1,721,126 research outputs found
The decisionalization of individualization
Get PDFThroughout forensic science and adjacent branches, academic researchers and practitioners continue to diverge in their perception and understanding of the notion of ‘individualization’, that is the claim to reduce a pool of potential donors of a forensic trace to a single source. In particular, recent shifts to refer to the practice of individualization as a decision have been revealed as being a mere change of label [1], leaving fundamental changes in thought and understanding still pending. What is more, professional associations and practitioners shy away from embracing the notion of decision in terms of the formal theory of decision in which individualization may be framed, mainly because of difficulties to deal with the measurement of desirability or undesirability of the consequences of decisions (e.g., using utility functions). Building on existing research in the area, this paper presents and discusses fundamental concepts of utilities and losses with particular reference to their application to forensic individualization. The paper emphasizes that a proper appreciation of decision tools not only reduces the number of individual assignments that the application of decision theory requires, but also shows how such assignments can be meaningfully related to constituting features of the real-world decision problem to which the theory is applied. It is argued that the decisonalization of individualization requires such fundamental insight to initiate changes in the fields’ underlying understandings, not merely in their label
Henri Busson, La Religion des Classiques (1660-1685) , Paris, Presses Universitaires de France, 1948
No full textBiedermann A. Henri Busson, La Religion des Classiques (1660-1685) , Paris, Presses Universitaires de France, 1948. In: Revue d'histoire et de philosophie religieuses, 28-29e année n°3,1948. pp. 255-257
Two items of evidence, no putative source: an inference problem in forensic intelligence
No full textIntelligence analysts commonly associate cases on the basis of similarities found in compared characteristics of scientific evidence. The present paper studies some of the inferential difficulties associated with such operations. An analysis is proposed that breaks down the reasoning process into inference to common source, and inference to case linkage. The former requires an approach to the difficulty associated with evaluating the similarities of items of evidence from different cases with no putative source being available. The latter requires consideration to be given to the relevance of evidence. Throughout the paper, probability theory is used to describe the nature of the proposed inferences. Graphical models are also introduced with the aim of providing further insight into the dependence and independence relationships assumed to hold among the various propositions considered. Notions from decision theory are used to discuss ways in which intelligence analysts may assist investigators in deciding whether or not cases should be considered as linked
Probabilistic evidential assessment of gunshot residue particle evidence (Part I): Likelihood ratio calculation and case pre-assessment using Bayesian networks
No full textWell developed experimental procedures currently exist for retrieving and analyzing particle evidence from hands of individuals suspected of being associated with the discharge of a firearm. Although analytical approaches (e.g. automated Scanning Electron Microscopy with Energy Dispersive X-ray (SEM-EDS) microanalysis) allow the determination of the presence of elements typically found in\ud
gunshot residue (GSR) particles, such analyses provide no information about a given particle’s actual source. Possible origins for which scientists may need to account for are a primary exposure to the\ud
discharge of a firearm or a secondary transfer due to a contaminated environment. In order to approach such sources of uncertainty in the context of evidential assessment, this paper studies the construction\ud
and practical implementation of graphical probability models (i.e. Bayesian networks). These can assist forensic scientists inmaking the issue tractable within a probabilistic perspective. The proposed models\ud
focus on likelihood ratio calculations at various levels of detail as well as case pre-assessment
Bayes Factors for Forensic Decision Analyses with R
Get PDFBayes Factors for Forensic Decision Analyses with R provides a self-contained introduction to computational Bayesian statistics using R. With its primary focus on Bayes factors supported by data sets, this book features an operational perspective, practical relevance, and applicability—keeping theoretical and philosophical justifications limited. It offers a balanced approach to three naturally interrelated topics:
– Probabilistic Inference: Relies on the core concept of Bayesian inferential statistics, to help practicing forensic scientists in the logical and balanced evaluation of the weight of evidence.
– Decision Making: Features how Bayes factors are interpreted in practical applications to help address questions of decision analysis involving the use of forensic science in the law.
– Operational Relevance: Combines inference and decision, backed up with practical examples and complete sample code in R, including sensitivity analyses and discussion on how to interpret results in context.
Over the past decades, probabilistic methods have established a firm position as a reference approach for the management of uncertainty in virtually all areas of science, including forensic science, with Bayes' theorem providing the fundamental logical tenet for assessing how new information—scientific evidence—ought to be weighed. Central to this approach is the Bayes factor, which clarifies the evidential meaning of new information, by providing a measure of the change in the odds in favor of a proposition of interest, when going from the prior to the posterior distribution. Bayes factors should guide the scientist's thinking about the value of scientific evidence and form the basis of logical and balanced reporting practices, thus representing essential foundations for rational decision making under uncertainty.
This book would be relevant to students, practitioners, and applied statisticians interested in inference and decision analyses in the critical field of forensic science. It could be used to support practical courses on Bayesian statistics and decision theory at both undergraduate and graduate levels, and will be of equal interest to forensic scientists and practitioners of Bayesian statistics for driving their evaluations and the use of R for their purposes
Decision theory
No full textForensic scientists, lawyers and other participants of the legal process are routinely faced with problems of making decisions under circumstances of uncertainty. Uncertainty relates to propositions of interest that are not completely known by the decision-maker at the time when a decision needs to be made. Propositions may relate to the source or nature of forensic traces, marks and objects. For example, with friction ridge marks, propositions of interest may be ‘Does this fingermark come from the person of interest (POI) or from some unknown person?’. In forensic document examination, a scientist may ask ‘Is this a genuine document or has it been modified (e.g., page substitution)?’. In forensic anthropology the question ‘Are these human remains?’ may arise, and so on. Replying in one way or another to such questions may be perceived as uncomfortable since knowledge about the relevant underlying truth-state of the world is incomplete to some extent. For example, in typical real-world applications of forensic science it is not known with certainty, when deciding to consider a POI as the source of a particular fingermark, whether the POI is in fact the source of the fingermark. Similarly, at an advanced stage of the legal process, the question of whether to convict or acquit a POI (i.e. the verdict) needs to be made in the presence of incomplete knowledge about whether or not the POI truly is the offender. There are analogies between the above questions, in terms of their logical underpinnings, that can be studied, analysed and described using formal methods, such as decision theory, which will be the main aim of this chapter.
