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

Abstract

This paper presents how a mixed-integer programming (MIP) formulation for influence diagrams, based on a gradual rooted junction tree representation of the diagram, can be generalized to incorporate risk considerations such as conditional value-at-risk and chance constraints. We present two algorithms on how targeted modifications can be made to the underlying influence diagram or to the gradual rooted junction tree representation to enable our reformulations. We present computational results comparing our reformulation with another MIP formulation for influence diagrams.