Decision Errors, Legal Uncertainty and Welfare: a General Treatment

This paper provides a general treatment of the implications for welfare of legal uncertainty. We distinguish legal uncertainty from decision errors: though the former can be influenced by the latter, the latter are neither necessary nor sufficient for the existence of legal uncertainty. We show that an increase in decision errors will always reduce welfare. However, for any given level of decision errors, information structures involving more legal uncertainty can improve welfare. This holds always, even when there is complete legal uncertainty, when sanctions on socially harmful actions are set at their optimal level. This transforms radically one’s perception about the “costs” of legal uncertainty. We also provide general proofs for two results, previously established under restrictive assumptions. The first is that Effects-Based enforcement procedures may welfare dominate Per Se (or object-based) procedures and will always do so when sanctions are optimally set. The second is that optimal sanctions may well be higher under enforcement procedures involving more legal uncertainty.

How family caregivers' medical and moral assumptions influence decision making for patients in the vegetative state: a qualitative interview study.

Background Decisions on limiting life-sustaining treatment for patients in the vegetative state (VS) are emotionally and morally challenging. In Germany, doctors have to discuss, together with the legal surrogate (often a family member), whether the proposed treatment is in accordance with the patient's will. However, it is unknown whether family members of the patient in the VS actually base their decisions on the patient's wishes. Objective To examine the role of advance directives, orally expressed wishes, or the presumed will of patients in a VS for family caregivers' decisions on life-sustaining treatment. Methods and sample A qualitative interview study with 14 next of kin of patients in a VS in a long-term care setting was conducted; 13 participants were the patient's legal surrogates. Interviews were analysed according to qualitative content analysis. Results The majority of family caregivers said that they were aware of aforementioned wishes of the patient that could be applied to the VS condition, but did not base their decisions primarily on these wishes. They gave three reasons for this: (a) the expectation of clinical improvement, (b) the caregivers' definition of life-sustaining treatments and (c) the moral obligation not to harm the patient. If the patient's wishes were not known or not revealed, the caregivers interpreted a will to live into the patient's survival and non-verbal behaviour. Conclusions Whether or not prior treatment wishes of patients in a VS are respected depends on their applicability, and also on the medical assumptions and moral attitudes of the surrogates. We recommend repeated communication, support for the caregivers and advance care planning.

A higher mutational burden in females supports a "female protective model" in neurodevelopmental disorders.

Increased male prevalence has been repeatedly reported in several neurodevelopmental disorders (NDs), leading to the concept of a "female protective model." We investigated the molecular basis of this sex-based difference in liability and demonstrated an excess of deleterious autosomal copy-number variants (CNVs) in females compared to males (odds ratio [OR] = 1.46, p = 8 × 10(-10)) in a cohort of 15,585 probands ascertained for NDs. In an independent autism spectrum disorder (ASD) cohort of 762 families, we found a 3-fold increase in deleterious autosomal CNVs (p = 7 × 10(-4)) and an excess of private deleterious single-nucleotide variants (SNVs) in female compared to male probands (OR = 1.34, p = 0.03). We also showed that the deleteriousness of autosomal SNVs was significantly higher in female probands (p = 0.0006). A similar bias was observed in parents of probands ascertained for NDs. Deleterious CNVs (>400 kb) were maternally inherited more often (up to 64%, p = 10(-15)) than small CNVs < 400 kb (OR = 1.45, p = 0.0003). In the ASD cohort, increased maternal transmission was also observed for deleterious CNVs and SNVs. Although ASD females showed higher mutational burden and lower cognition, the excess mutational burden remained, even after adjustment for those cognitive differences. These results strongly suggest that females have an increased etiological burden unlinked to rare deleterious variants on the X chromosome. Carefully phenotyped and genotyped cohorts will be required for identifying the symptoms, which show gender-specific liability to mutational burden.

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.

Data Envelopment Analysis (DEA) : A pedagogical guide for decision makers in the public sector

This guide introduces Data Envelopment Analysis (DEA), a performance measurement technique, in such a way as to be appropriate to decision makers with little or no background in economics and operational research. The use of mathematics is kept to a minimum. This guide therefore adopts a strong practical approach in order to allow decision makers to conduct their own efficiency analysis and to easily interpret results. DEA helps decision makers for the following reasons: - By calculating an efficiency score, it indicates if a firm is efficient or has capacity for improvement. - By setting target values for input and output, it calculates how much input must be decreased or output increased in order to become efficient. - By identifying the nature of returns to scale, it indicates if a firm has to decrease or increase its scale (or size) in order to minimize the average cost. - By identifying a set of benchmarks, it specifies which other firms' processes need to be analysed in order to improve its own practices.

