Coherence and correspondence in the psychological analysis of numerical predictions: How error-prone heuristics are replaced by ecologically valid heuristicsYoav Ganzach Judgment and Decision Making, 2009, vol. 4, issue 2, pages 175-185 Abstract: Numerical predictions are of central interest for both coherence-based approaches to judgment and decisions --- the Heuristic and Biases (HB) program in particular --- and to correspondence-based approaches --- Social Judgment Theory (SJT). In this paper I examine the way these two approaches study numerical predictions by reviewing papers that use Cue Probability Learning (CPL), the central experimental paradigm for studying numerical predictions in the SJT tradition, while attempting to look for heuristics and biases. The theme underlying this review is that both bias-prone heuristics and adaptive heuristics govern subjects' predictions in CPL. When they have little experience to guide them, subjects fall prey to relying on bias-prone natural heuristics, such as representativeness and anchoring and adjustment, which are the only prediction strategies available to them. But, as they acquire experience with the prediction task, these heuristics are abandoned and replaced by ecologically valid heuristics. Keywords: numerical prediction; social judgment theory; cue probability learning; heuristics and biases. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: http://EconPapers.repec.org/RePEc:jdm:journl:v:4:y:2009:i:2:p:175-185 Access Statistics for this article Judgment and Decision Making is edited by Jonathan Baron More articles in Judgment and Decision Making from Society for Judgment and Decision Making |
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