Reliable Reasoning

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.

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.

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.

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.

The goal of Reasoning for Intelligence Analysts is to address the three distinct dimensions of an analyst’s thinking: the person of the analyst (their traits), the processes they use (their techniques), and the problems they face (their targets). Based on a decade of academic research and university teaching in a program for aspiring intelligence analysts, this multidimensional approach will help the reader move beyond the traditional boundaries of accumulating knowledge or critical thinking with techniques to assess the unique targets of reasoning in the information age. This approach is not just a set of techniques, but covers all elements of reasoning by discussing the personal, procedural, and problem-specific aspects. It also addresses key challenges, such as uncertain data, irrelevant or misleading information, indeterminate outcomes, and significance for clients through an extensive examination of hypothesis development, causal analysis, futures exploration, and strategy assessment. Both critical and creative thinking, which are essential to reasoning in intelligence, are integrated throughout. Structured around independently readable chapters, this text offers a systematic approach to reasoning a long with an extensive toolkit that will serve the needs of both students and intelligence professionals.

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.