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Heuristics and Biases: The Psychology of Intuitive Judgment 1st Edition

4.7 out of 5 stars 9 customer reviews
ISBN-13: 978-0521796798
ISBN-10: 0521796792
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Product Details

  • Paperback: 882 pages
  • Publisher: Cambridge University Press; 1 edition (July 8, 2002)
  • Language: English
  • ISBN-10: 0521796792
  • ISBN-13: 978-0521796798
  • Product Dimensions: 6 x 1.7 x 9 inches
  • Shipping Weight: 2.1 pounds (View shipping rates and policies)
  • Average Customer Review: 4.7 out of 5 stars  See all reviews (9 customer reviews)
  • Amazon Best Sellers Rank: #310,638 in Books (See Top 100 in Books)

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By Steven Peterson TOP 1000 REVIEWERVINE VOICE on December 22, 2007
Format: Paperback Verified Purchase
In 1982, Daniel Kahneman and Amos Tversky edited a volume, "Judgment under Uncertainty." This served as a culmination of their and others' research, bringing together in one volume a large number of reports on how humans make decisions under conditions of uncertainty. In short, they contended that under such conditions, people tend to use heuristics or decision-making shortcuts. This can lead to suboptimal decision-making.

Since, much research has built upon the earlier works. Indeed, there are now two streams in the research on heuristics--one fairly optimistic, exemplified by works of scholars such as Gerd Gigerenzer, and the other more pessimistic, exemplified by this particular volume, edited by Gilovich, Griffin, and Kahneman.

The introduction sets the stage for the myriad essays making up this book. The editors note in the Preface that (page xv): "The core idea of the heuristics and biases program is that judgment under uncertainty is often based on a limited small number of simplifying heuristics rather than more formal and extensive algorithmic processing. These heuristics typically yield accurate judgments but can lead to systematic error." The Introduction itself provides an historical overview of this line of work and notes some of the critiques of this body of research.

The individual essays themselves note some of the basic heuristics (or decision-making shortcuts). To illustrate: representativeness. Here, one takes a small number of cases and generalizes from these. E.g., oh, I knew a couple college basketball players and they were pretty dumb. Hence, one then generalizes and concludes that all basketball players are not so smart. In short, one generalizes from a poor sample.
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Format: Paperback Verified Purchase
This collection of articles has its origin in the work of one of the editors (Daniel Kahneman) and Amos Tversky (now deceased) in the 1970's. The first article in the book gives an introduction to this work and a brief historical survey. This work, along with current developments, is extremely important, for it sheds light on the differences (if any) between "intuitive judgment" and judgment that is based on more quantitative, mathematical, or algorithmic reasoning. If human judgment in uncertain environments is based on a limited number of simplifying heuristics, and not on extensive algorithmic processing, this would be very important for someone who is attempting to implement or simulate human reasoning in a machine. Economics, finance, and political decision-making are other areas that need a more accurate view of human judgment. Indeed, the "rational agent" assumption in classical economics, wherein the person makes choices by assessing the probability of each possible outcome and then assigning a utility to each, is considered to be fundamental, even axiomatic. It is therefore of great interest to examine challenges to this assumption.

In order to test the rational agent assumption, experiments must be conducted to test whether indeed the human assessment of likelihood and risk does indeed conform to the laws of probability. The data obtained in these experiments must then be judged as to whether it can be used to decide between the rational agent model and models of human judgment that are based on "intuition" (however vaguely or mystically this latter term is defined).

The authors of the first article in this book discuss some of the work on these questions, in particular the research that involved comparing expert clinical prediction with actuarial methods.
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Format: Paperback
This book is primarily a collection of key papers on heuristics and biases published during the two decades between 1982 and 2002, generally in the tradition of the work initiated by Tversky and Kahneman. The book essentially picks up where Judgment under Uncertainty: Heuristics and Biases left off. An introductory chapter also provides an historical overview of the topic and outlines the scope of the book.

The paper-based format of the book means that the content is relatively detailed and technical, with specifics of many experimental studies, and thus the book is oriented towards an academic audience rather than the general reader (for whom Thinking, Fast and Slow is a better choice). Likewise, the book is NOT a systematic textbook on heuristics and biases, so look elsewhere if that's your interest - almost no one will read this book cover to cover, nor is that generally necessary.

I give the book 5 stars for achieving its goals for its intended audience, and I hope this review is helpful in clarifying who that audience is.
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I based a whole module of my PhD in management on this book. The heuristics and biases described in this book can be turned into power conversations in management to affect decisions in real world situations. With my understanding of these concepts, I can now easily detect them in management discussions, which allows me the opportunity to clarify, amplify, and simplify decision making situations.
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Format: Kindle Edition
This collection of work makes for an excellent text book for students of cognitive psychology who wish to read and learn about the subject of heuristics and biases in human decision and judgment making. Heuristics and biases are common, predictable and robust phenomena which affect all manner of human functioning but their study can prove confusing for students. This book presents works by some of the world's leading scientists in this area in a clear and precise fashion, history of research, arguments for models and future research are all simply presented. Its a fine text.
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