EconPapers    
Economics at your fingertips  
 

Expected utility for nonstochastic risk

Victor Ivanenko and Illia Pasichnichenko

MPRA Paper from University Library of Munich, Germany

Abstract: The world of random phenomena exceeds the domain of the classical probability theory. In the general case the description of randomness requires a specific set of probability distributions (which is called statistical regularity) rather than a singe distribution. Such statistical regularity arises as a limit of relative frequencies. This approach to randomness allows to generalize the expected utility theory in order to cover the decision problems under nonstochastic random events. Applying the von Neumann-Morgenstern utility theorem, we derive the maxmin expected utility representation for statistical regularities. The derivation is based on the axiom of the preference for stochastic risk, i.e. the decision maker wishes to reduce the set of probability distributions to a single one.

Keywords: expected utility; risk; mass phenomena; statistical regularity; nonstochastic randomness; multiple prior (search for similar items in EconPapers)
JEL-codes: C10 D81 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-mic, nep-rmg and nep-upt
Date: 2016-04-01
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/70433/1/MPRA_paper_70433.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/75947/2/MPRA_paper_75947.pdf revised version (application/pdf)

Related works:
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: http://EconPapers.repec.org/RePEc:pra:mprapa:70433

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Series data maintained by Joachim Winter ().

 
Page updated 2017-01-05
Handle: RePEc:pra:mprapa:70433