Order-of-Magnitude Influence Diagrams
This addresses the problem of modeling decisions with limited or vague data for researchers and practitioners in decision analysis, representing an incremental advancement in qualitative reasoning methods.
The paper tackles sequential decision-making under qualitative or imprecise information by developing a theory of influence diagrams based on order-of-magnitude approximations of probabilities and utilities, resulting in a dedicated variable elimination algorithm for solving such models.
In this paper, we develop a qualitative theory of influence diagrams that can be used to model and solve sequential decision making tasks when only qualitative (or imprecise) information is available. Our approach is based on an order-of-magnitude approximation of both probabilities and utilities and allows for specifying partially ordered preferences via sets of utility values. We also propose a dedicated variable elimination algorithm that can be applied for solving order-of-magnitude influence diagrams.