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Toward adaptive decision support for assessing infrastructure system resilience using hidden performance measures


The understanding of resilience is an emerging topic within the study of risks affecting distributed infrastructure systems. Although recent studies have explored the quantification of system resilience, there has been limited research aimed at understanding the role of multiple performance measures, spatiotemporal heterogeneities, and modeling uncertainties within the assessment of resilience and associated decision-making. Under real-world conditions, there is an increased burden on analysts for translating observed system data (including human and electronic sensor observations) into system performance estimates that may not be directly observable. This paper addresses these issues using a scenario-based risk modeling approach to understand: (1) resilience of complex systems, often in cases of hidden (not readily observable) measures of performance, (2) resilience sensitivity to modeling uncertainties in event and system characteristics, and (3) resilience sensitivity to the measurement of performance across multiple operational perspectives. The methods in this paper integrate uncertainty-driven risk and probabilistic modeling within a multi-state Markov-based approach. This study contributes to the state-of-the-art by developing methodologies for assessing community perceptions of infrastructure system resilience using observable factors and inferring possibly hidden performance measures for facilitating adaptive decision-support. The methods are demonstrated with hypothetical spatiotemporal data across multiple system performance dimensions. The analysis results are useful for infrastructure security analysts, system decision-makers, and the general public.

Keywords: Infrastructure system resilience, risk management, decision support, Markov models


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