The SOCS3-independent expression of IDO2 supports the homeostatic generation of T regulatory cells by human dendritic cells.

Dendritic cells (DCs) are professional APCs that have a role in the initiation of adaptive immune responses and tolerance. Among the tolerogenic mechanisms, the expression of the enzyme IDO1 represents an effective tool to generate T regulatory cells. In humans, different DC subsets express IDO1, but less is known about the IDO1-related enzyme IDO2. In this study, we found a different pattern of expression and regulation between IDO1 and IDO2 in human circulating DCs. At the protein level, IDO1 is expressed only in circulating myeloid DCs (mDCs) and is modulated by PGE2, whereas IDO2 is expressed in both mDCs and plasmacytoid DCs and is not modulated by PGE2. In healthy subjects, IDO1 expression requires the presence of PGE2 and needs continuous transcription and translation, whereas IDO2 expression is constitutive, independent from suppressor of cytokine signaling 3 activity. Conversely, in patients suffering from inflammatory arthritis, circulating DCs express both IDO1 and IDO2. At the functional level, both mDCs and plasmacytoid DCs generate T regulatory cells through an IDO1/IDO2-dependent mechanism. We conclude that, in humans, whereas IDO1 provides an additional mechanism of tolerance induced by proinflammatory mediators, IDO2 is stably expressed in steady-state conditions and may contribute to the homeostatic tolerogenic capacity of DCs.

Effects of anxiety on visuospatial pattern discrimination and decision-making processes: what can we learn from brain dynamics ?

Detection and discrimination of visuospatial input involve at least extracting, selecting and encoding relevant information and decision-making processes allowing selecting a response. These two operations are altered, respectively, by attentional mechanisms that change discrimination capacities, and by beliefs concerning the likelihood of uncertain events. Information processing is tuned by the attentional level that acts like a filter on perception, while decision-making processes are weighed by subjective probability of risk. In addition, it has been shown that anxiety could affect the detection of unexpected events through the modification of the level of arousal. Consequently, purpose of this study concerns whether and how decision-making and brain dynamics are affected by anxiety. To investigate these questions, the performance of women with either a high (12) or a low (12) STAI-T (State-Trait Anxiety Inventory, Spielberger, 1983) was examined in a decision-making visuospatial task where subjects have to recognize a target visual pattern from non-target patterns. The target pattern was a schematic image of furniture arranged in such a way as to give the impression of a living room. Non-target patterns were created by either the compression or the dilatation of the distances between objects. Target and non-target patterns were always presented in the same configuration. Preliminary behavioral results show no group difference in reaction time. In addition, visuo-spatial abilities were analyzed trough the signal detection theory for quantifying perceptual decisions in the presence of uncertainty (Green and Swets, 1966). This theory treats detection of a stimulus as a decision-making process determined by the nature of the stimulus and cognitive factors. Astonishingly, no difference in d' (corresponding to the distance between means of the distributions) and c (corresponds to the likelihood ratio) indexes was observed. Comparison of Event-related potentials (ERP) reveals that brain dynamics differ according to anxiety. It shows differences in component latencies, particularly a delay in anxious subjects over posterior electrode sites. However, these differences are compensated during later components by shorter latencies in anxious subjects compared to non-anxious one. These inverted effects seem indicate that the absence of difference in reaction time rely on a compensation of attentional level that tunes cortical activation in anxious subjects, but they have to hammer away to maintain performance.

Decision analysis for the genotype designation in low-template-DNA profiles

What genotype should the scientist specify for conducting a database search to try to find the source of a low-template-DNA (lt-DNA) trace? When the scientist answers this question, he or she makes a decision. Here, we approach this decision problem from a normative point of view by defining a decision-theoretic framework for answering this question for one locus. This framework combines the probability distribution describing the uncertainty over the trace's donor's possible genotypes with a loss function describing the scientist's preferences concerning false exclusions and false inclusions that may result from the database search. According to this approach, the scientist should choose the genotype designation that minimizes the expected loss. To illustrate the results produced by this approach, we apply it to two hypothetical cases: (1) the case of observing one peak for allele xi on a single electropherogram, and (2) the case of observing one peak for allele xi on one replicate, and a pair of peaks for alleles xi and xj, i ≠ j, on a second replicate. Given that the probabilities of allele drop-out are defined as functions of the observed peak heights, the threshold values marking the turning points when the scientist should switch from one designation to another are derived in terms of the observed peak heights. For each case, sensitivity analyses show the impact of the model's parameters on these threshold values. The results support the conclusion that the procedure should not focus on a single threshold value for making this decision for all alleles, all loci and in all laboratories.

