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.

Decisions We Make: How To Figure Things Out - Inductive Reasoning versus Deductive Reasoning is a most unusual book where the reader is taught how to figure things out using a step-by step process developed by one of the nation's leading behavioral scientist and best-selling author, Dr. Treat Preston. This book addresses such issues as the decisions we make, deadly decisions, inductive reasoning, deductive reasoning, reasoning skills, argumentation and debate, argumentation critical thinking in action. It leaves no stone unturned in its effort to teach its readers the value of being able to figure things out and developing a habit of proper decision making. Here is the TABLE OF CONTENTS: DISCLAIMER AND TERMS OF USE AGREEMENT:Introduction – There Are Two Sides to Every ArgumentChapter 1 - Laying A Proper FoundationChapter 2 – Inductive ReasoningChapter 3 – Deductive ReasoningChapter 4 – Intellectual versus Emotional DecisionsChapter 5 – Why We Must DecideChapter 6 – Summing It All UpI Have a Special Gift for My ReadersMeet the AuthorThe topic of “figuring things out” is not a subject taught in schools and it should be. Learning at a young age the art of reasoning and thinking is an essential tool to take from childhood into adulthood. Decision making should be based on facts and not emotion. Once all of the facts are garnered, a person is free to decide on what side of an argument they actually believe and reside but the decision should be based on fact and not emotion.

A thorough and practical introduction to inductive logic with a focus on arguments and the rules used for making inductive inferences. This textbook offers a thorough and practical introduction to inductive logic. The book covers a range of different types of inferences with an emphasis throughout on representing them as arguments. This allows the reader to see that, although the rules and guidelines for making each type of inference differ, the purpose is always to generate a probable conclusion. After explaining the basic features of an argument and the different standards for evaluating arguments, the book covers inferences that do not require precise probabilities or the probability calculus: the induction by confirmation, inference to the best explanation, and Mill's methods. The second half of the book presents arguments that do require the probability calculus, first explaining the rules of probability, and then the proportional syllogism, inductive generalization, and Bayes' rule. Each chapter ends with practice problems and their solutions. Appendixes offer additional material on deductive logic, odds, expected value, and (very briefly) the foundations of probability. Argument and Inference can be used in critical thinking courses. It provides these courses with a coherent theme while covering the type of reasoning that is most often used in day-to-day life and in the natural, social, and medical sciences. Argument and Inference is also suitable for inductive logic and informal logic courses, as well as philosophy of sciences courses that need an introductory text on scientific and inductive methods.

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.

From the very beginning of their investigation of human reasoning, philosophers have identified two other forms of reasoning, besides deduction, which we now call abduction and induction. Deduction is now fairly well understood, but abduction and induction have eluded a similar level of understanding. The papers collected here address the relationship between abduction and induction and their possible integration. The approach is sometimes philosophical, sometimes that of pure logic, and some papers adopt the more task-oriented approach of AI. The book will command the attention of philosophers, logicians, AI researchers and computer scientists in general.

Two psychologists, a computer scientist, and a philosopher have collaborated to present a framework for understanding processes of inductive reasoning and learning in organisms and machines. Theirs is the first major effort to bring the ideas of several disciplines to bear on a subject that has been a topic of investigation since the time of Socrates. The result is an integrated account that treats problem solving and induction in terms of rule�based mental models. Induction is included in the Computational Models of Cognition and Perception Series. A Bradford Book.

An introductory 2001 textbook on probability and induction written by a foremost philosopher of science.

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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.