44 research outputs found
Expected Utility Networks
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.Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999
I Don't Want to Think About it Now:Decision Theory With Costly Computation
Computation plays a major role in decision making. Even if an agent is
willing to ascribe a probability to all states and a utility to all outcomes,
and maximize expected utility, doing so might present serious computational
problems. Moreover, computing the outcome of a given act might be difficult. In
a companion paper we develop a framework for game theory with costly
computation, where the objects of choice are Turing machines. Here we apply
that framework to decision theory. We show how well-known phenomena like
first-impression-matters biases (i.e., people tend to put more weight on
evidence they hear early on), belief polarization (two people with different
prior beliefs, hearing the same evidence, can end up with diametrically opposed
conclusions), and the status quo bias (people are much more likely to stick
with what they already have) can be easily captured in that framework. Finally,
we use the framework to define some new notions: value of computational
information (a computational variant of value of information) and and
computational value of conversation.Comment: In Conference on Knowledge Representation and Reasoning (KR '10
On the decomposition of Generalized Additive Independence models
The GAI (Generalized Additive Independence) model proposed by Fishburn is a
generalization of the additive utility model, which need not satisfy mutual
preferential independence. Its great generality makes however its application
and study difficult. We consider a significant subclass of GAI models, namely
the discrete 2-additive GAI models, and provide for this class a decomposition
into nonnegative monotone terms. This decomposition allows a reduction from
exponential to quadratic complexity in any optimization problem involving
discrete 2-additive models, making them usable in practice