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AbstractThe concept of "resilience analytics" has recently been proposed as a means to leverage the promise of big data to improve the resilience of interdependent critical infrastructure systems and the communities supported by them. Given recent advances in machine learning and other data‐driven analytic techniques, as well as the prevalence of high‐profile natural and man‐made disasters, the temptation to pursue resilience analytics without question is almost overwhelming. Indeed, we find big data analytics capable to support resilience to rare, situational surprises captured in analytic models. Nonetheless, this article examines the efficacy of resilience analytics by answering a single motivating question: Can big data analytics help cyber–physical–social (CPS) systems adapt to surprise? This article explains the limitations of resilience analytics when critical infrastructure systems are challenged by fundamental surprises never conceived during model development. In these cases, adoption of resilience analytics may prove either useless for decision support or harmful by increasing dangers during unprecedented events. We demonstrate that these dangers are not limited to a single CPS context by highlighting the limits of analytic models during hurricanes, dam failures, blackouts, and stock market crashes. We conclude that resilience analytics alone are not able to adapt to the very events that motivate their use and may, ironically, make CPS systems more vulnerable. We present avenues for future research to address this deficiency, with emphasis on improvisation to adapt CPS systems to fundamental surprise.

Abstract
Despite Federal directives calling for an integrated approach to strengthening the resilience of critical infrastructure systems, little is known about the relationship between human behavior and infrastructure resilience. While it is well recognized that human response can either amplify or mitigate catastrophe, the role of human or psychological resilience when infrastructure systems are confronted with surprise remains an oversight in policy documents and resilience research. Existing research treats human resilience and technological resilience as separate capacities that may create stress conditions that act upon one another. There remains a knowledge gap regarding study of those attributes in each that build infrastructure resilience as an integrated system of humans and technologies. This work draws on concepts found in the resilience engineering and psychology literature to examine the dynamic relationships between human resilience and the resilience of complex, socio-technical critical infrastructure systems. We identify and organize 18 system capacities and 23 human capacities that influence infrastructure resilience. We then correlate individual human and system resilience capacities to determine how each influences four socio-technical processes for resilience: sensing, anticipating, adapting, and learning. Our analysis shows that the human and technical resilience capacities reviewed are interconnected, interrelated, and interdependent. Further, we find current literature is focused more on cognitive and behavioral dimensions of human resilience and we offer ways to better incorporate affective capacities. Together, we present a simple way to link the resilience of technological systems to the cognitive, behavioral, and affective dimensions of humans responsible for the system design, operation, and management.

Decision analysis-a systematic approach to solving complex problems-offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate the safety and viability of potential substitutes for hazardous chemicals.Assess whether decision science may assist the alternatives analysis decision maker in comparing alternatives across a range of metrics.A workshop was convened that included representatives from government, academia, business, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups' findings.We conclude the further incorporation of decision science into alternatives analysis would advance the ability of companies and regulators to select alternatives to harmful ingredients, and would also advance the science of decision analysis.We advance four recommendations: (1) engaging the systematic development and evaluation of decision approaches and tools; (2) using case studies to advance the integration of decision analysis into alternatives analysis; (3) supporting transdisciplinary research; and (4) supporting education and outreach efforts.

Decision analysis-a systematic approach to solving complex problems-offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate the safety and viability of potential substitutes for hazardous chemicals. Assess whether decision science may assist the alternatives analysis decision maker in comparing alternatives across a range of metrics. A workshop was convened that included representatives from government, academia, business, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups' findings. We conclude the further incorporation of decision science into alternatives analysis would advance the ability of companies and regulators to select alternatives to harmful ingredients, and would also advance the science of decision analysis. We advance four recommendations: (1) engaging the systematic development and evaluation of decision approaches and tools; (2) using case studies to advance the integration of decision analysis into alternatives analysis; (3) supporting transdisciplinary research; and (4) supporting education and outreach efforts.