Case-based learning with different similarity functions

Ani Guerdjikova

Games and Economic Behavior, 2008, vol. 63, issue 1, 107-132

Abstract: The paper applies the rule for adaptation of the aspiration level suggested by Gilboa and Schmeidler to a situation in which the similarity between acts is represented by an arbitrary similarity function [Gilboa, I., Schmeidler, D., 1996. Case-based optimization. Games Econ. Behav. 15, 1-26]. I show that the optimality result derived by Gilboa and Schmeidler in general fails. With a concave similarity function, only corner acts are chosen in the limit. The optimality result can be reestablished by introducing convex regions into the similarity function and modifying the aspiration adaptation rule. A similarity function which is "sufficiently convex" allows approximating optimal behavior with an arbitrary degree of precision.

Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0899-8256(07)00160-1
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:gamebe:v:63:y:2008:i:1:p:107-132

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 ().