Radiation-induced xerostomia in head and neck cancer patients

Langendijk, J.A. [Promotor]Slotman, B.J. [Promotor

The values of prediction in criminal cases

Like scientists, investigators and decision-makers in criminal cases both explain known evidence and use the resulting explanations to make novel predictions. Philosophers of science have made much of this distinction, arguing that hypotheses which lead to successful predictions are—all else being equal—epistemically superior to those that merely explain known data. Their ideas also offer important lessons for criminal evidence scholarship. This article distinguishes three values of prediction over explaining known facts in criminal cases. First, witnesses who predict are—all else being equal—more reliable than those who do not because they are less likely to be biased or lying. Second, investigators who only explain known facts run the risk of ‘fudging’ the scenarios that they formulate. Predictions can protect us against this danger. Third, carefully constructed predictions may help investigators to avoid confirmation bias. This article ends with a case study of the murder of Hae Min Lee

Case comment:responding to the implausible, incredible and highly improbable stories defendants tell: a Bayesian interpretation of the Venray murder ruling

In criminal trials, defendants often offer alternative explanations of the facts when they plead for their innocence. In its ruling on the Venray murder case, the Dutch Supreme Court dealt with the question when and how courts can reject such alternative explanations. According to the Supreme court, while courts should typically refer to evidence that refutes the explanation, they can also argue that the explanation 'did not become plausible' or that it is 'not credible'. Finally, courts can state that an explanation is so 'highly improbable' that it requires no response. However, the Supreme Court did not explain these terms, leading to confusion about how they ought to be interpreted. This case comment offers a Bayesian interpretation according to which these three terms relate to (i) the posterior probability of the explanation, (ii) the credibility of the defendant, and (iii) how obvious it is that the explanation is improbable. This interpretation clarifies the Supreme Courts ruling and ties it to the criminal law system's goals of error minimization and of producing understandable decisions efficiently

(Im)probable stories:combining Bayesian and explanation-based accounts of rational criminal proof

A key question in criminal trials is, ‘may we consider the facts of the case proven?’ Partially in response to miscarriages of justice, philosophers, psychologists and mathematicians have considered how we can answer this question rationally. The two most popular answers are the Bayesian and the explanation-based accounts. Bayesian models cast criminal evidence in terms of probabilities. Explanation-based approaches view the criminal justice process as a comparison between causal explanations of the evidence. Such explanations usually take the form of scenarios – stories about how a crime was committed. The two approaches are often seen as rivals. However, this thesis argues that both perspectives are necessary for a good theory of rational criminal proof. By comparing scenarios, we can, among other things, determine what the key evidence is, how the items of evidence interrelate, and what further evidence to collect. Bayesian probability theory helps us pinpoint when we can and cannot conclude that a scenario is likely to be true. This thesis considers several questions regarding criminal evidence from this combined perspective, such as: can a defendant sometimes be convicted on the basis of an implausible guilt scenario? When can we assume that we are not overlooking scenarios or evidence? Should judges always address implausible innocence scenarios of the accused? When is it necessary to look for new evidence? How do we judge whether an eyewitness is reliable? By combining the two theories, we arrive at new insights on how to rationally reason about these, and other questions surrounding criminal evidence

The reasonable doubt standard as inference to the best explanation

Explanationist accounts of rational legal proof view trials as a competition between explanations. Such accounts are often criticized for being underdeveloped. One question in need of further attention is when guilt is proven beyond a reasonable doubt (BARD) in criminal trials. This article defends an inference to the best explanation (IBE)-based approach on which guilt is only established BARD if (1) the best guilt explanation in a case is substantially more plausible than any innocence explanation, and (2) there is no good reason to presume that we have overlooked evidence or alternative explanations that could realistically have exonerated the defendant. This is a comparative account, which I argue is better suited for arriving at accurate verdicts than the non-comparative ‘no plausible alternative’ account that many explanationists tacitly assume. Furthermore, this account is not susceptible to the most important arguments against IBE in criminal trials or to arguments against other, non-explanationist interpretations of the BARD standard. I use a case study to illustrate how this account provides meaningful guidance for decision makers in criminal trials.<br/

Developing a euro area accounting matrix: issues and applications

Abstract: An important part of external or policy shocks is transmitted throughout the economy via various channels of transactions. To analyse such channels and to predict the impact of shocks, it is expedient to know who recently exchanged what with whom and for what purpose. The most appropriate format for presenting intersectoral linkages at the national level is in a National Accounting Matrix (NAM). A NAM is defined as the presentation of a sequence of integrated accounts and balancing items in a matrix that elaborates the linkages between a supply and use table and institutional sector accounts. This paper compiles the first pilot Euro Area Accounting Matrix (EAAM) and considers its usefulness for the euro area’s economic analysis. It also reports on the solution of a number of aggregation and consolidation issues that arise when constructing a multi-country accounting matrix. JEL Classification: E00, E19euro area, National Accounting Matrix, National Accounts