Multiple Priors as Similarity Weighted Frequencies

Jürgen Eichberger and Ani Guerdjikova
Additional contact information
Ani Guerdjikova: ?

Working Papers from Cornell University, Center for Analytic 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 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.

Date: 2007-04
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://cae.economics.cornell.edu/07-03.pdf

Related works:
Working Paper: Multiple Priors as Similarity Weighted Frequencies (2008)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ecl:corcae:07-03

Access Statistics for this paper

More papers in Working Papers from Cornell University, Center for Analytic Economics Contact information at EDIRC.
Bibliographic data for series maintained by ().