Skip navigation

Clark Glymour

Clark Glymour is Senior Research Scientist at IHMC and Alumni University Professor of Philosophy at Carnegie Mellon University.

Titles by This Author

Bayes Nets and Graphical Causal Models in Psychology

In recent years, small groups of statisticians, computer scientists, and philosophers have developed an account of how partial causal knowledge can be used to compute the effect of actions and how causal relations can be learned, at least by computers. The representations used in the emerging theory are causal Bayes nets or graphical causal models.

In his new book, Clark Glymour provides an informal introduction to the basic assumptions, algorithms, and techniques of causal Bayes nets and graphical causal models in the context of psychological examples. He demonstrates their potential as a powerful tool for guiding experimental inquiry and for interpreting results in developmental psychology, cognitive neuropsychology, psychometrics, social psychology, and studies of adult judgment. Using Bayes net techniques, Glymour suggests novel experiments to distinguish among theories of human causal learning and reanalyzes various experimental results that have been interpreted or misinterpreted—without the benefit of Bayes nets and graphical causal models. The capstone illustration is an analysis of the methods used in Herrnstein and Murray’s book The Bell Curve; Glymour argues that new, more reliable methods of data analysis, based on Bayes nets representations, would lead to very different conclusions from those advocated by Herrnstein and Murray.

What assumptions and methods allow us to turn observations into causal knowledge, and how can even incomplete causal knowledge be used in planning and prediction to influence and control our environment? In this book Peter Spirtes, Clark Glymour, and Richard Scheines address these questions using the formalism of Bayes networks, with results that have been applied in diverse areas of research in the social, behavioral, and physical sciences.

The authors show that although experimental and observational study designs may not always permit the same inferences, they are subject to uniform principles. They axiomatize the connection between causal structure and probabilistic independence, explore several varieties of causal indistinguishability, formulate a theory of manipulation, and develop asymptotically reliable procedures for searching over equivalence classes of causal models, including models of categorical data and structural equation models with and without latent variables.

The authors show that the relationship between causality and probability can also help to clarify such diverse topics in statistics as the comparative power of experimentation versus observation, Simpson's paradox, errors in regression models, retrospective versus prospective sampling, and variable selection.

The second edition contains a new introduction and an extensive survey of advances and applications that have appeared since the first edition was published in 1993.

An Introduction to Philosophical Issues and Achievements

Thinking Things Through provides a broad, historical, and rigorous introduction to the logical tradition in philosophy and to its contemporary significance. The book centers around three of the most fruitful issues in Western thought: What are proofs and why do they provide knowledge? How can experience be used to gain knowledge or to alter beliefs in a rational way? What is the nature of mind and of mental events and mental states? In a clear and lively style, Glymour describes these key philosophical problems and traces attempts at solutions from ancient Greece to the present.

A Bradford Book

Downloadable instructor resources available for this title: teaching manual

Titles by This Editor

For millennia, "from Aristotle to almost yesterday," the great problems of philosophy have all been about people: questions of epistemology and philosophy of mind have concerned human capacities and limitations. Still, say the editors of Thinking about Android Epistemology, there should be theories about other sorts of minds, other ways that physical systems can be organized to produce knowledge and competence. The emergence of artificial intelligence in mid-twentieth century provided a way to study the powers and limits of systems that learn, to theorize and to make theories sufficiently concrete so that their properties and consequences can be demonstrated. In this updated version of the 1995 MIT Press book Android Epistemology, computer scientists and philosophers—among them Herbert Simon, Daniel Dennett, and Paul Churchland—offer a gentle, unsystematic introduction to alternative systems of cognition. They look at android epistemology from both theoretical and practical points of view, offering not only speculative proposals but applications—ideas for using computational systems to expand human capacities. The accessible and entertaining essays include a comparison of 2001's HAL and today's computers, a conversation among aliens who have a low opinion of human cognition, an argument for the creativity of robots, and a short story illustrating the power of algorithms for learning causal relations.

Contributors:
Neil Agnew, Margaret Boden, Paul Churchland, Daniel Dennett, Ken M. Ford, Clark Glymour, Pat Hayes, Henry Kyburg, Doug Lenat, Marvin Minsky, Joseph Nadeau, Anatol Rappoport, Herbert Simon, Lynn Andrea Stein, Susan Sterrett

Android epistemology is the exploration of the space of possible machines and their capacities for knowledge, beliefs, attitudes, desires, and for action in accord with their mental states.