A Bayesian Analysis on the (Dis)Utility of Iterative-Showup Procedures: The Moderating Impact of Prior Probabilities

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A showup is an identification procedure in which a lone suspect is presented to the eyewitness for an identification attempt. Showups are commonly used when law enforcement personnel locate a suspect near the scene of a crime in both time and space but lack probable cause to make an arrest. If an eyewitness rejects a suspect from a showup, law enforcement personnel might find another suspect and run another showup. Indeed, law enforcement personnel might go through several iterations of finding suspects and running showups with the same eyewitness. We label this phenomenon the iterative-showup procedure. The consequence of this procedure is that innocent suspect identifications increase disproportionately to culprit identifications. This happens because there is only one culprit, but a seemingly endless supply of innocent suspects. We apply Bayesian modeling to single- and iterative-showup procedures to demonstrate that iterative showups are almost always associated with lower probative value. We demonstrate that the prior probabilities that later suspects are the culprit are greatly constrained by the posterior probabilities that earlier suspects were the culprit. Identifications from iterative-showup procedures are of questionable reliability. We review alternative investigative strategies that police might consider in order to limit the use of iterative-showup procedures.

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