Around the middle of the past century, discussions intensified and several fields of study emerged on decision-making concerning, for example, contexts where decisions have monetary consequences. These developments gravitated around questions such as how decisions should be made in order to be considered rational (Pratt et al., 1964). Though an important area, economics was not the only branch with strong interests in decision-making and decision analysis. Entire fields of study developed and interacted with each other in various ways, including psychology, mathematics and statistics, the law and philosophy of science, among others.
This chapter will primarily rely on statistical decision theory1 as developed by Leonard Savage (1954) and in subsequent treatises (e.g., Lindley, 1985; Luce and Raiffa, 1958; Raiffa, 1968) as the framework for studying the formal structure of decision problems arising in forensic science and the law. Before proceeding with this presentation, an important preliminary needs to be considered. It deals with the question of how to understand decision analysis and the notion of theory of decision. To this point, the field of judgment and deci- sion making, a branch of applied psychology, has contributed considerably by crystallizing three main perspectives and approaches, known as the descriptive, the normative and the prescriptive view (Baron, 2008; French et al., 2009). For a review of the history of these terms, see Baron (2006). Broadly speaking, the descriptive approach focuses on peoples’ observable decision behaviour and extends to the development of psychological theories in- tended to explain how individuals make decisions. Such research is valuable in that it allows one to better understand the conditions under which decision behaviour departs towards incoherence or, worse, logical error. However, revealing such departures requires reference points against which observable decision behaviour can be compared. The provision of such reference points, also called normative standards, is the object of study of the normative approach. Decision theory and decision criteria (or, norms) derived from it, fall into this category of study. It is mainly pursued by mathematicians, statisticians and philosophers of science. The third perspective, the prescriptive approach, addresses the question of what recommendations ought to be derived from normative insights in order to improve practical decision making. For example, some strict normativists, such as Lindley (1985), consider that the normative concept of probability – that is, a standard for reasoning under uncertainty – and decision theory as its extension, are also prescriptive in the sense that they provide direct prescriptions on how to arrange one’s reasoning and acting. Properly distinguishing the different intentions and goals of these kinds of decision science research is important for an informed discourse about notions of decision and decision analysis in forensic science applications (Biedermann et al., 2014).
This chapter is structured as follows. Section 5.2 outlines standard elements of statistical decision theory that will be exemplified in Section 5.3 for decision problems arising in the law in general (Section 5.3.1) and forensic science in particular (Section 5.3.2). This exposition will include examples such as decisions following forensic inference of source (i.e., identification/individualization; Section 5.3.2.1). Discussion and conclusions will be presented in Section 5.4. Further readings on applications of decision theory in forensic science and treatments of decision theory in general are given in Section 5.5
Implementing statistical learning methods through Bayesian networks. Part 1: A guide to Bayesian parameter estimation using forensic science data
No full textAs a thorough aggregation of probability and graph theory, Bayesian networks currently enjoy widespread interest as a means for studying factors that affect the coherent evaluation of scientific evidence in forensic science. Paper I of this series of papers intends to contribute to the discussion of Bayesian networks as a framework that is helpful for both illustrating and implementing statistical procedures that are commonly employed for the study of uncertainties (e.g. the estimation of unknown quantities). While the respective statistical procedures are widely described in literature, the primary aim of this paper is to offer an essentially non-technical introduction on how interested readers may use these analytical approaches – with the help of Bayesian networks – for processing their own forensic science data. Attention is mainly drawn to the structure and underlying rationale of a series of basic and context-independent network fragments that users may incorporate as building blocs while constructing larger inference models. As an example of how this may be done, the proposed concepts will be used in a second paper (Part II) for specifying graphical probability networks whose purpose is to
assist forensic scientists in the evaluation of scientific evidence encountered in the context of forensic document examination (i.e. results of the analysis of black toners present on printed or copied
documents)
Probabilistic evidential assessment of gunshot residue particle evidence (Part II): Bayesian parameter estimation for experimental count data
No full textPart I of this series of articles focused on the construction of graphical probabilistic inference procedures, at various levels of detail, for assessing the evidential value of gunshot residue (GSR) particle evidence. The proposed models – in the form of Bayesian networks – address the issues of background presence of
GSR particles, analytical performance (i.e., the efficiency of evidence searching and analysis procedures) and contamination. The use and practical implementation of Bayesian networks for case pre-assessment is also discussed. This paper, Part II, concentrates on Bayesian parameter estimation. This topic
complements Part I in that it offers means for producing estimates useable for the numerical specification of the proposed probabilistic graphicalmodels. Bayesian estimation procedures are given a primary focus of attention because they allow the scientist to combine (his/her) prior knowledge about the problem of interest with newly acquired experimental data. The present paper also considers further topics such as the sensitivity of the likelihood ratio due to uncertainty in parameters and the study of likelihood ratio values obtained for members of particular populations (e.g., individuals with or without exposure to GSR)
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