title: Decision theoretic properties of forensic identification: underlying logic and argumentative implications.
abstract: The field of forensic science has profited from recent advances in the elicitation of various kinds probabilistic data. These provide the basis for implementing probabilistic inference procedures (e.g., in terms of likelihood ratios) that address the task of discriminating among competing target propositions. There is ongoing discussion, however, whether forensic identification, that is, a conclusion that associates a potential source (such as an individual or object) with a given item of scientific evidence (e.g., a biological stain or a tool mark), can, if ever, be based on purely probabilistic argument. With regard to this issue, the present paper proposes to analyze the process of forensic identification from a decision theoretic point of view. Existing probabilistic inference procedures are used therein as an integral part. The idea underlying the proposed analyses is that inference and decision are connected in the sense that the former is the point of departure for the latter. As such the approach forms a coordinated whole, that is a framework also known in the context as 'full Bayesian (decision) approach'. This study points out that, as a logical extension to purely probabilistic reasoning, a decision theoretic conceptualization of forensic identification allows the content and structure of arguments to be examined from a reasonably distinct perspective and common fallacious interpretations to be avoided.
authors.affiliation: The University of Lausanne, Ecole des Sciences Criminelles, Institut de Police Scientifique, le Batochime, 1015 Lausanne-Dorigny, Switzerland. alex.biedermann@unil.ch
authors.forename: A
authors.initials: A
authors.lastname: Biedermann
authors.forename: S
authors.initials: S
authors.lastname: Bozza
authors.forename: F
authors.initials: F
authors.lastname: Taroni
mesh_terms: D001499:Bayes Theorem; D003661:Decision Support Techniques; D018732:Forensic Anthropology / Q000379:methods*; D006801:Humans