Jürgen Eichberger () (University of Heidelberg, Department of Economics) Ani Guerdjikova () (Cornell University, Department of Economics)
Abstract
In 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 predicts a set of priors expressing his beliefs about the underlying probability distribution. 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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Publisher Info
Paper provided by University of Heidelberg, Department of Economics in its series Working Papers with number
0470.
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
Larry Epstein & Martin Schneider, 2006.
"Learning Under Ambiguity,"
RCER Working Papers
527, University of Rochester - Center for Economic Research (RCER).
[Downloadable!]
Other versions:
Larry Epstein & Martin Schneider, 2002.
"Learning Under Ambiguity,"
RCER Working Papers
497, University of Rochester - Center for Economic Research (RCER), revised Mar 2005.
[Downloadable!]
Klaus Nehring & Clemens Puppe, 2002.
"A Theory of Diversity,"
Econometrica,
Econometric Society, vol. 70(3), pages 1155-1198, May.
[Downloadable!] (restricted)