Evaluating case-based decision theory: Predicting empirical patterns of human classification learning
Andreas Pape and
Kenneth J. Kurtz
Games and Economic Behavior, 2013, vol. 82, issue C, 52-65
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.
Keywords: Case-based decision theory; Human cognition; Learning; Agent-based computational economics; Psychology; Cognitive science (search for similar items in EconPapers)
JEL-codes: C63 C88 D83 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:gamebe:v:82:y:2013:i:c:p:52-65
Access Statistics for this article
Games and Economic Behavior is currently edited by E. Kalai
More articles in Games and Economic Behavior from Elsevier
Bibliographic data for series maintained by Catherine Liu ().