Graphical Analysis of Value of Information in Decision Models

May 2001

Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2001)

Volume

Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2001)

All Papers

We review and extend the qualitative relationships about the informational relevance of variables in graphical decision models based on conditional independencies revealed through graphical separations of nodes from nodes representing utility on outcomes. We exploit these qualitative relationships to generate non-numerical graphical procedures for identifying partial orderings over chance variables in a decision model in terms of their informational relevance. We describe an efficient algorithm based on a consideration of local properties of a property we refer to as u-separation. Finally, we present results of computational efficiencies gained via the application of the new policies, based on analyses of sample networks with different degrees of connectivity.