Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/37467
Year of Publication: 
2010
Series/Report no.: 
Beiträge zur Jahrestagung des Vereins für Socialpolitik 2010: Ökonomie der Familie - Session: Asymmetric Information and Incentives No. A8-V3
Publisher: 
Verein für Socialpolitik, Frankfurt a. M.
Abstract: 
In this paper, we consider a decision-maker who tries to learn the distribution of outcomes from previously observed cases. For each observed database 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 ensures 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. The predicted probabilities, however, may vary with the number of observations. This generalisation of BILLOT, GILBOA, SAMET, AND SCHMEIDLER (2005) allows one to model learning processes.
Subjects: 
case-based decision theory
ambiguity
multiple priors
learning
similarity
JEL: 
D81
D83
D80
Document Type: 
Conference Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.