and punishment; learning;
Find related papers by JEL classification:
- K4 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior
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