Judgment under Uncertainty: Heuristics and Biases PDF

Rating: (10 reviews) Author: Daniel Kahneman ISBN : 9780521284141 New from $55.79 Format: PDF

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The thirty-five chapters in this book describe various judgmental heuristics and the biases they produce, not only in laboratory experiments but in important social, medical, and political situations as well. Individual chapters discuss the representativeness and availability heuristics, problems in judging covariation and control, overconfidence, multistage inference, social perception, medical diagnosis, risk perception, and methods for correcting and improving judgments under uncertainty. About half of the chapters are edited versions of classic articles; the remaining chapters are newly written for this book. Most review multiple studies or entire subareas of research and application rather than describing single experimental studies. This book will be useful to a wide range of students and researchers, as well as to decision makers seeking to gain insight into their judgments and to improve them.

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• Paperback: 544 pages
• Publisher: Cambridge University Press; 1 edition (April 30, 1982)
• Language: English
• ISBN-10: 0521284147
• ISBN-13: 978-0521284141
• Product Dimensions: 1 x 5.9 x 8.8 inches
• Shipping Weight: 1.7 pounds (View shipping rates and policies)

Judgment under Uncertainty: Heuristics and Biases PDF

In this volume Daniel Kahneman and the late Amos Tversky gathered together 35 authoritative papers that demonstrate through well-designed experiments and through observation the hard-wired biases and heuristics that influence (or define) the way humans go about making choices when the outcomes are from certain.

There are a raft of biases, and just one example is the Anchoring Effect. If you asked 100 people to guess the population of Turkey, what you'd probably get is a wide range of answers. If you broke the question into two parts: first by asking whether the population is higher or lower than 14 million - and then by asking the respondents to guess the population - you'd find that the answers would gravitate around our arbitrary 14 million mark.

The Heuristics we use to weigh up and evaluate data provide a second family of biases. Here, the human brain is shown to go about problem evaluation along certain pathways and shortcuts, and the route we take tends to define where we'll emerge. By way of example, we tend to give undue weight to highly retrievable or available data: and treat this as representative. So in the wake of Katrina, you or I would be fairly excused for judging 2005 as a particularly bad year for global weather-related disasters. In probability, 2005 was not particularly unusual on a global scale.

This volume is an important collection of papers, with relevance to anyone working in fields where decision-making is at the core. You might be in market research, medicine, social sciences, economics or other fields: this book contains material of direct relevance to your work. The conclusions from the papers range from disturbing (the judgments of professional medical and psychological experts, we see, can be alarmingly biased!