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 the light of the central role played by the multilinearity of the decision-support models, we investigate of 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 factors individual 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 sub-optimal 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.
A Study of Interactions in the Risk Assessment of Complex Engineering Systems: an Application to Space PSA
BORGONOVO, EMANUELE;
2011
Abstract
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 the light of the central role played by the multilinearity of the decision-support models, we investigate of 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 factors individual 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 sub-optimal 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.
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