A Practitioner's Guide to Teach for Generalization Within ABA Treatment for Autism and Other Disabilities

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

Integrating humanism and behaviorism, this volume presents evidence-based techniques for improving health, safety, and well-being in all walks of life.

This volume examines the limitations of mathematical logic and proposes a new approach to logic intended to overcome them. To this end, the book compares mathematical logic with earlier views of logic, both in the ancient and in the modern age, including those of Plato, Aristotle, Bacon, Descartes, Leibniz, and Kant. From the comparison it is apparent that a basic limitation of mathematical logic is that it narrows down the scope of logic confining it to the study of deduction, without providing tools for discovering anything new. As a result, mathematical logic has had little impact on scientific practice. Therefore, this volume proposes a view of logic according to which logic is intended, first of all, to provide rules of discovery, that is, non-deductive rules for finding hypotheses to solve problems. This is essential if logic is to play any relevant role in mathematics, science and even philosophy. To comply with this view of logic, this volume formulates several rules of discovery, such as induction, analogy, generalization, specialization, metaphor, metonymy, definition, and diagrams. A logic based on such rules is basically a logic of discovery, and involves a new view of the relation of logic to evolution, language, reason, method and knowledge, particularly mathematical knowledge. It also involves a new view of the relation of philosophy to knowledge. This book puts forward such new views, trying to open again many doors that the founding fathers of mathematical logic had closed historically. trigger

Many of America's greatest artists, scientists, investors, educators, and entrepreneurs have come from abroad. Rather than suffering from the "brain drain" of talented and educated individuals emigrating, the United States has benefited greatly over the years from the "brain gain" of immigration. These gifted immigrants have engineered advances in energy, information technology, international commerce, sports, arts, and culture. To stay competitive, the United States must institute more of an open-door policy to attract unique talents from other nations. Yet Americans resist such a policy despite their own immigrant histories and the substantial social, economic, intellectual, and cultural benefits of welcoming newcomers. Why? In Brain Gain, Darrell West asserts that perception or "vision" is one reason reform in immigration policy is so politically difficult. Public discourse tends to emphasize the perceived negatives. Fear too often trumps optimism and reason. And democracy is messy, with policy principles that are often difficult to reconcile. The seeming irrationality of U.S. immigration policy arises from a variety of thorny and interrelated factors: particularistic politics and fragmented institutions, public concern regarding education and employment, anger over taxes and social services, and ambivalence about national identity, culture, and language. Add to that stew a myopic (or worse) press, persistent fears of terrorism, and the difficulties of implementing border enforcement and legal justice. West prescribes a series of reforms that will put America on a better course and enhance its long-term social and economic prosperity. Reconceptualizing immigration as a way to enhance innovation and competitiveness, the author notes, will help us find the next Sergey Brin, the next Andrew Grove, or even the next Albert Einstein.

Based upon the authors' many years of classroom experience and consulting work this volume is filled with practical, research-based and tested strategies to help teachers create an environment that supports students' sense of self-esteem, influence and autonomy whilst preventing possible conflict.

Based on a collection of chapters of leading scholars in the field, the purpose of this book is to intervene in current debates on the scientific foundation of psychological theory, methodology and research practice, and to offer an in-depth, situated and contextual understanding of psychological generalization. This book aims to contribute to a theoretical and methodological vocabulary which includes the subjective dimension of human life in psychological inquiry, and roots processes of generalization in persons’ common, social, cultural and material practices of everyday living. The volume is directed to students, professors, and researchers in psychology as well as to scholars in other branches of the humanities and social science where psychology and especially subjectivity, everyday practice and the development of psychological knowledge is an issue. The volume will be of particular interest to scholars in the field of cultural psychology, critical psychology, psychology of everyday life as well as psychological methodology and qualitative studies of everyday life including the various critical undergraduate, graduate, master, and PhD programs. The book will also be of special interest for scholars working in social psychology, history of psychology, general psychology, theoretical psychology, environmental psychology and political psychology.

In choosing between moral alternatives -- choosing between various forms of ethical action -- we typically make calculations of the following kind: A is better than B; B is better than C; therefore A is better than C. These inferences use the principle of transitivity and are fundamental to many forms of practical and theoretical theorizing, not just in moral and ethical theory but in economics. Indeed they are so common as to be almost invisible. What Larry Temkin's book shows is that, shockingly, if we want to continue making plausible judgments, we cannot continue to make these assumptions. Temkin shows that we are committed to various moral ideals that are, surprisingly, fundamentally incompatible with the idea that "better than" can be transitive. His book develops many examples where value judgments that we accept and find attractive, are incompatible with transitivity. While this might seem to leave two options -- reject transitivity, or reject some of our normative commitments in order to keep it -- Temkin is neutral on which path to follow, only making the case that a choice is necessary, and that the cost either way will be high. Temkin's book is a very original and deeply unsettling work of skeptical philosophy that mounts an important new challenge to contemporary ethics.

Discover the essential thinking tools you’ve been missing with The Great Mental Models series by Shane Parrish, New York Times bestselling author and the mind behind the acclaimed Farnam Street blog and “The Knowledge Project” podcast. This first book in the series is your guide to learning the crucial thinking tools nobody ever taught you. Time and time again, great thinkers such as Charlie Munger and Warren Buffett have credited their success to mental models–representations of how something works that can scale onto other fields. Mastering a small number of mental models enables you to rapidly grasp new information, identify patterns others miss, and avoid the common mistakes that hold people back. The Great Mental Models: Volume 1, General Thinking Concepts shows you how making a few tiny changes in the way you think can deliver big results. Drawing on examples from history, business, art, and science, this book details nine of the most versatile, all-purpose mental models you can use right away to improve your decision making and productivity. This book will teach you how to: Avoid blind spots when looking at problems. Find non-obvious solutions. Anticipate and achieve desired outcomes. Play to your strengths, avoid your weaknesses, … and more. The Great Mental Models series demystifies once elusive concepts and illuminates rich knowledge that traditional education overlooks. This series is the most comprehensive and accessible guide on using mental models to better understand our world, solve problems, and gain an advantage.

An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers