Theory and Decision 48 (4):359-381 (2000)

Abstract
We propose a class [I,S] of loss functions for modeling the imprecise preferences of the decision maker in Bayesian Decision Theory. This class is built upon two extreme loss functions I and S which reflect the limited information about the loss function. We give an approximation of the set of Bayes actions for every loss function in [I,S] and every prior in a mixture class; if the decision space is a subset of R, we obtain the exact set
Keywords Bayesian Decision Theory  Global robustness  Loss function  Mixture class
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DOI 10.1023/A:1005212125699
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