Directed expected utility networks
  A variety of statistical graphical models have been defined to represent the
conditional independences underlying a random vector of interest. Similarly,
many different graphs embedding various types of preferential independences, as
for example conditional utility independence and generalized additive
independence, have more recently started to appear. In this paper we define a
new graphical model, called a directed expected utility network, whose edges
depict both probabilistic and utility conditional independences. These embed a
very flexible class of utility models, much larger than those usually conceived
in standard influence diagrams. Our graphical representation, and various
transformations of the original graph into a tree structure, are then used to
guide fast routines for the computation of a decision problem's expected
utilities. We show that our routines generalize those usually utilized in
standard influence diagrams' evaluations under much more restrictive
conditions. We then proceed with the construction of a directed expected
utility network to support decision makers in the domain of household food
security.
