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Managing the risk of terrorism to interdependent infrastructure systems through the dynamic inoperability input–output model

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  • Chenyang Lian
  • Yacov Y. Haimes

Abstract

This paper discusses the Dynamic Input–Output Inoperability Model (DIIM), which is an extension to the static Inoperability Input–Output Model (IIM). Based on Wassily Leontief's Input–Output (I–O) model, both the IIM and the DIIM analyze how the system of interdependent sectors can be adversely affected as a result of initial perturbations to other sectors through willful attacks or natural disasters. To model the industry/sector interdependencies, the DIIM uses the national and regional commodity‐transaction data from the Bureau of Economic Analysis (BEA) and the Regional Input–Output Multiplier System (RIMS II). In contrast to most traditional dynamic I–O models, the DIIM introduces industry resilience coefficients to measure the efficacy of sectors' risk management options. The DIIM also incorporates the stochastic properties of a recovery through the Brownian motion, representing short‐term uncertainties. The paper uses two metrics to assess the consequences to the economic sectors of attacks. The DIIM methodology is demonstrated in detail through a two‐by‐two economy system. This is followed by an analysis of a terrorist attack scenario, using the DIIM and the BEA/RIMS II commodity‐flow data of the 59 sectors in Virginia. © 2006 Wiley Periodicals, Inc. Syst Eng 9: 241–258, 2006. DOI 10.1002/sys.20051

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  • Chenyang Lian & Yacov Y. Haimes, 2006. "Managing the risk of terrorism to interdependent infrastructure systems through the dynamic inoperability input–output model," Systems Engineering, John Wiley & Sons, vol. 9(3), pages 241-258, September.
    • Handle: RePEc:wly:syseng:v:9:y:2006:i:3:p:241-258
    DOI: 10.1002/sys.20051
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    1. Leontief, Wassily & Duchin, Faye, 1986. "The Future Impact of Automation on Workers," OUP Catalogue, Oxford University Press, number 9780195036237.
    2. George E. Apostolakis & Douglas M. Lemon, 2005. "A Screening Methodology for the Identification and Ranking of Infrastructure Vulnerabilities Due to Terrorism," Risk Analysis, John Wiley & Sons, vol. 25(2), pages 361-376, April.
    3. Adam Rose, 2004. "Economic Principles, Issues, and Research Priorities in Hazard Loss Estimation," Advances in Spatial Science, in: Yasuhide Okuyama & Stephanie E. Chang (ed.), Modeling Spatial and Economic Impacts of Disasters, chapter 2, pages 13-36, Springer.
    4. Kenneth G. Crowther & Yacov Y. Haimes, 2005. "Application of the inoperability input—output model (IIM) for systemic risk assessment and management of interdependent infrastructures," Systems Engineering, John Wiley & Sons, vol. 8(4), pages 323-341.
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    Cited by:

    1. Pradeep V. Mandapaka & Edmond Y. M. Lo, 2023. "Assessing Shock Propagation and Cascading Uncertainties Using the Input–Output Framework: Analysis of an Oil Refinery Accident in Singapore," Sustainability, MDPI, vol. 15(2), pages 1-24, January.
    2. Kenneth G. Crowther & Yacov Y. Haimes, 2010. "Development of the multiregional inoperability input‐output model (MRIIM) for spatial explicitness in preparedness of interdependent regions," Systems Engineering, John Wiley & Sons, vol. 13(1), pages 28-46, March.
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    4. Jian Jin & Haoran Zhou, 2023. "A Demand-Side Inoperability Input–Output Model for Strategic Risk Management: Insight from the COVID-19 Outbreak in Shanghai, China," Sustainability, MDPI, vol. 15(5), pages 1-22, February.
    5. Shanshan Ye & Mingming Cao, 2023. "The Impact of Industrial Linkage Structures on Urban Economic Resilience in China in the Context of the COVID-19 Shock," Sustainability, MDPI, vol. 15(6), pages 1-15, March.
    6. Lu, Qing-Chang & Xu, Peng-Cheng & Zhao, Xiangmo & Zhang, Lei & Li, Xiaoling & Cui, Xin, 2022. "Measuring network interdependency between dependent networks: A supply-demand-based approach," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    7. Hairui Wei & Ming Dong & Shuyu Sun, 2010. "Inoperability input‐output modeling (IIM) of disruptions to supply chain networks," Systems Engineering, John Wiley & Sons, vol. 13(4), pages 324-339, December.
    8. Cottafava, Dario & Gastaldo, Michele & Quatraro, Francesco & Santhiá, Cristina, 2022. "Modeling economic losses and greenhouse gas emissions reduction during the COVID-19 pandemic: Past, present, and future scenarios for Italy," Economic Modelling, Elsevier, vol. 110(C).
    9. Jalal Ali & Joost R. Santos, 2015. "Modeling the Ripple Effects of IT‐Based Incidents on Interdependent Economic Systems," Systems Engineering, John Wiley & Sons, vol. 18(2), pages 146-161, March.
    10. Hiroyuki Shibusawa & Daichi Matsushima, 2022. "Assessing the economic impact of tsunami and nuclear power plant disasters in Shizuoka, Japan: a dynamic inter-regional input–output (IRIO) approach," Asia-Pacific Journal of Regional Science, Springer, vol. 6(1), pages 307-333, February.

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