Multiple priors as similarity weighted frequencies
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...
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| Hauptverfasser: | , |
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| Dokumenttyp: | Buch/Monographie Arbeitspapier |
| Sprache: | Englisch |
| Veröffentlicht: |
Heidelberg
University of Heidelberg, Department of Economics
26 May 2008
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| Schriftenreihe: | Discussion paper series / Universität Heidelberg, Department of Economics
no. 470 |
| In: |
Discussion paper series (no. 470)
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| Schlagworte: | |
| Online-Zugang: | Resolving-System, Volltext: http://hdl.handle.net/10419/127289 Verlag, Volltext: http://www.awi.uni-heidelberg.de/with2/Discussion%20papers/papers/dp470.pdf 
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| Verfasserangaben: | Jürgen Eichberger and Ani Guerdjikova |
| Zusammenfassung: | 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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| Beschreibung: | Online Resource |
| Dokumenttyp: | Systemvoraussetzungen: Acrobat Reader. |