1 research outputs found
Decision making with decision event graphs
We introduce a new modelling representation, the Decision Event Graph (DEG), for asymmetric
multistage decision problems. The DEG explicitly encodes conditional independences
and has additional significant advantages over other representations of asymmetric decision
problems. The colouring of edges makes it possible to identify conditional independences on
decision trees, and these coloured trees serve as a basis for the construction of the DEG.
We provide an efficient backward-induction algorithm for finding optimal decision rules on
DEGs, and work through an example showing the efficacy of these graphs. Simplifications of
the topology of a DEG admit analogues to the sufficiency principle and barren node deletion
steps used with influence diagrams