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by Steven Sloman

Download Causal Models: How People Think about the World and Its Alternatives fb2, epub

ISBN: 0195183118
Author: Steven Sloman
Language: English
Publisher: Oxford University Press; 1 edition (July 28, 2005)
Pages: 224
Category: Social Sciences
Subcategory: Other
Rating: 4.9
Votes: 605
Size Fb2: 1142 kb
Size ePub: 1531 kb
Size Djvu: 1854 kb
Other formats: docx lrf lit lrf


To understand how thought serves action requires understanding how people conceive of the relation between cause and effect.

To understand how thought serves action requires understanding how people conceive of the relation between cause and effect. Steven Sloman, Professor of Psychology, Brown University, 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 P. in Psychology from Stanford in 1990. He has published a book and many articles about human cognition on topics ranging from categorization and memory to decision making, inductive inference, and reasoning.

This book consists of two parts: Part 1 is an introduction to concepts and terminology of causal models. What I liked about the book was the detailed explanation of such fundamental concepts as conditional probability and Bayes' rule. 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). These are not as trivial as they might look when you see them as just one formula.

No difficult technical concepts are assumed.

Human beings are active agents who can think. Details (if other): Cancel. Thanks for telling us about the problem. Causal Models: How People Think about the World and Its Alternatives. 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.

Causal models: How people think about the world and its alternatives. DA Lagnado, MR Waldmann, Y Hagmayer, SA Sloman. Causal learning: Psychology, philosophy, and computation, 154-172, 2007. Political extremism is supported by an illusion of understanding. Oxford University Press, 2005. Twelve issues for cognitive science. Cognitive science 4 (1), 1-32, 1980. PM Fernbach, T Rogers, CR Fox, SA Sloman. Psychological science 24 (6), 939-946, 2013.

Home Browse Books Book details, Causal Models: How People Think about the . Publication year: 2005. Contributors: Steven Sloman. Subjects: cal Models.

Home Browse Books Book details, Causal Models: How People Think about the World. How do people think? The effort to answer this question is the domain of cognitive science, a field of study that includes cognitive psychology and parts of computer science, linguistics, anthropology, and philosophy. The field emerged in lock-step with the development of the computer.

oceedings{Sloman2005CausalM, title {Causal models : how people think about the .

oceedings{Sloman2005CausalM, title {Causal models : how people think about the world and its alternatives}, author {Steven A. Sloman}, year {2005} }. Steven A. Sloman.

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How People Think about the World. and Its Alternatives. Sloman, Steven A. Causal models : how people think about the world and its alternatives, by Steven Sloman. p. cm. Includes bibliographical references and index. ISBN-13 978-0-19-518311-5 ISBN 0-19-518311-8 1. cal models.

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.

Comments:

Tekasa
This book is dIvided into Parts I and II. Part I needs to be edited significantly.

But for Part I, this book would be a very good, nontechnical introduction of causal inference, but for the sometimes poor and convoluted language, unsuccessful arguments which place multiple examples within the same paragraph, and sometimes within the same sentence. The often convoluted English sentence structures are seemingly caused by misuse of prepositions while trying to put too much information into a sentence or paragraph.

The problem with the book is perhaps the author's strong focus to eliminate all mathematics, as a result he eliminates not only the mathematics but the mathematical concepts that underlie the logic.

"So because of the backdoor path between bacterial infection and peptic ulcer that goes through appetite, intervening on peptic ulcer does not render it independent of bacterial infection."

If you are already conversant with this material, it will make sense, but because this book is meant to be read and understood by non-mathematical laypersons who are trying to grasp this material for the first time, the above statement only poorly explains why it is true. There are many such examples in the book.

I think the book would be more effective if the author highlighted the key mathematical concepts laying them out like theorems but without the mathematics itself.

Part II of the book, in contrast to Part I, reads fluently.
Anarahuginn
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).
Dukinos
The general theme of Sloman's book is that causal structure plays a central (though not exclusive) role in human cognition. Sloman discusses both the basics of causal relationships and several real applications of those ideas. Since I share a (somewhat) similar view on cognition, I have no complaint with his objective. In fact, Sloman's book covers a wide range of important topics, minimizes technical jargon, and is impressively brief. As an introduction to causal relationships and their relationship to cognition, it is useful.

If you want something more than an introduction, however, the book is woefully inadequate. The evidence sections make minimal use of external research and, when they do, provide little or no substance from those sources. I am fortunate (from a critical standpoint at least) that I am familiar with essentially all of his cited sources. However, I am troubled that several of the simplifications flirt with inaccuracy. I found his comments about Michotte's research particularly notable:

"Michotte worked out in detail the conditions that lead people to see one moving object on a screen cause another to launch. For example, they must make contact; there must be no delay in launching after contact with a solid object, and so on." (p165)

These are ABSOLUTELY NOT the necessary conditions for the perception of causation and, in fact, aren't even the optimal conditions. For example, Michotte (1963, p94) found that a delay of 30 to 40ms between contact and launching provided a better causal impression than instantaneous motion.

Indeed, a significant part of Michotte's contribution to the field was the identification of a vast array of non-optimal conditions where the perception of causality still holds. This was a major reason that Michotte's (1963, p87) concludes that, "there is an actual perception of causality, in the same sense that there is a perception of shapes, movements, and so on". This appears to *support* Sloman's thesis so it's all the more surprising that his summary is so inadequate.

I gave the book 3 stars because Sloman's message is admirable and some readers will benefit from a non-technical introduction to the philosophy and theory behind causal relationships. However, most readers are forewarned of the general paucity of evidentiary support.

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