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The implications for philosophy and cognitive science of developments in statistical learning theory. In Reliable Reasoning, Gilbert Harman and Sanjeev Kulkarni—a philosopher and an engineer—argue that philosophy and cognitive science can benefit from statistical learning theory (SLT), the theory that lies behind recent advances in machine learning. The philosophical problem of induction, for example, is in part about the reliability of inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors—a central topic in SLT. After discussing philosophical attempts to evade the problem of induction, Harman and Kulkarni provide an admirably clear account of the basic framework of SLT and its implications for inductive reasoning. They explain the Vapnik-Chervonenkis (VC) dimension of a set of hypotheses and distinguish two kinds of inductive reasoning. The authors discuss various topics in machine learning, including nearest-neighbor methods, neural networks, and support vector machines. Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT.
The implications for philosophy and cognitive science of developments in statistical learning theory. In Reliable Reasoning, Gilbert Harman and Sanjeev Kulkarni—a philosopher and an engineer—argue that philosophy and cognitive science can benefit from statistical learning theory (SLT), the theory that lies behind recent advances in machine learning. The philosophical problem of induction, for example, is in part about the reliability of inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors—a central topic in SLT. After discussing philosophical attempts to evade the problem of induction, Harman and Kulkarni provide an admirably clear account of the basic framework of SLT and its implications for inductive reasoning. They explain the Vapnik-Chervonenkis (VC) dimension of a set of hypotheses and distinguish two kinds of inductive reasoning. The authors discuss various topics in machine learning, including nearest-neighbor methods, neural networks, and support vector machines. Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT.
'You shouldn't drink too much. The Earth is round. Milk is good for your bones.' Are any of these claims true? How can you tell? Can you ever be certain you are right? For anyone tackling philosophical logic for the first time, here is a practical guide to the skills required to think critically. From the basics of good reasoning to the difference between claims, evidence and arguments, Jamie Carlin Watson, Robert Arp and Skyler King cover the topics found in an introductory course. Now revised and fully updated, this 3rd edition gives you the chance to develop critical thinking skills that can be used in and out of the classroom. Two new chapters on reasoning in the age of conspiracy theories and fake news demonstrate how to apply reason and avoid being dissuaded by the persuasive power of evidence-free emoting. Features include a glossary, chapter goals, more student-friendly exercises, study questions, diagrams, and suggestions for further reading. Chapter topics, organised around real-life examples such as predicting the weather, a murder mystery and the Ouija board, cover: - the structure, formation, analysis and recognition of arguments - deductive validity and soundness - inductive strength and cogency - inference to the best explanation - truth tables - tools for argument assessment - informal and formal fallacies This entertaining and easy-to-follow introduction is a complete beginner's tool set to good reasoning, analyzing and arguing.
Good Reasoning Matters uses an innovative approach to critical thinking by teaching students how to argue effectively rather than just point out the short comings of ineffective arguments.
Although both philosophers and scientists are interested in how to obtain reliable knowledge in the face of error, there is a gap between their perspectives that has been an obstacle to progress. By means of a series of exchanges between the editors and leaders from the philosophy of science, statistics and economics, this volume offers a cumulative introduction connecting problems of traditional philosophy of science to problems of inference in statistical and empirical modelling practice. Philosophers of science and scientific practitioners are challenged to reevaluate the assumptions of their own theories - philosophical or methodological. Practitioners may better appreciate the foundational issues around which their questions revolve and thereby become better 'applied philosophers'. Conversely, new avenues emerge for finally solving recalcitrant philosophical problems of induction, explanation and theory testing.
Dealing with uncertainty, moving from ignorance to knowledge, is the focus of cognitive processes. Understanding these processes and modelling, designing, and building artificial cognitive systems have long been challenging research problems. This book describes the theory and methodology of a new, scientifically well-founded general approach, and its realization in the form of intelligent systems applicable in disciplines ranging from social sciences, such as cognitive science and sociology, through natural sciences, such as life sciences and chemistry, to applied sciences, such as medicine, education, and engineering. The main subject developed in the book is cognitive reasoning investigated at three levels of abstraction: conceptual, formal, and realizational. The authors offer a model of a cognizing agent for the conceptual theory of cognitive reasoning, and they also present a logically well-founded formal cognitive reasoning framework to handle the various plausible reasoning methods. They conclude with an object model of a cognitive engine. The book is suitable for researchers, scientists, and graduate students working in the areas of artificial intelligence, mathematical logic, and philosophy.
Good reasoning can lead to success; bad reasoning can lead to catastrophe. Yet, it's not obvious how we reason, and why we make mistakes. This book looks at the mental processes that underlie our reasoning. It provides the most accessible account yet of the science of reasoning.
Philosophers have always recognized the value of reason, but the process of reasoning itself has only recently begun to emerge as a philosophical topic in its own right. Is reasoning a distinctive kind of mental process? If so, what is its nature? How does reasoning differ from merely freely associating thoughts? What is the relationship between reasoning about what to believe and reasoning about how to act? Is reasoning itself something you do, or something that happens to you? And what is the value of reasoning? Are there rules for good or correct reasoning and, if so, what are they like? Does good reasoning always lead to justified belief or rational action? Is there more than one way to reason correctly from your evidence? This volume comprises twelve new essays by leading researchers in the philosophy of reasoning that together address these questions and many more, and explore the connections between them.
An essential tool for our post-truth world: a witty primer on logic—and the dangers of illogical thinking—by a renowned Notre Dame professor Logic is synonymous with reason, judgment, sense, wisdom, and sanity. Being logical is the ability to create concise and reasoned arguments—arguments that build from given premises, using evidence, to a genuine conclusion. But mastering logical thinking also requires studying and understanding illogical thinking, both to sharpen one’s own skills and to protect against incoherent, or deliberately misleading, reasoning. Elegant, pithy, and precise, Being Logical breaks logic down to its essentials through clear analysis, accessible examples, and focused insights. D. Q. McInerney covers the sources of illogical thinking, from naïve optimism to narrow-mindedness, before dissecting the various tactics—red herrings, diversions, and simplistic reasoning—the illogical use in place of effective reasoning. An indispensable guide to using logic to advantage in everyday life, this is a concise, crisply readable book. Written explicitly for the layperson, McInerny’s Being Logical promises to take its place beside Strunk and White’s The Elements of Style as a classic of lucid, invaluable advice. Praise for Being Logical “Highly readable . . . D. Q. McInerny offers an introduction to symbolic logic in plain English, so you can finally be clear on what is deductive reasoning and what is inductive. And you’ll see how deductive arguments are constructed.”—Detroit Free Press “McInerny’s explanatory outline of sound thinking will be eminently beneficial to expository writers, debaters, and public speakers.”—Booklist “Given the shortage of logical thinking, And the fact that mankind is adrift, if not sinking, It is vital that all of us learn to think straight. And this small book by D.Q. McInerny is great. It follows therefore since we so badly need it, Everybody should not only but it, but read it.” —Charles Osgood