Bayesian nets are widely used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. But many philosophers have criticised and ultimately rejected the central assumption on which such work is based - the Causal Markov Condition. So should Bayesian nets be abandoned? What explains their success in artificial intelligence? This book argues that the Causal Markov Condition holds as a default rule: it often holds but may need to be repealed in the face of counterexamples. Thus Bayesian nets are the right tool to use by default but naively applying them can lead to problems. The book develops a systematic account of causal reasoning and shows how Bayesian nets can be coherently employed to automate the reasoning processes of an artificial agent. The resulting framework for causal reasoning involves not only new algorithms but also new conceptual foundations. Probability and causality are treated as mental notions - part of an agent's belief state.Yet probability and causality are also objective - different agents with the same background knowledge ought to adopt the same or similar probabilistic and causal beliefs. This book, aimed at researchers and graduate students in computer science, mathematics and philosophy, provides a general introduction to these philosophical views as well as an exposition of the computational techniques that they motivate.

Distinguished physicist examines emotive significance of time, time order of mechanics, time direction of thermodynamics and microstatistics, time direction of macrostatistics, and time of quantum physics. Analytic methods of scientific philosophy in investigation of probability, quantum mechanics, theory of relativity, causality. 1971 edition.

One philosophical approach to causation sees counterfactual dependence as the key to the explanation of causal facts: for example, events c (the cause) and e (the effect) both occur, but had c not occurred, e would not have occurred either. The counterfactual analysis of causation became a focus of philosophical debate after the 1973 publication of the late David Lewis's groundbreaking paper, "Causation," which argues against the previously accepted "regularity" analysis and in favor of what he called the "promising alternative" of the counterfactual analysis. Thirty years after Lewis's paper, this book brings together some of the most important recent work connecting--or, in some cases, disputing the connection between--counterfactuals and causation, including the complete version of Lewis's Whitehead lectures, "Causation as Influence," a major reworking of his original paper. Also included is a more recent essay by Lewis, "Void and Object," on causation by omission. Several of the essays first appeared in a special issue of the Journal of Philosophy, but most, including the unabridged version of "Causation as Influence," are published for the first time or in updated forms.Other topics considered include the "trumping" of one event over another in determining causation; de facto dependence; challenges to the transitivity of causation; the possibility that entities other than events are the fundamental causal relata; the distinction between dependence and production in accounts of causation; the distinction between causation and causal explanation; the context-dependence of causation; probabilistic analyses of causation; and a singularist theory of causation.

A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

This book, by one of the pre-eminent philosophers of science writing today, offers the most comprehensive account available of causal asymmetries. Causation is asymmetrical in many different ways. Causes precede effects; explanations cite causes not effects. Agents use causes to manipulate their effects; they don't use effects to manipulate their causes. Effects of a common cause are correlated; causes of a common effect are not. This book explains why a relationship that is asymmetrical in one of these regards is asymmetrical in the others. Hausman discovers surprising hidden connections between theories of causation and traces them all to an asymmetry of independence. This is a major book for philosophers of science that will also prove insightful to economists and statisticians.

There is a need for integrated thinking about causality, probability and mechanisms in scientific methodology. Causality and probability are long-established central concepts in the sciences, with a corresponding philosophical literature examining their problems. On the other hand, the philosophical literature examining mechanisms is not long-established, and there is no clear idea of how mechanisms relate to causality and probability. But we need some idea if we are to understand causal inference in the sciences: a panoply of disciplines, ranging from epidemiology to biology, from econometrics to physics, routinely make use of probability, statistics, theory and mechanisms to infer causal relationships. These disciplines have developed very different methods, where causality and probability often seem to have different understandings, and where the mechanisms involved often look very different. This variegated situation raises the question of whether the different sciences are really using different concepts, or whether progress in understanding the tools of causal inference in some sciences can lead to progress in other sciences. The book tackles these questions as well as others concerning the use of causality in the sciences.

Mario Bunge, author of the monumental Treatise on Basic Philosophy, is widely renowned as a philosopher of science. In this new and ambitious work he shifts his attention to the social sciences and the social technologies. He considers a number of disciplines, including anthropology, sociology, economics, political science, law, history, and management science.Bunge contends that social science research has fallen prey to a postmodern fascination with irrationalism and relativism. He urges social scientists to re-examine the philosophy and the methodology at the base of their discipline. Bunge calls for objective and relevant fact-finding, rigorous theorizing, and empirical testing, as well as morally sensitive and socially responsible policy design.

Electrifying when they were first delivered in 1973, becoming legendary in the years since, as transcripts passed from hand to hand, Dieter Henrich's lectures on German idealism were the first contact a major German philosopher had made with an American audience since the onset of World War II. They remain, to this day, one of the most eloquent interpretations of the central philosophical tradition of Germany and the way in which it relates to the concerns of contemporary philosophy. Thanks to the editorial work of David Pacini, one of the original auditors of Henrich's course, the lectures appear here with annotations that link them to the editions of the masterworks of German philosophy as they are now available. </p>
Henrich describes the movement that led from Kant to Hegel, beginning with an interpretation of the structure and tensions of Kant's system. He locates the Kantian movement and revival of Spinoza, as sketched by F. H. Jacobi, in the intellectual conditions of the time and in the philosophical motivations of modern thought. And he explains the motives behind Fichte's Doctrine of Science. Henrich connects this history to the poet Hölderlin's original philosophy and to the thought of the founders of Romanticism, Novalis and Friedrich Schlegel. He concludes with an interpretation of the basic design of Hegel's system. </p>