A Bradford Book
2 total works
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.
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.
Thinking Things Through provides a broad, historical, and rigorous introduction to the logical tradition in philosophy and to its contemporary significance. The presentation is centered 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 to solve them, from ancient Greece to the present.Thinking Things Through reveals the philosophical sources of modern work in logic, the theory of computation, Bayesian statistics, cognitive psychology, and artificial intelligence, and it connects these subjects with contemporary problems in epistemology and metaphysics. The text is full of examples and problems, and an instructor's manual is available.Clark Glymour is Alumni Professor of Philosophy at Carnegie-Mellon University and Adjunct Professor of History and Philosophy of Science at the University of Pittsburgh.