Decision analysis: practice and promise
Management Science
Concave extensions for nonlinear 0–1 maximization problems
Mathematical Programming: Series A and B
Equivalent Representations of Set Functions
Mathematics of Operations Research
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
A differential approach to inference in Bayesian networks
Journal of the ACM (JACM)
A differential semantics for jointree algorithms
Artificial Intelligence
Sensitivity analysis in Bayesian networks: from single to multiple parameters
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Multiattribute Preference Analysis with Performance Targets
Operations Research
A Fictitious Play Approach to Large-Scale Optimization
Operations Research
Optimal Allocation of Risk-Reduction Resources in Event Trees
Management Science
A review of recent advances in global optimization
Journal of Global Optimization
When do numbers really matter?
Journal of Artificial Intelligence Research
A distance measure for bounding probabilistic belief change
International Journal of Approximate Reasoning
How to avoid a perfunctory sensitivity analysis
Environmental Modelling & Software
Untangling drivers of species distributions: Global sensitivity and uncertainty analyses of MaxEnt
Environmental Modelling & Software
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Risk managers are often confronted with the evaluation of operational policies in which two or more system components are simultaneously affected by a change. In these instances, the decision-making process should be informed by the relevance of interactions. However, because of system and model complexity, a rigorous study for determining whether and how interactions quantitatively impact operational choices has not been developed yet. In light of the central role played by the multilinearity of the decision support models, we investigate the presence of interactions in multilinear functions first. We identify interactions that can be a priori excluded from the analysis. We introduce sensitivity measures that apportion the model output change to individual factors and interaction contributions in an exact fashion. The sensitivity measures are linked to graphical representation methods as tornado diagrams and Pareto charts, and a systematic way of inferring managerial insights is presented. We then specialize the findings to reliability and probabilistic safety assessment (PSA) problems. We set forth a procedure for determining the magnitude of changes that make interactions relevant in the analysis. Quantitative results are discussed by application to a PSA model developed at NASA to support decision making in space mission planning and design. Numerical findings show that suboptimal decisions concerning the components on which to focus managerial attention can be made, if the decision-making process is not informed by the relevance of interactions.