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In An Aristotelian Account of Induction Groarke discusses the intellectual process through which we access the "first principles" of human thought - the most basic concepts, the laws of logic, the universal claims of science and metaphysics, and the deepest moral truths. Following Aristotle and others, Groarke situates the first stirrings of human understanding in a creative capacity for discernment that precedes knowledge, even logic. Relying on a new historical study of philosophical theories of inductive reasoning from Aristotle to the twenty-first century, Groarke explains how Aristotle offers a viable solution to the so-called problem of induction, while offering new contributions to contemporary accounts of reasoning and argument and challenging the conventional wisdom about induction.
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.
Induction is a basic method of scientific and philosophical inquiry. The work seeks to show against the skeptical tide that the method is secure and reliable. The problem of induction has been a hotly debated issue in modern and contemporary philosophy since David Hume. However, long before the modern era Indian philosophers have addressed this problem for about two thousand years. This work examines some major Indian viewpoints including those of Jayarasi (7th century), Dharmakirti (7th century), Prabhakara (8th century), Udayana (11th century) and Prabhacandra (14th century). It also discusses some influential contemporary positions including those of Russell, Strawson, Popper, Reichenbach, Carnap, Goodman and Quine. The main focus is on the Nyaya view developed by Gangesa (13th century). A substantial part of the work is devoted to annotated translation of selected chapters from Gangesa's work dealing with the problem of induction with copious references to the later Nyaya philosophers including Raghunatha (15th century), Mathuranatha (16th century), Jagadisa (17th century) and Gadadhara (17th century). An annotated translation of selections from Sriharsa (12th century) of the Vedanta school, Prabhacandra of the Jaina school and Dharmakirti of the Buddhist school is also included. A solution is presented to the classical problem of induction and the Grue paradox based on the Nyaya perspective. The solution includes an argument from counterfactual reasoning, arguments in defense of causality, analyses of circularity and logical economy, arguments for objective universals and an argument from belief-behavior contradiction.
"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."--
An introductory 2001 textbook on probability and induction written by a foremost philosopher of science.
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.
Unique and accessible explanations to some of life's biggest questions, obtained through a series of irresistable mental challenges
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Without inductive reasoning, we couldn't generalize from one instance to another, derive scientific hypotheses, or predict that the sun will rise again tomorrow morning. Despite the widespread nature of inductive reasoning, books on this topic are rare. Indeed, this is the first book on the psychology of inductive reasoning in twenty years. The chapters survey recent advances in the study of inductive reasoning and address questions about how it develops, the role of knowledge in induction, how best to model people's reasoning, and how induction relates to other forms of thinking. Written by experts in philosophy, developmental science, cognitive psychology, and computational modeling, the contributions here will be of interest to a general cognitive science audience as well as to those with a more specialized interest in the study of thinking.