Our decisions reflect uncertainty in various ways. We take account of the uncertainty embodied in the roll of the die; we less often take account of the uncertainty of our belief that the die is fair. We need to take account of both uncertain knowledge and our knowledge of uncertainty. Evidence itself has been regarded as uncertain. We argue that pointvalued probabilities are a poor representation of uncertainty; that we need not be concerned with uncertain evidence; that interval-valued probabilities that result from knowledge of convex sets of distribution functions in reference classes (properly) include Shafer's mass functions as a special case; that these probabilities yield a plausible non-monotonic form of inference (uncertain inference, inductive inference, statistical inference); and finally that this framework provides a very nearly classical decision theory -- so far as it goes. It is unclear how global the principles (such as minimax) that go beyond the principle of maximizing expected utility are. Artificial Intelligence, Data Fusion, evidence, uncertainty, decision non-monotonicity, knowledge representation, expert systems.

Policy knowledge derived from data, information, and evidence is a powerful tool for contributing to policy discussions and debates, and for understanding and improving the effectiveness, efficiency, and equity of government action. For decades, politicians, advocates, reformers, and researchers have simultaneously espoused this value, while also paradoxically lamenting the lack of impact of policy knowledge on decision making, and the failure of related reforms. This text explores this paradox, identifying the reliance on a proverb of using policy knowledge to supplant politics as a primary culprit for these perceived failures. The evidence in this book suggests that any consideration of the role of policy knowledge in decision making must be considered alongside, rather than in place of, considerations of the ideologies, interests, and institutional factors that shape political decisions. This contextually rich approach offers practical insights to understand the role of policy knowledge, and to better leverage it to support good governance decisions.

This book, jointly authored by two distinguished philosophers and two prominent social scientists, has an ambitious aim: to improve decision-making in education policy. First they dive into the goals of education policy and explain the terms "educational goods" and "childhood goods," adding precision and clarity to the discussion of the distributive values that are essential for good decision-making about education. Then they provide a framework for individual decision-makers that enables them to combine values and evidence in the evaluation of educational policy options. Finally they delve into the particular policy issues of school finance, school accountability, and school choice, and they show how decision makers might approach them in the light of this decision-making framework. The authors are not advocated particular policy choices, however. The focus instead is a smart framework that will make it easier for policymakers (and readers) to identify and think through what they disagree with others about.

The main problem addressed by this work is how to model and combine bodies of knowledge (or evidence) while maintaining the representation of the unkowledge and of the conflict among the bodies. This is a problem with far-reaching applications in many knowledge segments, in particular for the fields of artificial intelligence, product design, decision making, knowledge engineering and uncertain probability. It must be kept in mind that knowledge based systems depend on algorithms able to relate the inputs of a system to a correct answer coming out of the knowledge-base, and both the inputs and the knowledge-base are subject to information imperfections caused by the unknowledge and the conflict. There are several formalism to deal with knowledge representation and combination, among them the Mathematical Theory of Evidence or Dempster-Shafer Theory. This work extends the Mathematical Theory of Evidence through the adoption of a new rule for the combination of evidence and a companion set of concepts. This extension solves the counter-intuitive problems illustrated in the original theory, extends its power of expression and allows the representation of uncertainty in the results. The representation of uncertainty implies the possibility of its use in decision-making and also makes explicit the relationship between the numeric results achieved and the results from classical probability theory.

This volume brings together a range of contributors from Europe and North America. All contributions were especially commissioned with a view to e- cidating a major multidisciplinary topic that is of concern to both academics and practitioners. The focus of the book is on expert judgment and its interaction with decision support systems. In the first part, the nature of expertise is discussed and characteristics of expert judges are described. Issues concemed with the eval- tion of judgment in the psychological laboratory are assessed and contrasted with studies of expert judgment in ecologically valid contexts. In addition, issues concerned with eliciting and validating expert knowledge are discussed. Dem- strations of good judgmental performance are linked to situational factors such as feedback cycles, and measurement of coherence and reliability in expert ju- ment is introduced as a baseline determinant of good judgmental performance. Issues concerned with the representation of elicited expert knowledge in kno- edge-based systems are evaluated and methods are described that have been shown to produce improvements in judgmental performance. Behavioral and mathematical ways of combining judgments from multiple experts are compared and contrasted. Finally, the issues developed in the preceding contributions are focused on current controversies in decision support. Expert judgment is utilized as a major input into decision analysis, forecasting with statistical models, and expert s- tems.

"Nurses play a vital role in improving the safety and quality of patient car -- not only in the hospital or ambulatory treatment facility, but also of community-based care and the care performed by family members. Nurses need know what proven techniques and interventions they can use to enhance patient outcomes. To address this need, the Agency for Healthcare Research and Quality (AHRQ), with additional funding from the Robert Wood Johnson Foundation, has prepared this comprehensive, 1,400-page, handbook for nurses on patient safety and quality -- Patient Safety and Quality: An Evidence-Based Handbook for Nurses. (AHRQ Publication No. 08-0043)." - online AHRQ blurb, http://www.ahrq.gov/qual/nurseshdbk/