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