Causal Models: How People Think about the World and Its Alternatives [Hardcover]

Steven Sloman (Author)

Book Description

ISBN-10: 0195183118 | ISBN-13: 978-0195183115 | Publication Date: July 28, 2005 | Edition: 1

Human beings are active agents who can think. To understand how thought serves action requires understanding how people conceive of the relation between cause and effect, between action and outcome. In cognitive terms, how do people construct and reason with the causal models we use to represent our world? A revolution is occurring in how statisticians, philosophers, and computer scientists answer this question. Those fields have ushered in new insights about causal models by thinking about how to represent causal structure mathematically, in a framework that uses graphs and probability theory to develop what are called causal Bayesian networks. The framework starts with the idea that the purpose of causal structure is to understand and predict the effects of intervention. How does intervening on one thing affect other things? This is not a question merely about probability (or logic), but about action. The framework offers a new understanding of mind: Thought is about the effects of intervention and cognition is thus intimately tied to actions that take place either in the actual physical world or in imagination, in counterfactual worlds. The book offers a conceptual introduction to the key mathematical ideas, presenting them in a non-technical way, focusing on the intuitions rather than the theorems. It tries to show why the ideas are important to understanding how people explain things and why thinking not only about the world as it is but the world as it could be is so central to human action. The book reviews the role of causality, causal models, and intervention in the basic human cognitive functions: decision making, reasoning, judgment, categorization, inductive inference, language, and learning. In short, the book offers a discussion about how people think, talk, learn, and explain things in causal terms, in terms of action and manipulation.

Editorial Reviews

Review

"Steven Sloman's Causal Models is the first broadly accessible book to survey an important and growing field of cognitive research: how people understand the causal structure of their world, and the role of causal understanding in all aspects of thinking, perceiving and acting. No difficult technical concepts are assumed. Important unifying themes are explained clearly and illustrated with numerous examples. It will provide an excellent entry into this field for students, researchers, or interested general readers." --Joshua B. Tenenbaum, Paul E. Newton Career Development Professor, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology

"In the last 15 years, there has been a quiet revolution in how we model, understand, and learn about the causal structure of the world. Having started in philosophy and computer science, but now vital in psychology and statistics, the causal revolution has been slowed by the conspicuous absence of a truly readable book-length introduction. Fortunately, Steve Sloman has now written one. In a book that includes all the key ideas behind causal modeling but none of the tedious technical details, hundreds of worked examples ranging from marketing to arithmetic, and dozens of applications ranging from how we categorize the world to how we might be evolved to learn about its causal structure, Sloman has made a difficult subject exciting and simple." --Richard Scheines, Professor of Philosophy, Carnegie Mellon University

"The scientific analysis of causal systems has become much more sophisticated with recent developments in computer science, statistics, and philosophy during the past decade. For the first time, we have available a comprehensive formal language in which to represent complex causal systems and which can be used to define normative solutions to causal inference and judgment problems. Steven Sloman's book makes these important developments easily accessible to the reader, as well as presenting many of his own exciting applications of these new ideas in behavioral studies of learning and judging causal relationships. This well-written book is full of profound insights and fascinating results. Anyone who wants to know what's going on at the cutting edge of cognitive science should read it." --Reid Hastie, Professor of Behavioral Science, University of Chicago

"The field of Bayesian causal models is becoming increasingly important for theory building in cognitive science. This book provides an lively and lucid introduction to the core concepts, and weaves them together with the latest research on causality and related topics from the cognitive sciences. Elegant and entertaining." --Nick Chater, Director of the Institute for Applied Cognitive Science and Professor of Psychology, University of Warwick

"Sloman has written an accessible, popular-level book that will serve as a useful general introduction to the tricky and complex issues involved in understanding causality and its role in cognitive processing. For people who are unfamiliar with the issues and the research involved, this is a good starting point, although parts may require thoughtful rereadings. For people who are generally familiar with the issues but not the recent research or theoretical conceptions (e.g., the use of counterfactuals), this book can serve as a useful guide to update one's knowledge. People who are actively working in this area will probably find this book a quick and enjoyable read."--Michael Palij, PsycCRITIQUES

About the Author

Steven Sloman has been on the faculty in Cognitive and Linguistic Sciences at Brown University since 1992. He completed his undergraduate studies at the University of Toronto in 1986 and received a Ph.D. in Psychology from Stanford in 1990. He has published many papers and a book about human cognition on topics ranging from categorization and memory to decision-making, inductive inference, and reasoning.

