Accession Number:

ADA415623

Title:

Utilities as Random Variables: Density Estimation and Structure Discovery

Descriptive Note:

Conference paper

Corporate Author:

STANFORD UNIV CA DEPT OF COMPUTER SCIENCE

Personal Author(s):

Report Date:

2000-01-01

Pagination or Media Count:

9.0

Abstract:

Decision theory does not traditionally include uncertainty over utility functions. We argue that the a persons utility value for a given outcome can be treated as we treat other domain attributes as a random variable with a density function over its possible values. We show that we can apply statistical density estimation techniques to learn such a density function from a database of partially elicited utility functions. In particular, we define a Bayesian learning framework for this problem, assuming the distribution over utilities is a mixture of Gaussians, where the mixture components represent statistically coherent subpopulations. We can also extend our techniques to the problem of discovering generalized additivity structure in the utility functions in the population. We define a Bayesian model selection criterion for utility function structure and a search procedure over structures. The factorization of the utilities in the learned model, and the generalization obtained from density estimation, allows us to provide robust estimates of utilities using a significantly smaller number of utility elicitation questions. We experiment with our technique on synthetic utility data and on a real database of utility functions in the domain of prenatal diagnosis.

Subject Categories:

  • Medicine and Medical Research
  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE