Rethinking Resilience Analytics
Authors
Eisenberg, Daniel
Seager, Thomas
Alderson, David L.
Subjects
Analytics
infrastructure
resilience
surprise
infrastructure
resilience
surprise
Advisors
Date of Issue
2019
Date
Publisher
Wiley
Language
Abstract
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.
Type
Article
Description
The article of record as published may be found at https://doi.org/10.1111/risa.13328
Series/Report No
Department
Organization
Identifiers
NPS Report Number
Sponsors
Funding
Format
15 p.
Citation
Eisenberg, Daniel, Thomas Seager, and David L. Alderson. "Rethinking Resilience Analytics." Risk Analysis 39.9 (2019): 1870-1884.
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, it may not be copyrighted.