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Measurable Resilience for Actionable Policy Igor Linkov,†,* Daniel A. Eisenberg,‡ Matthew E. Bates,† Derek Chang,‡ Matteo Convertino,‡,▽ Julia H. Allen,∥ Stephen E. Flynn,⊥ and Thomas P. Seager# †
Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, Mississippi 39180-6199, United States ‡ Contractor to the Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, Mississippi 39180-6199, United States § Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States ∥ CERT Program, Software Engineering Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15289, United States ⊥ Department of Political Science, Kostas Research Institute for Homeland Security, Northeastern University, Boston, Massachusetts 02115-5000, United States # School of Engineering and the Built Environment, Arizona State University, Tempe, Arizona 85287, United States (iv) adaptation.1 Nevertheless, quantitative approaches to resilience in the context of system processes have neglected to combine those aspects of the NAS understanding that focus on management processes (i.e., planning/preparation and adaptation) with those that focus on performance under extreme loadings or shocks (i.e., absorption and recovery). Advancing the fundamental understanding and practical application of resilience requires greater attention to the development of resilience process metrics, as well as comparison of resilience approaches in multiple engineering contexts for the purpose of extracting generalizable principles. There are at least two important obstacles that have inhibited progress in resilience measurement for complex systems. The first of these is the success of quantitative risk assessment as the dominant paradigm for system design and management. In infrastructure and disaster management, pervasive concepts of risk have encroached upon the understanding of resilience.4 However, resilience has a broader purview than risk and is essential when risk is incomputable, such as when hazardous conditions are a complete surprise or when the risk analytic paradigm has been proven ineffective. Therefore, resilience measurement must be advanced with novel analytic approaches nprecedented losses associated with adverse events such that are complementary to, but readily distinguishable from, as natural disasters and cyber-attacks have focused those already identified with risk analysis. attention on new approaches to mitigating damages. Whereas The second of these obstacles is the fragmentation of the dominant analytic and governance paradigm of the last resilience knowledge into separate disciplines, including several decades has been risk analysis, recently rhetoric has engineering infrastructure, environmental management, and shifted toward the necessity of understanding and designing for cybersecurity. This balkanized approach will inevitably fail to resilience.1,2 Despite it being an established feature of meet national resilience goals to manage “all hazards” to critical sustainable technological, ecological, and sociological systems,3 cyber infrastructure and associated systems 2 by supporting planned resilience still requires metrics that are both precise to only incremental changes to known risks. Such an ambitious measure individual system qualities and generalizable to inform policy objective requires a generalizable approach that is both resource allocation and operations. To date, the failure to applicable to a diverse array of systems and revealing of their understand resilience in the context of these complex systems interconnectivity. has precluded the creation of an actionable metrics framework to inform resilience decisions. A recent National Academy of Sciences (NAS) report on Received: August 2, 2013 “disaster resilience” defines resilience as the ability of a system Revised: August 12, 2013 to perform four functions with respect to adverse events: (i) Accepted: August 16, 2013 Published: September 3, 2013 planning and preparation, (ii) absorption, (iii) recovery, and
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© 2013 American Chemical Society
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Figure 1. The Resilience Matrix, mapping of system domains across an event management cycle of resilience functions. Cells in this matrix provide guidelines for resilience metrics that need to be developed and combined to measure overall system resilience.
locus of meaning, where people make sense of the data accessed from the information domain. Therefore, the cognitive and social domains contain the learning processes that enable continuous improvement of the system state (e.g., the physical domain) and future planning/preparation. Each cell of the resilience matrix describes what is important for developing application-specific quantitative and qualitative measures of each NAS function. Thus, the resilience matrix supports a transparent connection between resilience policies and potential outcomes. As each cell directs the creation, collection, and combination of metrics, they also offer a guideline to develop the objectives of systems designers. For example, postcatastrophe lessons learned programs may improve adaptation functions by strengthening cross-domain learning. Moreover, the resilience matrix approach provides a framework for cross-comparability of metrics from different disciplines that reside in the same matrix cells. Therefore, the matrix approach will help both designers focused on resilience performance and emergency managers focused on resilience management to adopt a more holistic view of resilience necessary to reduce the impact of an adverse event. Taken together, the expansion and implementation of this framework will provide actionable and specific guidance for implementing national policy goals and improving system resilience across many disciplines.
Although disaster resilience may be understood as a challenge for civil society, advances in military theory are directly applicable. Both warfare and disaster resilience are characterized by surprise, complexity, urgency and the necessity of adaptation. In response, military scholars have proposed the doctrine of Network Centric Warfare (NCW), which is focused on creating shared situational awareness and decentralized decision-making by distributing information across networks operating in physical, information, cognitive, and social domains:5 Physical: sensors, facilities, equipment, system states and capabilities. Information: creation, manipulation, and storage of data. Cognitive: understanding, mental models, preconceptions, biases, and values. Social: interaction, collaboration and self-synchronization between individuals and entities. Current approaches extant in the academic literature treat each of these as separate domains of study, yet complex systems must be governed by understanding the management and performance dynamics that connect each domain. To understand how NCW doctrine applies to disaster resilience, Figure 1 maps each domain against the event management cycle defined by the NAS. For example, weather radar and other sensors in the physical domain generate information that facilitates anticipation and forecasting of tornadoes. However, if data is not also available to describe the vulnerability of physical infrastructure, even accurate forecasts may not be actionable or meaningful in the cognitive domain. Also, if knowledge sharing in the social domain (such as information about the location of tornado shelters, or the status of loved ones) is limited, coordination of responses may be ineffective at best or counterproductive at worst. The cognitive and social domains are the
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected] Virginia Rd., Concord, MA 01742, USA. 10109
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Present Address ▽
M.C.: Currently at Virginia Polytechnic Institute and State University. Author Contributions
The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS We acknowledge Todd Bridges and Martin Schultz of the U.S. Army Corps of Engineers, Celia Merzbacher of the Semiconductor Research Corporation, and James Mancillas and John McDonagh of the Army Environmental Command COL Paul Roege, US Army for their help and support. Permission was granted by the Chief of Engineers, U.S. Army Corps of Engineers to publish this material.This study was funded by the U.S. Army Corps of Engineers Civil Works and Military Programs.
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REFERENCES
(1) Disaster Resilience: A National Imperative; The U.S. National Academy of Sciences: The National Academies Press: Washington, DC, 2012. (2) Presidential Policy Directive21: Critical Infrastructure Security and Resilience; Office of the President of the United States: Washington, DC, 2013. (3) Fiksel, J. Designing resilient, sustainable systems. Environ. Sci. Technol. 2003, 37, 5330−5339. (4) Park, J.; Seager, T. P.; Rao, P. S. C.; Convertino, M.; Linkov, I. Integrating risk and resilience approaches to catastrophe management in engineering systems. Risk Analysis. 2013, 33, 356−367. (5) Alberts, D. S.; Power to the Edge: Command and Control in the Information Age; The Command and Control Research Program: CCRP Publications, Library of Congress: Washington, DC, 2003.
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