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


  • Guerdjikova, Ani

    () (Cornell University)


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.

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  • Guerdjikova, Ani, 2004. "A Note on Case-Based Optimization with a Non-Degenerate Similarity Function," Sonderforschungsbereich 504 Publications 04-46, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
  • Handle: RePEc:xrs:sfbmaa:04-46
    Note: I am indebted to my advisor Juergen Eichberger for his helpful guidance and to Alexander Zimper for his helpful

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