Dynamic Law Enforcement with Learning
AbstractThis article 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. Copyright 2004, Oxford University Press.
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Bibliographic InfoArticle provided by Oxford University Press in its journal The Journal of Law, Economics, and Organization.
Volume (Year): 20 (2004)
Issue (Month): 1 (April)
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Postal: Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK
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Web page: http://jleo.oupjournals.org/
Other versions of this item:
- K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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