Directional decomposition of multiattribute utility functions

Ronen I. Brafman, Yagil Engel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


Several schemes have been proposed for compactly representing multiattribute utility functions, yet none seems to achieve the level of success achieved by Bayesian and Markov models for probability distributions. In an attempt to bridge the gap, we propose a new representation for utility functions which follows its probabilistic analog to a greater extent. Starting from a simple definition of marginal utility by utilizing reference values, we define a notion of conditional utility which satisfies additive analogues of the chain rule and Bayes rule. We farther develop the analogy to probabilities by describing a directed graphical representation that relies on our concept of conditional independence. One advantage of this model is that it leads to a natural structured elicitation process, very similar to that of Bayesian networks.

Original languageEnglish
Title of host publicationAlgorithmic Decision Theory - First International Conference, ADT 2009, Proceedings
Number of pages11
StatePublished - 14 Dec 2009
Event1st International Conference on Algorithmic Decision Theory, ADT 2009 - Venice, Italy
Duration: 20 Oct 200923 Oct 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5783 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference1st International Conference on Algorithmic Decision Theory, ADT 2009

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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