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Assessing uncertainty in extreme events: Applications to risk-based decision making in interdependent infrastructure sectors

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  • Barker, Kash
  • Haimes, Yacov Y.

Abstract

Risk-based decision making often relies upon expert probability assessments, particularly in the consequences of disruptive events and when such events are extreme or catastrophic in nature. Naturally, such expert-elicited probability distributions can be fraught with errors, as they describe events which occur very infrequently and for which only sparse data exist. This paper presents a quantitative framework, the extreme event uncertainty sensitivity impact method (EE-USIM), for measuring the sensitivity of extreme event consequences to uncertainties in the parameters of the underlying probability distribution. The EE-USIM is demonstrated with the Inoperability input–output model (IIM), a model with which to evaluate the propagation of inoperability throughout an interdependent set of economic and infrastructure sectors. The EE-USIM also makes use of a two-sided power distribution function generated by expert elicitation of extreme event consequences.

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  • Barker, Kash & Haimes, Yacov Y., 2009. "Assessing uncertainty in extreme events: Applications to risk-based decision making in interdependent infrastructure sectors," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 819-829.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:4:p:819-829
    DOI: 10.1016/j.ress.2008.09.008
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    10. Jan Oosterhaven, 2017. "On the limited usability of the inoperability IO model," Economic Systems Research, Taylor & Francis Journals, vol. 29(3), pages 452-461, July.
    11. Ouyang, Min, 2014. "Review on modeling and simulation of interdependent critical infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 43-60.
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    14. Reilly, Allison C. & Baroud, Hiba & Flage, Roger & Gerst, Michael D., 2021. "Sources of uncertainty in interdependent infrastructure and their implications," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
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    17. Joost R. Santos & Larissa May & Amine El Haimar, 2013. "Risk‐Based Input‐Output Analysis of Influenza Epidemic Consequences on Interdependent Workforce Sectors," Risk Analysis, John Wiley & Sons, vol. 33(9), pages 1620-1635, September.
    18. Desheng Dash Wu & Xie Kefan & Chen Gang & Gui Ping, 2010. "A Risk Analysis Model in Concurrent Engineering Product Development," Risk Analysis, John Wiley & Sons, vol. 30(9), pages 1440-1453, September.
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    20. Thomas A. Birkland, 2016. "Conceptualizing Resilience," Politics and Governance, Cogitatio Press, vol. 4(4), pages 117-120.
    21. Elisabeth Paté‐Cornell, 2012. "On “Black Swans” and “Perfect Storms”: Risk Analysis and Management When Statistics Are Not Enough," Risk Analysis, John Wiley & Sons, vol. 32(11), pages 1823-1833, November.
    22. Zhang, Jianhua & Song, Bo & Zhang, Zhaojun & Liu, Haikuan, 2014. "An approach for modeling vulnerability of the network of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 412(C), pages 127-136.
    23. Chou, Jui-Sheng & Ongkowijoyo, Citra Satria, 2015. "Reliability-based decision making for selection of ready-mix concrete supply using stochastic superiority and inferiority ranking method," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 29-39.
    24. Oosterhaven, Jan, 2015. "On the doubtful usability of the inoperability IO model," Research Report 15008-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).

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