Directed expected utility networks

TL;DR

This paper introduces directed expected utility networks, a new graphical model that combines probabilistic and utility independences to improve decision-making efficiency in complex utility scenarios.

Contribution

The paper defines a novel graphical model that captures both probabilistic and utility independences, enabling more flexible and efficient decision analysis.

Findings

The new model generalizes influence diagrams for broader utility representations.

Transformations into tree structures facilitate faster expected utility computations.

Application demonstrated in household food security decision-making.

Abstract

A variety of statistical graphical models have been defined to represent the conditional independences underlying a random vector of interest. Similarly, many different graphs embedding various types of preferential independences, as for example conditional utility independence and generalized additive independence, have more recently started to appear. In this paper we define a new graphical model, called a directed expected utility network, whose edges depict both probabilistic and utility conditional independences. These embed a very flexible class of utility models, much larger than those usually conceived in standard influence diagrams. Our graphical representation, and various transformations of the original graph into a tree structure, are then used to guide fast routines for the computation of a decision problem's expected utilities. We show that our routines generalize those usually utilized in standard influence diagrams' evaluations under much more restrictive conditions. We then proceed with the construction of a directed expected utility network to support decision makers in the domain of household food security.