A new method for tackling asymmetric decision problems

Chain Event Graphs are probabilistic graphical models designed especially for the analysis of discrete statistical problems which do not admit a natural product space structure. We show here how they can be used for decision analysis through designation of some nodes as decision nodes, and the addition of utilities. We provide a local propagation algorithm for finding an optimal decision strategy and maximising expected utility. We also compare CEGs with Influence diagrams, Valuation Networks, Sequential decision diagrams, Sequential influence diagrams and Decision circuits for the representation and analysis of asymmetric decision problems.