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Judgment under Uncertainty: Heuristics and Biases

The paper describes three heuristics, or mental operations, that are employed in judgment under uncertainty. The first is an assessment of representativeness or similarity which is usually performed when people are asked to judge the likelihood that an object or event A belongs to a class or process B. The second is an assessment of the availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development. The third is an adjustment from a starting point, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgments and decisions in situations of uncertainty. (Modified author abstract).

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  • "Judgement under uncertainty"@en

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  • "The paper describes three heuristics, or mental operations, that are employed in judgment under uncertainty. The first is an assessment of representativeness or similarity which is usually performed when people are asked to judge the likelihood that an object or event A belongs to a class or process B. The second is an assessment of the availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development. The third is an adjustment from a starting point, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgments and decisions in situations of uncertainty. (Modified author abstract)."@en
  • "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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  • "Electronic books"
  • "Aufsatzsammlung - Entscheidung bei Unsicherheit - Beurteilung"
  • "Proceedings"
  • "Aufsatzsammlung"
  • "Zborniki"

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  • "Judgment under Uncertainty: Heuristics and Biases"@en
  • "Judgment under Uncertainty : heuristics and biases"
  • "Judgment under uncertainty: Heuristics and biases"
  • "Judgement under uncertainity : Heuristics and biases"@en
  • "Judgment under uncertainty: heuristics and biases"
  • "Judgement under uncertainty: heuristics and biases"
  • "Judgment under uncertainty : heuristics and biases"
  • "Judgment under uncertainty : heuristics and biases"@en
  • "Judgement under uncertainty : heuristics and biases"
  • "Judgement under uncertainty : heuristics and biases"@en
  • "Judgment under uncertainty heuristics and biases"

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