The Probabilistic Foundations Of Rational Learning

This book extends Bayesian epistemology to develop new approaches to general rational learning within the framework of probability theory.

A number of well-developed theories shed light on the question, under what circumstances our beliefs enjoy epistemic justification. Yet, comparatively little is known about epistemic defeat—when new information causes the loss of epistemic justification. This book proposes and defends a detailed account of epistemic defeaters. The main kinds of defeaters are analyzed in detail and integrated into a general framework that aims to explain how beliefs lose justification. It is argued that defeaters introduce incompatibilities into a noetic system and thereby prompt a structured re-evaluation process that makes a justified reinstatement of the defeated belief impossible. The account is then applied to the topic of disagreement, where it is used in an argument for conciliationism, as well as a new explanation for higher-order defeat. Throughout the book, the notion of defeat is the center of attention, while a number of new issues are discussed at the intersections of defeat and justification. Specifically, new problems are raised for broadly internalist accounts of defeat, a fully descriptive reliabilist account of defeat is provided, and the case for normative defeat is revisited.

The year’s finest mathematical writing from around the world This annual anthology brings together the year’s finest mathematics writing from around the world. Featuring promising new voices alongside some of the foremost names in the field, The Best Writing on Mathematics 2018 makes available to a wide audience many pieces not easily found anywhere else—and you don’t need to be a mathematician to enjoy them. These essays delve into the history, philosophy, teaching, and everyday aspects of math, offering surprising insights into its nature, meaning, and practice—and taking readers behind the scenes of today’s hottest mathematical debates. James Grime shows how to build subtly mischievous dice for playing slightly unfair games and Michael Barany traces how our appreciation of the societal importance of mathematics has developed since World War II. In other essays, Francis Su extolls the inherent values of learning, doing, and sharing mathematics, and Margaret Wertheim takes us on a mathematical exploration of the mind and the world—with glimpses at science, philosophy, music, art, and even crocheting. And there’s much, much more. In addition to presenting the year’s most memorable math writing, this must-have anthology includes an introduction by the editor and a bibliography of other notable pieces on mathematics. This is a must-read for anyone interested in where math has taken us—and where it is headed.

The principles of game theory apply to a wide range of topics in biology. This book presents the central concepts in evolutionary game theory and provides an authoritative and up-to-date account. The focus is on concepts that are important for biologists in their attempts to explain observations. This strong connection between concepts and applications is a recurrent theme throughout the book which incorporates recent and traditional ideas from animal psychology, neuroscience, and machine learning that provide a mechanistic basis for behaviours shown by players of a game. The approaches taken to modelling games often rest on idealized and unrealistic assumptions whose limitations and consequences are not always appreciated. The authors provide a novel reassessment of the field, highlighting how to overcome limitations and identifying future directions. Game Theory in Biology is an advanced textbook suitable for graduate level students as well as professional researchers (both empiricists and theoreticians) in the fields of behavioural ecology and evolutionary biology. It will also be of relevance to a broader interdisciplinary audience including psychologists and neuroscientists.

Rudolf Carnap (1891–1970), one of the most important philosophers of the twentieth century, helped found logical positivism, was one of the originators of the field of philosophy of science, and was a leading contributor to semantics and inductive logic. This volume of new essays, written by leading international experts, places Carnap in his philosophical context and studies his topics, his interests, and the major stages of his thought. The essays reassess Carnap's place in the history of analytic philosophy through his approach to metaphysics, values, politics, epistemology and philosophy of science. They delve into important topics of Carnap's mature thought, namely explication, naturalism, and his defence of analyticity; and they recover the logical and the linguistic components of philosophy and how they unfolded in the syntax-semantics relation, induction, and language-planning. The resulting interpretation of Carnap will be illuminating for both current and future research.

Probabilistic logic programming extends logic programming by enabling the representation of uncertain information by means of probability theory. Probabilistic logic programming is at the intersection of two wider research fields: the integration of logic and probability and probabilistic programming. Logic enables the representation of complex relations among entities while probability theory is useful for modeling uncertainty over attributes and relations. Combining the two is a very active field of study. Probabilistic programming extends programming languages with probabilistic primitives that can be used to write complex probabilistic models. Algorithms for inference and learning tasks are then provided automatically by the system. Probabilistic logic programming is at the same time a logic language, with its knowledge representation capabilities, and a Turing complete language, with its computation capabilities, thus providing the best of both worlds. Since its birth, the field of probabilistic logic programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. This book aims at providing an overview of the field with a special emphasis on languages under the distribution semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections between the methods. Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online. This 2nd edition aims at reporting the most exciting novelties in the field since the publication of the 1st edition. The semantics for hybrid programs with function symbols is placed on a sound footing.

This book challenges the generally accepted theories of classical economics, explaining why the expected utility theory, even if it were true, fails to be of much help in solving economic controversies.