  In this paper, we first consider a Bayesian framework and model the "utility
function" in terms of fuzzy random variables. On the basis of this model, we
define the "prior (fuzzy) expected utility" associated with each action, and
the corresponding "posterior (fuzzy) expected utility given sample information
from a random experiment". The aim of this paper is to analyze how sample
information can affect the expected utility. In this way, by using some fuzzy
preference relations, we conclude that sample information allows a decision
maker to increase the expected utility on the average. The upper bound on the
value of the expected utility is when the decision maker has perfect
information. Applications of this work to the field of artificial intelligence
are presented through two examples.
