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This paper focuses on a mixed-integer programming formulation for influencediagrams, based on a gradual rooted junction tree representation of thediagram. We show that different risk considerations, including chanceconstraints and conditional value-at-risk, can be incorporated into theformulation with targeted, appropriate modifications to the diagram structure.The computational performance of the formulation is assessed on two exampleproblems and is found to be highly dependent on the structure of the junctiontree.

Risk-averse decision strategies for influence diagrams using rooted junction trees