Evaluating case-based decision theory: Predicting empirical patterns of human classification learning

We introduce a computer program which calculates an agentʼs optimal behavior according to case-based decision theory (Gilboa and Schmeidler, 1995) and use it to test CBDT against a benchmark set of problems from the psychological literature on human classification learning (Shepard et al., 1961). This allows us to evaluate the efficacy of CBDT as an account of human decision-making on this set of problems.

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Year of publication:	2013
Authors:	Pape, Andreas Duus ; Kurtz, Kenneth J.
Published in:	Games and Economic Behavior. - Elsevier, ISSN 0899-8256. - Vol. 82.2013, C, p. 52-65
Publisher:	Elsevier
Subject:	Case-based decision theory \| Human cognition \| Learning \| Agent-based computational economics \| Psychology \| Cognitive science

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Type of publication:	Article
Classification:	D83 - Search, Learning, Information and Knowledge ; C63 - Computational Techniques ; C88 - Other Computer Software
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10011049853