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The Natural Selection of Prediction Heuristics: Anchoring and Adjustment versus Representativeness
Author(s) -
Czaczkes Benjamin,
Ganzach Yoav
Publication year - 1996
Publication title -
journal of behavioral decision making
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 76
eISSN - 1099-0771
pISSN - 0894-3257
DOI - 10.1002/(sici)1099-0771(199606)9:2<125::aid-bdm221>3.0.co;2-7
Subject(s) - representativeness heuristic , anchoring , heuristics , salience (neuroscience) , computer science , econometrics , psychology , social psychology , artificial intelligence , economics , operating system
There are several heuristics which people use in making numerical predictions and these heuristics compete for the determination of prediction output. Some of them (e.g. representativeness) lead to excessively extreme predictions while others (e.g. anchoring and adjustment) lead to regressive (and even over‐regressive) predictions. In this paper we study the competition between these two heuristics by varying the representation of predictor and outcome. The results indicate that factors which facilitate reliance on representativeness (e.g. compatibility between predictor and outcome) indeed lead to an increase in extremity, while factors that facilitate reliance on anchoring and adjustment (e.g. increased salience of a potential anchor) lead to a decrease in extremity.