Different models with fuzzy random variables in single-stage decision problems

Abstract

In this paper we examine two different models using fuzzy random variables as the tool for dealing with single-stage decision problems with imprecise assessments of utilities. Both of them are oriented to prove the equivalence between normal and extensive forms of Bayesian analysis. The first model uses Fubini-type techniques to obtain the result whereas the second does not construct a product space and the result is obtained by different techniques. Addition of fuzzy-valued sample information is also considered. © Springer-Verlag Berlin Heidelberg 2010.