Are police officers Bayesians? Police updating in investigative stops
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Fagan, Jeffrey [1] ; Nojima, Lila J.E. [1]
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[1] Columbia University
Columbia University
Estados Unidos
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- Localización: The journal of criminal law and criminology, ISSN 0091-4169, Vol. 113, Nº. 3, 2023, págs. 593-652
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
Theories of rational behavior assume that actors make decisions where the benefits of their acts exceed their costs or losses. If those expected costs and benefits change over time, the behavior will change accordingly as actors learn and internalize the parameters of success and failure. In the context of proactive policing, police stops that achieve any of several goals— constitutional compliance, stops that lead to “good” arrests or summonses, stops that lead to seizures of weapons, drugs, or other contraband, or stops that produce good will and citizen cooperation—should signal to officers the features of a stop that increase its rewards or benefits. Having formed a subjective estimate of success (i.e., prior beliefs), officers should observe their outcomes in subsequent encounters and form updated probability estimates, with specific features of the event, with a positive weight on those features. Officers should also learn the features of unproductive stops and adjust accordingly. A rational actor would pursue “good” or “productive” stops and avoid “unproductive” stops by updating their knowledge of these features through experience.
