A Graph-Theoretic Analysis of Information Value

Kim Leng Poh

Department of Industrial and Systems Engineering
National University of Singapore
Kent Ridge, Singapore 119260

Eric Horvitz

Decision Theory & Adaptive Systems Group
Microsoft Research
Redmond, WA 98052

Pdf or Postscript


We derive qualitative relationships about the informational relevance of variables in graphical decision models based on a consideration of the topology of the models. Specifically, we identify dominance relations for the expected value of information on chance variables in terms of their position and relationships in influence diagrams. The qualitative relationships can be harnessed to generate nonnumerical procedures for ordering uncertain variables in a decision model by their informational relevance.

Keywords: Expected value of information, graphical analysis, d-separation, qualitative reasoning, model refinement

In: Proceedings of Twelfth Conference on Uncertainty in Artificial Intelligence, Portland WA, August 1996. Morgan Kaufmann: San Francisco.

Author Email: isepohkl@leonis.nus.sg, horvitz@microsoft.com