This work develops logical theories necessary to understand adaptable human reasoning & the design ofintelligent systems. It unifies lively & significant strands of research in logic, philosophy, economics & artificial intelligence.

A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.

This fascinating study in the sociology of science explores the way scientists conduct, and draw conclusions from, their experiments. The book is organized around three case studies: replication of the TEA-laser, detecting gravitational rotation, and some experiments in the paranormal. "In his superb book, Collins shows why the quest for certainty is disappointed. He shows that standards of replication are, of course, social, and that there is consequently no outside standard, no Archimedean point beyond society from which we can lever the intellects of our fellows."—Donald M. McCloskey, Journal of Economic Psychology "Collins is one of the genuine innovators of the sociology of scientific knowledge. . . . Changing Order is a rich and entertaining book."—Isis "The book gives a vivid sense of the contingent nature of research and is generally a good read."—Augustine Brannigan, Nature "This provocative book is a review of [Collins's] work, and an attempt to explain how scientists fit experimental results into pictures of the world. . . . A promising start for new explorations of our image of science, too often presented as infallibly authoritative."—Jon Turney, New Scientist

Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.

Belief revision theory and philosophy of science both aspire to shed light on the dynamics of knowledge – on how our view of the world changes (typically) in the light of new evidence. Yet these two areas of research have long seemed strangely detached from each other, as witnessed by the small number of cross-references and researchers working in both domains. One may speculate as to what has brought about this surprising, and perhaps unfortunate, state of affairs. One factor may be that while belief revision theory has traditionally been pursued in a bottom- up manner, focusing on the endeavors of single inquirers, philosophers of science, inspired by logical empiricism, have tended to be more interested in science as a multi-agent or agent-independent phenomenon.

This book examines the human proclivity to resist changing our beliefs. Drawing on psychological, neurological, and philosophical research, and integrating topics as wide ranging as emotion, cognition, social (and physical) context, and learning theory, Lao and Young explore why this resistance to change impedes our learning and progression. They also suggest that failure to adapt our beliefs to available and informed evidence can incur costs that may be seen in personal growth, politics, science, law, medicine, education, and business. Resistance to Belief Change explores the various manifestations of resistance, including overt, discursive, and especially inertial forms of resistance. As well as the influential factors that can impact upon them, the book also examines how the self-directed learner, as well as teachers, may structure the learning experience to overcome resistance and facilitate progressive and adaptive learning. Lao and Young find that the impediments to learning and resistance to change are far more prevalent and costly than previously suggested in research, and so this book will be of interest to a range of people in cognitive development, social psychology, and clinical and educational psychology.

“An intense snapshot of the chain reaction caused by pulling a trigger.” —Booklist (starred review) “Astonishing.” —Kirkus Reviews (starred review) “A tour de force.” —Publishers Weekly (starred review) A Newbery Honor Book A Coretta Scott King Honor Book A Printz Honor Book A Time Best YA Book of All Time (2021) A Los Angeles Times Book Prize Winner for Young Adult Literature Longlisted for the National Book Award for Young People’s Literature Winner of the Walter Dean Myers Award An Edgar Award Winner for Best Young Adult Fiction Parents’ Choice Gold Award Winner An Entertainment Weekly Best YA Book of 2017 A Vulture Best YA Book of 2017 A Buzzfeed Best YA Book of 2017 An ode to Put the Damn Guns Down, this is New York Times bestselling author Jason Reynolds’s electrifying novel that takes place in sixty potent seconds—the time it takes a kid to decide whether or not he’s going to murder the guy who killed his brother. A cannon. A strap. A piece. A biscuit. A burner. A heater. A chopper. A gat. A hammer A tool for RULE Or, you can call it a gun. That’s what fifteen-year-old Will has shoved in the back waistband of his jeans. See, his brother Shawn was just murdered. And Will knows the rules. No crying. No snitching. Revenge. That’s where Will’s now heading, with that gun shoved in the back waistband of his jeans, the gun that was his brother’s gun. He gets on the elevator, seventh floor, stoked. He knows who he’s after. Or does he? As the elevator stops on the sixth floor, on comes Buck. Buck, Will finds out, is who gave Shawn the gun before Will took the gun. Buck tells Will to check that the gun is even loaded. And that’s when Will sees that one bullet is missing. And the only one who could have fired Shawn’s gun was Shawn. Huh. Will didn’t know that Shawn had ever actually USED his gun. Bigger huh. BUCK IS DEAD. But Buck’s in the elevator? Just as Will’s trying to think this through, the door to the next floor opens. A teenage girl gets on, waves away the smoke from Dead Buck’s cigarette. Will doesn’t know her, but she knew him. Knew. When they were eight. And stray bullets had cut through the playground, and Will had tried to cover her, but she was hit anyway, and so what she wants to know, on that fifth floor elevator stop, is, what if Will, Will with the gun shoved in the back waistband of his jeans, MISSES. And so it goes, the whole long way down, as the elevator stops on each floor, and at each stop someone connected to his brother gets on to give Will a piece to a bigger story than the one he thinks he knows. A story that might never know an END…if Will gets off that elevator. Told in short, fierce staccato narrative verse, Long Way Down is a fast and furious, dazzlingly brilliant look at teenage gun violence, as could only be told by Jason Reynolds.

The purpose of our research is to enhance the efficiency of AI problem solvers by automating representation changes. We have developed a system that improves the description of input problems and selects an appropriate search algorithm for each given problem. Motivation. Researchers have accumulated much evidence on the impor tance of appropriate representations for the efficiency of AI systems. The same problem may be easy or difficult, depending on the way we describe it and on the search algorithm we use. Previous work on the automatic im provement of problem descriptions has mostly been limited to the design of individual learning algorithms. The user has traditionally been responsible for the choice of algorithms appropriate for a given problem. We present a system that integrates multiple description-changing and problem-solving algorithms. The purpose of the reported work is to formalize the concept of representation and to confirm the following hypothesis: An effective representation-changing system can be built from three parts: • a library of problem-solving algorithms; • a library of algorithms that improve problem descriptions; • a control module that selects algorithms for each given problem.