A drug administration decision support system

Drug delivery is one of the most common clinical routines in hospitals, and is critical to patients' health and recovery. It includes a decision making process in which a medical doctor decides the amount (dose) and frequency (dose interval) on the basis of a set of available patients' feature data and the doctor's clinical experience (a priori adaptation). This process can be computerized in order to make the prescription procedure in a fast, objective, inexpensive, non-invasive and accurate way. This paper proposes a Drug Administration Decision Support System (DADSS) to help clinicians/patients with the initial dose computing. The system is based on a Support Vector Machine (SVM) algorithm for estimation of the potential drug concentration in the blood of a patient, from which a best combination of dose and dose interval is selected at the level of a DSS. The addition of the RANdom SAmple Consensus (RANSAC) technique enhances the prediction accuracy by selecting inliers for SVM modeling. Experiments are performed for the drug imatinib case study which shows more than 40% improvement in the prediction accuracy compared with previous works. An important extension to the patient features' data is also proposed in this paper.

Impact of voting advice applications on voters's decision-making

So-called online Voting Advice Applications (VAAs) have become very popular all over Europe. Millions of voters are using them as an assistance to make up their minds for which party they should vote. Despite this popularity there are only very few studies about the impact of these tools on individual electoral choice. On the basis of the Swiss VAA smartvote we present some first findings about the question whether VAAs do have a direct impact on the actual vote of their users. In deed, we find strong evidence that Swiss voters were affected by smartvote. However, our findings are somewhat contrary to the results of previous studies from other countries. Furthermore, the quality of available data for such studies needs to be improved. Future studies should pay attention to both: the improvement of the available data, as well as the explanation of the large variance of findings between the specific European countries.

A decision-making Fokker-Planck model in computational neuroscience

Minimal models for the explanation of decision-making in computational neuroscience are based on the analysis of the evolution for the average firing rates of two interacting neuron populations. While these models typically lead to multi-stable scenario for the basic derived dynamical systems, noise is an important feature of the model taking into account finite-size effects and robustness of the decisions. These stochastic dynamical systems can be analyzed by studying carefully their associated Fokker-Planck partial differential equation. In particular, we discuss the existence, positivity and uniqueness for the solution of the stationary equation, as well as for the time evolving problem. Moreover, we prove convergence of the solution to the the stationary state representing the probability distribution of finding the neuron families in each of the decision states characterized by their average firing rates. Finally, we propose a numerical scheme allowing for simulations performed on the Fokker-Planck equation which are in agreement with those obtained recently by a moment method applied to the stochastic differential system. Our approach leads to a more detailed analytical and numerical study of this decision-making model in computational neuroscience.

Decision making in a human population living sustainably

The Tiwi people of northern Australia have managed natural resources continuously for 6000-8000 years. Tiwi management objectives and outcomes may reflect how they gather information about the environment. We qualitatively analyzed Tiwi documents and management techniques to examine the relation between the social and physical environment of decision makers and their decision-making strategies. We hypothesized that principles of bounded rationality, namely, the use of efficient rules to navigate complex decision problems, explain how Tiwi managers use simple decision strategies (i.e., heuristics) to make robust decisions. Tiwi natural resource managers reduced complexity in decision making through a process that gathers incomplete and uncertain information to quickly guide decisions toward effective outcomes. They used management feedback to validate decisions through an information loop that resulted in long-term sustainability of environmental use. We examined the Tiwi decision-making processes relative to management of barramundi (Lates calcarifer) fisheries and contrasted their management with the state government's management of barramundi. Decisions that enhanced the status of individual people and their attainment of aspiration levels resulted in reliable resource availability for Tiwi consumers. Different decision processes adopted by the state for management of barramundi may not secure similarly sustainable outcomes.

Public particiation and the legitimacy of intergovernmental decision-making : a focus on the World Trade Organization

The legitimacy of the WTO's decision-making process has always been questioned, and many have advocated public participation mechanisms as a remedy. The present study considers the limits and potential of these mechanisms by advancing a conceptual framework, which distinguishes the four 'implementation parameters' of public participation: the goal, the object, the modalities, and the actors. It addresses the issue of legitimacy by considering to what extent, and by virtue of which legal developments, one can see implementing the democratic principle as a goal for public participation in the context of the WTO. By analyzing the institutional structure of the WTO and its different types of decisions, it then outlines how this goal should influence the object and modalities of public participation, which decision-making procedures should be opened to public participation, and how the mechanisms should be implemented in practice. Finally, it suggests speciflrc amendments to existing WTO affangements on public participation