From random security checks at airports to the use of risk assessment in sentencing, actuarial methods are being used more than ever to determine whom law enforcement officials target and punish. And with the exception of racial profiling on our highways and streets, most people favor these methods because they believe they’re a more cost-effective way to fight crime. In Against Prediction, Bernard E. Harcourt challenges this growing reliance on actuarial methods. These prediction tools, he demonstrates, may in fact increase the overall amount of crime in society, depending on the relative responsiveness of the profiled populations to heightened security. They may also aggravate the difficulties that minorities already have obtaining work, education, and a better quality of life—thus perpetuating the pattern of criminal behavior. Ultimately, Harcourt shows how the perceived success of actuarial methods has begun to distort our very conception of just punishment and to obscure alternate visions of social order. In place of the actuarial, he proposes instead a turn to randomization in punishment and policing. The presumption, Harcourt concludes, should be against prediction.

By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.

This innovative book brings together two disciplines — science and art — and enables readers to produce their own computer-generated displays. 44 colour plates and 200 black and white pictures showcase the diagrams that can easily be reproduced using the accompanying CD-ROM. It is possible to create diagrams that indicate predictability or unpredictability of physical, chemical, ecological, mathematical or economic systems. Grey levels and colours indicate the stability of a predictable system, or the extent of unpredictability. In addition, diagrams can be drawn purely for their aesthetic value. Directed both at scientists and laymen, technicians and artists, this combination of book and CD-ROM is the first of its kind./aDownload Images as Wallpapers:

This book develops a philosophico-methodological analysis of prediction and its role in economics. Prediction plays a key role in economics in various ways. It can be seen as a basic science, as an applied science and in the application of this science. First, it is used by economic theory in order to test the available knowledge. In this regard, prediction has been presented as the scientific test for economics as a science. Second, prediction provides a content regarding the possible future that can be used for prescription in applied economics. Thus, it can be used as a guide for economic policy, i.e., as knowledge concerning the future to be employed for the resolution of specific problems. Third, prediction also has a role in the application of this science in the public arena. This is through the decision-making of the agents — individuals or organizations — in quite different settings, both in the realm of microeconomics and macroeconomics. Within this context, the research is organized in five parts, which discuss relevant aspects of the role of prediction in economics: I) The problem of prediction as a test for a science; II) The general orientation in methodology of science and the problem of prediction as a scientific test; III) The methodological framework of social sciences and economics: Incidence for prediction as a test; IV) Epistemology and methodology of economic prediction: Rationality and empirical approaches and V) Methodological aspects of economic prediction: From description to prescription. Thus, the book is of interest for philosophers and economists as well as policy-makers seeking to ascertain the roots of their performance. The style used lends itself to a wide audience.

Since its original publication, Expert Political Judgment by New York Times bestselling author Philip Tetlock has established itself as a contemporary classic in the literature on evaluating expert opinion. Tetlock first discusses arguments about whether the world is too complex for people to find the tools to understand political phenomena, let alone predict the future. He evaluates predictions from experts in different fields, comparing them to predictions by well-informed laity or those based on simple extrapolation from current trends. He goes on to analyze which styles of thinking are more successful in forecasting. Classifying thinking styles using Isaiah Berlin's prototypes of the fox and the hedgehog, Tetlock contends that the fox--the thinker who knows many little things, draws from an eclectic array of traditions, and is better able to improvise in response to changing events--is more successful in predicting the future than the hedgehog, who knows one big thing, toils devotedly within one tradition, and imposes formulaic solutions on ill-defined problems. He notes a perversely inverse relationship between the best scientific indicators of good judgement and the qualities that the media most prizes in pundits--the single-minded determination required to prevail in ideological combat. Clearly written and impeccably researched, the book fills a huge void in the literature on evaluating expert opinion. It will appeal across many academic disciplines as well as to corporations seeking to develop standards for judging expert decision-making. Now with a new preface in which Tetlock discusses the latest research in the field, the book explores what constitutes good judgment in predicting future events and looks at why experts are often wrong in their forecasts.

This monograph analyzes Nicholas Rescher’s system of pragmatic idealism. It also looks at his approach to prediction in science. Coverage highlights a prominent contribution to a central topic in the philosophy and methodology of science. The author offers a full characterization of Rescher’s system of philosophy. She presents readers with a comprehensive philosophico-methodological analysis of this important work. Her research takes into account different thematic realms: semantic, logical, epistemological, methodological, ontological, axiological, and ethical. The book features three, thematic-parts: I) General Coordinates, Semantic Features and Logical Components of Scientific Prediction; II) Predictive Knowledge and Predictive Processes in Rescher’s Methodological Pragmatism; and III) From Reality to Values: Ontological Features, Axiological Elements, and Ethical Aspects of Scientific Prediction. This insightful analysis offers a critical reconstruction of Rescher’s philosophy. The system he created is often characterized as pragmatic idealism that is open to some realist elements. He is a prominent representative of contemporary pragmatism who has made a great deal of contributions to the study of this topic. This area is crucial for science and it has been little considered in the philosophy of science.