In this book, Chattopadhyaya examines the epistemological and methodological implications of induction and probability. Opposed to foundationalism and the thesis of certainty of human knowledge, he has defended a qualified form of fallibilism and constructive kind of skepticism.

This volume offers a solution to one of the central, unsolved problems of Western philosophy, that of induction. It explores the implications of Hume's argument that successful prediction tells us nothing about the truth of the predicting theory.

This book develops new techniques in formal epistemology and applies them to the challenge of Cartesian skepticism. It introduces two formats of epistemic evaluation that should be of interest to epistemologists and philosophers of science: the dual-component format, which evaluates a statement on the basis of its safety and informativeness, and the relative-divergence format, which evaluates a probabilistic model on the basis of its complexity and goodness of fit with data. Tomoji Shogenji shows that the former lends support to Cartesian skepticism, but the latter allows us to defeat Cartesian skepticism. Along the way, Shogenji addresses a number of related issues in epistemology and philosophy of science, including epistemic circularity, epistemic closure, and inductive skepticism.

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.

This volume brings out the varieties of forms of philosophical skepticism that have continued to preoccupy philosophers for the past of couple of centuries, as well as the specific varieties of philosophical response that these have engendered — above all, in the work of those who have sought to take their cue from Kant, Wittgenstein, or Cavell — and to illuminate how these philosophical approaches are related to and bear upon one another. The philosophers brought together in this volume are united by the thought that a proper appreciation of the depth of the skeptical challenge must reveal it to be deeply disquieting, in the sense that skepticism threatens not just some set of theoretical commitments, but also-and fundamentally-our very sense of self, world, and other. Second, that skepticism is the proper starting point for any serious attempt to make sense of what philosophy is, and to gauge the prospects of philosophical progress.

In this book, Chattopadhyaya examines the epistemological and methodological implications of induction and probability. Opposed to foundationalism and the thesis of certainty of human knowledge, he has defended a qualified form of fallibilism and constructive kind of skepticism.

"The inaugural title in the new, Open Access series BSPS Open, The Material Theory of Induction will initiate a new tradition in the analysis of inductive inference. The fundamental burden of a theory of inductive inference is to determine which are the good inductive inferences or relations of inductive support and why it is that they are so. The traditional approach is modeled on that taken in accounts of deductive inference. It seeks universally applicable schemas or rules or a single formal device, such as the probability calculus. After millennia of halting efforts, none of these approaches has been unequivocally successful and debates between approaches persist. The Material Theory of Induction identifies the source of these enduring problems in the assumption taken at the outset: that inductive inference can be accommodated by a single formal account with universal applicability. Instead, it argues that that there is no single, universally applicable formal account. Rather, each domain has an inductive logic native to it. Which that is, and its extent, is determined by the facts prevailing in that domain. Paying close attention to how inductive inference is conducted in science and copiously illustrated with real-world examples, The Material Theory of Induction will initiate a new tradition in the analysis of inductive inference."--

This book contains important technical innovations, including comparative measures for the testable content, depth, and unity of scientific theories. Originally published in 1984. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.

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