Liberties and constraints of the normative approach to evaluation and decision in forensic science: a discussion towards overcoming some common misconceptions

At a time when disciplined inference and decision making under uncertainty represent common aims to participants in legal proceedings, the scientific community is remarkably heterogenous in its attitudes as to how these goals ought to be achieved. Probability and decision theory exert a considerable influence, and we think by all reason rightly do so, but they go against a mainstream of thinking that does not embrace-or is not aware of-the 'normative' character of this body of theory. It is normative, in the sense understood in this article, in that it prescribes particular properties, typically (logical) coherence, to which reasoning and decision making ought to conform. Disregarding these properties can result in diverging views which are occasionally used as an argument against the theory, or as a pretext for not following it. Typical examples are objections according to which people, both in everyday life but also individuals involved at various levels in the judicial process, find the theory difficult to understand and to apply. A further objection is that the theory does not reflect how people actually behave. This article aims to point out in what sense these examples misinterpret the analytical framework in its normative perspective. Through examples borrowed mostly from forensic science contexts, it is argued that so-called intuitive scientific attitudes are particularly liable to such misconceptions. These attitudes are contrasted with a statement of the actual liberties and constraints of probability and decision theory and the view according to which this theory is normative.

The origins of C4 grasslands: integrating evolutionary and ecosystem science.

The evolution of grasses using C4 photosynthesis and their sudden rise to ecological dominance 3 to 8 million years ago is among the most dramatic examples of biome assembly in the geological record. A growing body of work suggests that the patterns and drivers of C4 grassland expansion were considerably more complex than originally assumed. Previous research has benefited substantially from dialog between geologists and ecologists, but current research must now integrate fully with phylogenetics. A synthesis of grass evolutionary biology with grassland ecosystem science will further our knowledge of the evolution of traits that promote dominance in grassland systems and will provide a new context in which to evaluate the relative importance of C4 photosynthesis in transforming ecosystems across large regions of Earth.

New perspectives in the use of ink evidence in forensic science: Part I. Development of a quality assurance process for forensic ink analysis by HPTLC

The level of information provided by ink evidence to the criminal and civil justice system is limited. The limitations arise from the weakness of the interpretative framework currently used, as proposed in the ASTM 1422-05 and 1789-04 on ink analysis. It is proposed to use the likelihood ratio from the Bayes theorem to interpret ink evidence. Unfortunately, when considering the analytical practices, as defined in the ASTM standards on ink analysis, it appears that current ink analytical practices do not allow for the level of reproducibility and accuracy required by a probabilistic framework. Such framework relies on the evaluation of the statistics of the ink characteristics using an ink reference database and the objective measurement of similarities between ink samples. A complete research programme was designed to (a) develop a standard methodology for analysing ink samples in a more reproducible way, (b) comparing automatically and objectively ink samples and (c) evaluate the proposed methodology in a forensic context. This report focuses on the first of the three stages. A calibration process, based on a standard dye ladder, is proposed to improve the reproducibility of ink analysis by HPTLC, when these inks are analysed at different times and/or by different examiners. The impact of this process on the variability between the repetitive analyses of ink samples in various conditions is studied. The results show significant improvements in the reproducibility of ink analysis compared to traditional calibration methods.

Modified 'dumbbell' technique: a simple and intuitive method to position balloon-expandable stent valves.

Intraoperative cardiac imaging plays a key role during transcatheter aortic valve replacement. In recent years, new techniques and new tools for improved image quality and virtual navigation have been proposed, in order to simplify and standardize stent valve positioning and implantation. But routine performance of the new techniques may require major economic investments or specific knowledge and skills and, for this reason, they may not be accessible to the majority of cardiac centres involved in transcatheter valve replacement projects. Additionally, they still require injections of contrast medium to obtain computed images. Therefore, we have developed and describe here a very simple and intuitive method of positioning balloon-expandable stent valves, which represents the evolution of the 'dumbbell' technique for echocardiography-guided transcatheter valve replacement without angiography. This method, based on the partial inflation of the balloon catheter during positioning, traps the crimped valve in the aortic valve orifice and, consequently, very near to the ideal landing zone. It does not require specific echocardiographic knowledge; it does not require angiographies that increase the risk of postoperative kidney failure in elderly patients, and it can be also performed in centres not equipped with a hybrid operating room.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.