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

2024/01/08 by Olli Herrala, Herrala, Olli, Topias Terho +3
Computer Science · #Bayesian Modeling and Causal Inference #FOS: Mathematics #Optimization and Control (math.OC)

paper · pdf · doi:10.48550/arxiv.2401.03734

openalex publication_date 2024/01/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Related

• From Influence Diagrams to Junction Trees
• Solving influence diagrams via efficient mixed-integer programming formulations and heuristics
• Myopic Value of Information in Influence Diagrams
• Integer programming on the junction tree polytope for influence diagrams
• Information/Relevance Influence Diagrams