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Heuristic Decision Making

As reflected in the amount of controversy, few areas in psychology have undergone such dramatic conceptual changes in the past decade as the emerging science of heuristics. Heuristics are efficient cognitive processes, conscious or unconscious, that ignore part of the information. Because using heur... Full description

Journal Title: Annual review of psychology 2011, Vol.62 (1), p.451-482
Main Author: GIGERENZER, Gerd
Other Authors: GAISSMAIER, Wolfgang
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Alma/SFX Local Collection
Publisher: Palo Alto, CA: Annual Reviews
ID: ISSN: 0066-4308
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recordid: cdi_proquest_miscellaneous_815965305
title: Heuristic Decision Making
format: Article
creator:
  • GIGERENZER, Gerd
  • GAISSMAIER, Wolfgang
subjects:
  • Biological and medical sciences
  • Cognition & reasoning
  • Cognition. Intelligence
  • Cognitive models
  • Decision Making
  • Decision making. Choice
  • Fundamental and applied biological sciences. Psychology
  • Humans
  • Judgment
  • Models, Psychological
  • Problem Solving
  • Psychology
  • Psychology. Psychoanalysis. Psychiatry
  • Psychology. Psychophysiology
  • Social Perception
ispartof: Annual review of psychology, 2011, Vol.62 (1), p.451-482
description: As reflected in the amount of controversy, few areas in psychology have undergone such dramatic conceptual changes in the past decade as the emerging science of heuristics. Heuristics are efficient cognitive processes, conscious or unconscious, that ignore part of the information. Because using heuristics saves effort, the classical view has been that heuristic decisions imply greater errors than do "rational" decisions as defined by logic or statistical models. However, for many decisions, the assumptions of rational models are not met, and it is an empirical rather than an a priori issue how well cognitive heuristics function in an uncertain world. To answer both the descriptive question ("Which heuristics do people use in which situations?") and the prescriptive question ("When should people rely on a given heuristic rather than a complex strategy to make better judgments?"), formal models are indispensable. We review research that tests formal models of heuristic inference, including in business organizations, health care, and legal institutions. This research indicates that (a) individuals and organizations often rely on simple heuristics in an adaptive way, and (b) ignoring part of the information can lead to more accurate judgments than weighting and adding all information, for instance for low predictability and small samples. The big future challenge is to develop a systematic theory of the building blocks of heuristics as well as the core capacities and environmental structures these exploit.
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 0066-4308
fulltext: fulltext
issn:
  • 0066-4308
  • 1545-2085
url: Link


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descriptionAs reflected in the amount of controversy, few areas in psychology have undergone such dramatic conceptual changes in the past decade as the emerging science of heuristics. Heuristics are efficient cognitive processes, conscious or unconscious, that ignore part of the information. Because using heuristics saves effort, the classical view has been that heuristic decisions imply greater errors than do "rational" decisions as defined by logic or statistical models. However, for many decisions, the assumptions of rational models are not met, and it is an empirical rather than an a priori issue how well cognitive heuristics function in an uncertain world. To answer both the descriptive question ("Which heuristics do people use in which situations?") and the prescriptive question ("When should people rely on a given heuristic rather than a complex strategy to make better judgments?"), formal models are indispensable. We review research that tests formal models of heuristic inference, including in business organizations, health care, and legal institutions. This research indicates that (a) individuals and organizations often rely on simple heuristics in an adaptive way, and (b) ignoring part of the information can lead to more accurate judgments than weighting and adding all information, for instance for low predictability and small samples. The big future challenge is to develop a systematic theory of the building blocks of heuristics as well as the core capacities and environmental structures these exploit.
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subjectBiological and medical sciences ; Cognition & reasoning ; Cognition. Intelligence ; Cognitive models ; Decision Making ; Decision making. Choice ; Fundamental and applied biological sciences. Psychology ; Humans ; Judgment ; Models, Psychological ; Problem Solving ; Psychology ; Psychology. Psychoanalysis. Psychiatry ; Psychology. Psychophysiology ; Social Perception
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abstractAs reflected in the amount of controversy, few areas in psychology have undergone such dramatic conceptual changes in the past decade as the emerging science of heuristics. Heuristics are efficient cognitive processes, conscious or unconscious, that ignore part of the information. Because using heuristics saves effort, the classical view has been that heuristic decisions imply greater errors than do "rational" decisions as defined by logic or statistical models. However, for many decisions, the assumptions of rational models are not met, and it is an empirical rather than an a priori issue how well cognitive heuristics function in an uncertain world. To answer both the descriptive question ("Which heuristics do people use in which situations?") and the prescriptive question ("When should people rely on a given heuristic rather than a complex strategy to make better judgments?"), formal models are indispensable. We review research that tests formal models of heuristic inference, including in business organizations, health care, and legal institutions. This research indicates that (a) individuals and organizations often rely on simple heuristics in an adaptive way, and (b) ignoring part of the information can lead to more accurate judgments than weighting and adding all information, for instance for low predictability and small samples. The big future challenge is to develop a systematic theory of the building blocks of heuristics as well as the core capacities and environmental structures these exploit.
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