Multiple Priors as Similarity Weighted Frequencies
AbstractIn this paper, we consider a decision-maker who tries to learn the distribution of outcomes from previously observed cases. For each observed sequence of cases, the decision-maker entertains a set of priors expressing his hypotheses about the underlying probability distribution. The set of probability distributions shrinks when new information confirms old data. We impose a version of the concatenation axiom introduced in BILLOT, GILBOA, SAMET AND SCHMEIDLER (2005) which insures that the sets of priors can be represented as a weighted sum of the observed frequencies of cases. The weights are the uniquely determined similarities between the observed cases and the case under investigation.
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Bibliographic InfoPaper provided by Cornell University, Center for Analytic Economics in its series Working Papers with number 07-03.
Date of creation: Apr 2007
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Other versions of this item:
- Jürgen Eichberger & Ani Guerdjikova, 2008. "Multiple Priors as Similarity Weighted Frequencies," Working Papers 0470, University of Heidelberg, Department of Economics, revised Jul 2008.
- Eichberger, Jürgen & Guerdjikova, Ani, 2008. "Multiple Priors as Similarity Weighted Frequencies," Sonderforschungsbereich 504 Publications 08-07, Sonderforschungsbereich 504, Universität Mannheim & Sonderforschungsbereich 504, University of Mannheim.
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