 Subjects -> STATISTICS (Total: 130 journals)
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- Bayesian Sequential Experimental Design for Planning Series of Police
Lineups-
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First page: mgae017
Abstract: AbstractTo differentiate between guilty and innocent suspects during a criminal case, investigators often query eyewitness memory with a series of police lineups. Most current research on lineup efficacy, however, focuses almost exclusively on individual lineups—perhaps because the literature lacks analytic tools for the multi-lineup setting. In this article, we develop the first general formalism for evaluating the configurations of series of police lineups, thereby equipping the lineup research community to more fully understand these important cases. To accomplish this, we ground the problem of configuring police lineups in the theory of Bayesian sequential experimental design. Using both synthetic data and publicly available data from human-subjects studies, we find that a well-configured series can yield information greater than the sum of its parts. Evidentiary value increases when lineups are configured in light of what has been learned from past lineups and what might be learned from future lineups. Strikingly, some naive approaches reduce the information gained about the suspect’s guilt or innocence to a degree comparable to losing an entire witness in a multi-witness investigation.
PubDate: Mon, 03 Feb 2025 00:00:00 GMT
DOI: 10.1093/lpr/mgae017
Issue No: Vol. 24, No. 1 (2025)
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