A Note on Case-Based Optimization with a Non-Degenerate Similarity Function

Ani Guerdjikova ()
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Ani Guerdjikova: Cornell University, Postal: Uris Hall 462, Ithaca, NY 14853

No 04-46, Sonderforschungsbereich 504 Publications from Sonderforschungsbereich 504, Universität Mannheim, Sonderforschungsbereich 504, University of Mannheim

Abstract: The paper applies the ��realistic-ambitious�� rule for adaptation of the aspiration level suggested by Gilboa and Schmeidler (1996) to a situation in which the similarity between the available acts is represented by a non-degenerate function. The paper shows that the optimality result obtained by Gilboa and Schmeidler (1996) in general fails. With a concave similarity function, the best corner act is chosen in the limit. Introducing convex regions into the similarity function improves the limit choice. A sufficiently fine similarity function allows to approximate optimal behavior with an arbitrary degree of precision.

Pages: 14 pages
Date: 2004-11-24
Note: I am indebted to my advisor Juergen Eichberger for his helpful guidance and to Alexander Zimper for his helpful
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