  We introduce a new class of graphical representations, expected utility
networks (EUNs), and discuss some of its properties and potential applications
to artificial intelligence and economic theory. In EUNs not only probabilities,
but also utilities enjoy a modular representation. EUNs are undirected graphs
with two types of arc, representing probability and utility dependencies
respectively. The representation of utilities is based on a novel notion of
conditional utility independence, which we introduce and discuss in the context
of other existing proposals. Just as probabilistic inference involves the
computation of conditional probabilities, strategic inference involves the
computation of conditional expected utilities for alternative plans of action.
We define a new notion of conditional expected utility (EU) independence, and
show that in EUNs node separation with respect to the probability and utility
subgraphs implies conditional EU independence.
