Solving decision problems with endogenous uncertainty and conditional information revelation using influence diagrams

Olli Herrala (), Tommi Ekholm () and Fabricio Oliveira ()
Additional contact information
Olli Herrala: Aalto University
Tommi Ekholm: Finnish Meteorological Institute
Fabricio Oliveira: Aalto University

Computational Management Science, 2025, vol. 22, issue 2, No 10, 37 pages

Abstract: Abstract Mathematical programming formulations of influence diagrams can bridge the gap between representing and solving decision problems. However, they suffer from both modeling and computational limitations. Aiming to address modeling limitations, we show how to incorporate conditionally observed information within the mathematical programming representation of the influence diagram. Multi-stage stochastic programming models use conditional non-anticipativity constraints to represent such uncertainties, and we show how such constraints can be incorporated into the influence diagram formulations. This allows us to consider the two main types of endogenous uncertainty simultaneously, namely decision-dependent information structure and decision-dependent probability distribution. Additionally, we apply a subdiagram decomposition to improve both computational efficiency and modeling capabilities. Under suitable conditions, this decomposition allows for considering continuous decision variables arising from, e.g., investment sizing decisions, leading to better solutions than a discretization of the continuous decisions. Finally, our proposed framework is illustrated with a large-scale cost-benefit problem regarding climate change mitigation, simultaneously considering technological research and development, and optimal emission trajectories.

Keywords: Influence diagrams; Endogenous uncertainty; Stochastic programming; Climate change mitigation (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10287-025-00543-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:comgts:v:22:y:2025:i:2:d:10.1007_s10287-025-00543-4

Ordering information: This journal article can be ordered from
http://www.springer. ... ch/journal/10287/PS2

DOI: 10.1007/s10287-025-00543-4

Access Statistics for this article

Computational Management Science is currently edited by Ruediger Schultz

More articles in Computational Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().