A symbolic algebra for the computation of expected utilities in multiplicative influence diagrams

Abstract

Influence diagrams provide a compact graphical representation of decision problems. Several algorithms for the quick computation of their associated expected utilities are available in the literature. However, often they rely on a full quantification of both probabilistic uncertainties and utility values. For problems where all random variables and decision spaces are finite and discrete, here we develop a symbolic way to calculate the expected utilities of influence diagrams that does not require a full numerical representation. Within this approach expected utilities correspond to families of polynomials. After characterizing their polynomial structure, we develop an efficient symbolic algorithm for the propagation of expected utilities through the diagram and provide an implementation of this algorithm using a computer algebra system. We then characterize many of the standard manipulations of influence diagrams as transformations of polynomials. We also generalize the decision analytic framework of these diagrams by defining asymmetries as operations over the expected utility polynomials.

Sensitivity analysis beyond linearity

“…Most of the above-mentioned results, although specifically derived for BNs, hold for a variety of models whose atomic probabilities can be written as a multilinear polynomial (Leonelli et al, 2017a). The multilinear structure of atomic probabilities in BNs has been known for quite some time (Castillo et al, 1995;Darwiche, 2003), but other models entertain the same property under specific parametrisations, for instance stratified staged trees (Görgen et al, 2015), context-specific BNs (Boutilier et al, 1996) and influence diagrams (Leonelli et al, 2017b).…”