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
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Abstract:
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