Rethinking Resilience Analytics

The 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...

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Bibliographic Details
Published in:Risk analysis 2019-09, Vol.39 (9), p.1870-1884
Main Authors: Eisenberg, Daniel, Seager, Thomas, Alderson, David L.
Format: Article
Language:English
Subjects:
Analytics
Big Data
Blackouts
Critical infrastructure
Dam failure
Data analysis
Data management
Decision analysis
Disasters
Efficacy
Hurricanes
Infrastructure
Learning algorithms
Leverage
Machine learning
Mathematical analysis
Mathematical models
Natural disasters
Questions
Resilience
Risk assessment
Stock market crashes
surprise
Temptation
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Summary:The 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.
ISSN:0272-4332
1539-6924
DOI:10.1111/risa.13328