Document Detail


Judgments under uncertainty: representativeness or potential surprise?
MedLine Citation:
PMID:  12519527     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Tversky and Kahneman's (1983) account of conjunctive probability judgment in terms of the representativeness heuristic is questioned. Instead, potential surprise (Shackle, 1969) is proposed as an important mechanism underlying subjective probability judgment. Study 1 reveals that, consistent with Shackle's theory, probabilities assigned to conjunctions are predominantly determined by the magnitude of the smaller component event probability. Also consistent with Shackle, Study 2 shows that for disjunctions, this role is performed by the larger component. Study 3 again contrasts the relative roles played by the component events in determining the value assigned to the conjunction. The results of the study are consistent with two reasoning processes: one analytically based, in which due account is taken of both component events, the other heuristic in nature and consistent with Shackle's theory of potential surprise. The implications of these results for a range of different types of judgment, e.g. with regard to person perception, stereotyping, categorization, and typicality, are evaluated.
Authors:
John E Fisk
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  British journal of psychology (London, England : 1953)     Volume:  93     ISSN:  0007-1269     ISO Abbreviation:  Br J Psychol     Publication Date:  2002 Nov 
Date Detail:
Created Date:  2003-01-09     Completed Date:  2003-04-04     Revised Date:  2004-11-17    
Medline Journal Info:
Nlm Unique ID:  0373124     Medline TA:  Br J Psychol     Country:  England    
Other Details:
Languages:  eng     Pagination:  431-49     Citation Subset:  IM    
Affiliation:
Centre for Applied Psychology, Liverpool John Moores University, UK. j.e.fisk@livjm.ac.uk
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MeSH Terms
Descriptor/Qualifier:
Adult
Humans
Judgment*
Problem Solving
Psychological Theory
Questionnaires
Random Allocation
Regression Analysis

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