Product Details

• Hardcover: 224 pages
• Publisher: Oxford University Press, USA; 1 edition (July 28, 2005)
• Language: English
• ISBN-10: 0195183118
• ISBN-13: 978-0195183115
• Product Dimensions: 9.3 x 6.1 x 0.9 inches
• Shipping Weight: 1.1 pounds (View shipping rates and policies)
• Average Customer Review: 4.7 out of 5 stars  See all reviews (3 customer reviews)
• Amazon Best Sellers Rank: #1,236,185 in Books (See Top 100 in Books)

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9 of 9 people found the following review helpful:

5.0 out of 5 stars Excellent, February 5, 2011

By 

Dr. Lee D. Carlson (Baltimore, Maryland USA) - See all my reviews
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This review is from: Causal Models: How People Think About the World and Its Alternatives (Paperback)

The philosophical debate on the notion of causality has never been too much of a concern for scientists, particularly physicists who take a pragmatic attitude about cause and effect, and therefore do not get mired in the huge (and frequently useless) conceptual spaces constructed by philosophers and their apologists. The exception to this has been in some areas of theoretical physics, such as quantum mechanics and the physics of collapsed stars (black holes). In general though, it is probably fair to say that the scientific community has not been shaken by the arguments of philosophers such as David Hume, who supposedly have "demolished" some of the ideas on causality that are taken for granted by pre-Hume philosophers and the "general public."

The debate on how humans conceptualize causality and how they integrate their models of causality into decision-making however is of great interest to the scientific community, particularly psychologists and cognitive neuroscientists, who especially in the last two decades, have engaged in intensive research on this topic. A study of this research reveals that there is still a lot more to be done in this area, but what has been accomplished is impressive and fascinating. Those working in the field of artificial intelligence have taken some of these results and tried to integrate them into intelligent machines, with varying degrees of success.

For the most part, the author of this book has eschewed philosophical musings and has given the reader a view of conceptual models that is scientific and is currently in vogue in applied mathematics. Indeed, within its covers the reader will find discussions of possible worlds logic, Bayesian data modeling, and other techniques that are formulated in a framework that goes beyond the one developed in the 18th century (to paraphrase the author). The author is not shy about confronting some of the nagging issues behind how humans think about causality, but successfully avoids the trap of endless philosophical debate on the topic. Ironically though, his analysis draws on the work of some highly regarded philosophers, such as Peter Spirtes, Clark Glymour, and Richard Scheine. These philosophers have given excellent discussions of what are now called Bayesian belief networks, which have myriads of practical applications in areas such as financial and network modeling.

At least for this reviewer, the most interesting part of the book is how humans make decisions based on the causal models they develop, which as the author reminds the reader are usually based on qualitative evidence, frequently in error and fail to assess probabilities accurately (sometimes collectively called "cognitive bias"). This discussion is valuable for those readers who are actively involved in modeling real systems, both in applied and academic contexts. It sheds light for example on why managers of modeling groups insist on some sort of nontrivial time duration for the model execution, believing that to be viable a model must take an appreciable amount of time to complete in order to produce valid results. For those readers involved in models deploying discrete event simulation, it sheds light on why causal mechanisms are frequently imputed to these models, even though none can ever be found (these types of models avoid causal explanations by exhausting the realm of possibilities for the behavior of the modeled system using hypothetical randomized paths that the system may actually realize).

9 of 10 people found the following review helpful:

4.0 out of 5 stars Readable introduction into the modern understanding auf causality, April 25, 2010

By 

Juergen Kahrs (Bremen, Germany) - See all my reviews
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This review is from: Causal Models: How People Think About the World and Its Alternatives (Paperback)

This book consists of two parts: Part 1 is an introduction to concepts and terminology of causal models. Part 2 consists of chapters that apply the concepts to various domains of everyday life. All chapters are written from the point of view of a computer scientist with a strong interest in philosophical foundations and social sciences (psychology, cognition and law).

The author admits that his book contains nothing new, but is an introduction for those who don't feel well prepared for advanced literature like the well-known book Causality: Models, Reasoning and Inference by Judea Pearl. Such philosophical books are often hard to read, not because of the amount of facts to remember but because they carefully examine common terms like causality to such an extent that the reader may get puzzled. The author carefully addresses the basic terminology, concepts and ideas so that the reader will be prepared for reading other books.

What I liked about the book was the detailed explanation of such fundamental concepts as conditional probability and Bayes' rule. These are not as trivial as they might look when you see them as just one formula. I also liked the explanation of Causal models, Bayesian nets and the modern theory of intervention. I was also surprised to see how much of this is closely related to the philosophical basis of law.

Why do I rate this book at 4 stars (and not 5) ? I expected more of an undergraduate textbook with a more systematic introduction. As it is, the book is good for reading in the morning while driving to work on a bus, but not as a preparation for any courses. Other authors invest much more care into writing introductory textbooks (for example Consciousness: An Introduction) and reach an even higher level of entertainment while teaching along the lines of curricula.

1 of 1 people found the following review helpful:

5.0 out of 5 stars Awesome introduction to a intriguing topic., July 12, 2011

By 

Dan B "Dan" (Troy Michigan,) - See all my reviews

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This review is from: Causal Models: How People Think About the World and Its Alternatives (Paperback)

If you are at all interested in how we think, learn and interpret the world then read this book. It's a weighty subject, but the author brings about an ease of understanding. It'll still require some thought on your part, but it's well worth it. Very well worth the time and effort.