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.

This new volume addresses the central questions which surround the process of reasoning. This emerging topic of analytic philosophy intersects with numerous other areas of philosophy, such as epistemology, philosophy of mind, philosophy of language, and metaethics, and also psychological work on reasoning.

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.

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.

Introductions to the theory of knowledge are plentiful, but none introduce students to the most recent debates that exercise contemporary philosophers. Ian Evans and Nicholas D. Smith aim to change that. Their book guides the reader through the standard theories of knowledge while simultaneously using these as a springboard to introduce current debates. Each chapter concludes with a “Current Trends” section pointing the reader to the best literature dominating current philosophical discussion. These include: the puzzle of reasonable disagreement; the so-called "problem of easy knowledge" the intellectual virtues; and new theories in the philosophy of language relating to knowledge. Chapters include discussions of skepticism, the truth condition, belief and acceptance, justification, internalism versus externalism, epistemic evaluation, and epistemic contextualism. Evans and Smith do not merely offer a review of existing theories and debates; they also offer a novel theory that takes seriously the claim that knowledge is not unique to humans. Surveying current scientific literature in animal ethology, they discover surprising sophistication and diversity in non-human cognition. In their final analysis the authors provide a unified account of knowledge that manages to respect and explain this diversity. They argue that animals know when they make appropriate use of the cognitive processes available to animals of that kind, in environments within which those processes are veridically well-adapted. Knowledge is a lively and accessible volume, ideal for undergraduate and post-graduate students. It is also set to spark debate among scholars for its novel approaches to traditional topics and its thoroughgoing commitment to naturalism.