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

TL;DR

This paper introduces a symbolic algebraic method for computing expected utilities in influence diagrams, avoiding full numerical quantification by representing utilities as polynomial families and enabling efficient symbolic manipulation.

Contribution

It develops a novel symbolic algorithm for expected utility calculation in influence diagrams, generalizing decision asymmetries through polynomial transformations.

Findings

Expected utilities are represented as families of polynomials.

The symbolic algorithm efficiently propagates utilities without full quantification.

The approach generalizes influence diagram manipulations and decision asymmetries.

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.