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Rethinking Resilience Analytics.

Daniel Eisenberg1, Thomas Seager2, David L Alderson1

  • 1Operations Research Department, Naval Postgraduate School, Monterey, CA, USA.

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PubMed
Summary
This summary is machine-generated.

Resilience analytics, using big data, can aid critical infrastructure against predictable surprises. However, it may fail or harm cyber-physical-social systems facing unprecedented events.

Keywords:
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Area of Science:

  • Infrastructure resilience
  • Big data analytics
  • Cyber-physical-social systems

Background:

  • Resilience analytics leverages big data and machine learning to enhance critical infrastructure and community resilience.
  • High-profile disasters increase the appeal of data-driven resilience strategies.
  • Existing analytic models are effective for rare, predictable surprises.

Purpose of the Study:

  • To evaluate the efficacy of resilience analytics in helping cyber-physical-social (CPS) systems adapt to surprise.
  • To identify the limitations of resilience analytics when facing unprecedented events.

Main Methods:

  • Examining the application of resilience analytics in CPS contexts.
  • Analyzing the performance of analytic models during various disaster scenarios (hurricanes, dam failures, blackouts, stock market crashes).

Main Results:

  • Resilience analytics can support adaptation to predictable, situational surprises.
  • These analytics demonstrate limitations when CPS systems encounter fundamental surprises outside model parameters.
  • In unprecedented events, resilience analytics may be ineffective or even detrimental.

Conclusions:

  • Resilience analytics alone are insufficient for adapting to fundamental surprises.
  • Over-reliance on resilience analytics can paradoxically increase CPS system vulnerability.
  • Future research should focus on improvisation strategies for adapting CPS systems to unforeseen events.