Judgment under uncertainty heuristics and biases 1982 pdf
Kahneman Et Al. - 1982 - Judgment Under Uncertainty Heuristics and Biases
The Pitt Building, Trumpington Street. Spain Dock House. Subject to statutory errception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published Reprinted Many decisions are based on beliefs concerning the likelihood of uncertain events such as the outcome of an election, the guilt of a defendant.
Judgment Under Uncertainty: Heuristics and Biases is one of the foundational works on the flaws of human reasoning, and as such gets cited a lot on Less Wrong — but it's also rather long and esoteric, which makes it inaccessible to most Less Wrong users. Over the next few months, I'm going to attempt to distill the essence of the studies that make up the collection, in an attempt to convey the many interesting bits without forcing you to slog through the or so pages of the volume itself. By way of background: Judgment Under Uncertainty is a collection of 35 scientific papers and articles on how people make decisions with limited information, edited by Daniel Kahneman, Amos Tversky, and Paul Slovic. Kahneman and Tversky are the most recognizable figures in the area and the names most associated with the book, but only 12 of the studies are their work. It was first published in my version is from , and most studies were performed in the '70s — so note that this is not up-to-date research, and I can't say for sure what the current scientific consensus on the topic is. Judgement Under Uncertainty focuses on the divergence of human intuition from optimal reasoning, so it uses a lot of statistics and probability to define what's optimal.
Judgment Under Uncertainty : Heuristics and Biases. 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.