Economists studying public policy have generally assumed that the relevant social planner knows
how policy affects population behavior. Planners typically do not possess all of this knowledge, so
there is reason to consider policy formation with partial knowledge of policy impacts. Here I
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 being searched. To begin I pose a planning
problem whose objective is to minimize the utilitarian social cost of crime and search. The
consequences of candidate search rules depends on the extent to which search deters crime.
Deterrence is expressed through the offense function, which describes how the offense rate of
persons with given covariates varies with the search rate applied to these persons. I study the
planning problem when the planner has partial knowledge of the offense function. To demonstrate
general ideas, I suppose that the planner observes the offense rates of a study population whose
search rule has previously been chosen. He knows that the offense rate weakly decreases as the
search rate increases, but he does not know the magnitude of the deterrent effect of search. In this
setting, I first show how the planner can eliminate dominated search rules and then how he can use
the minimax or minimax-regret criterion to choose an undominated search rule.
*Published:
Manski, Charles F. "Optimal Search Profiling With Linear Deterrence," American Economic Review, 2005, v95(2,May), 12-126.
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