Search Profiling with Partial Knowledge of Deterrence

Bibliographic Data

ID9708940
AuthorsCharles F Manski (0000-0001-7260-7686, Northwestern University, corresponding author)
Year2006
Volume116
Issue515
PagesF385-F401
Publication date2006-11-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueThe Economic Journal (JOURNAL)
Journal identifiersISSN: 0013-0133 • E-ISSN: 1468-0297
PublisherOxford University Press (OUP) (PUBLISHER)
DOI10.1111/j.1468-0297.2006.01128.x
OpenAlexW2075128985
LanguageEN
Citations received11
References cited14

Consider the choice of a profiling policy where decisions to search for evidence of crime may vary with observable covariates of the persons at risk of search. I pose a planning problem whose objective is to minimise the social cost of crime and search. The consequences of a search rule depend on the extent to which search deters crime. I study the planning problem when the planner has partial knowledge of deterrence. I show how the planner can eliminate dominated search rules and how he can use the minimax or minimax-regret criterion to choose an undominated rule. Copyright 2006 The Author(s). Journal compilation Royal Economic Society 2006

Deterrence (psychology · Economics · Law and economics · Machine learning · Mathematical economics · Microeconomics · Minimax · Planner · Pooling · Profiling (computer programming · Regret · Social planner · Artificial Intelligence · Auction Theory and Applications · Computer Science · Consumer Market Behavior and Pricing · Law, Economics, and Judicial Systems

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