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

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...

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Bibliographic Details
Published inSystems engineering Vol. 9; no. 3; pp. 241 - 258
Main Authors Lian, Chenyang, Haimes, Yacov Y.
Format Journal Article
LanguageEnglish
Published Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.09.2006
Wiley
Subjects
Applied sciences
Computer science; control theory; systems
Computer systems and distributed systems. User interface
dynamic IIM
dynamic recovery
Exact sciences and technology
input-output inoperability model
risks of terrorism
Software
Software engineering
Short term
Economic analysis
Input output
Probabilistic approach
Terrorism
Disaster
Dynamical system
Transaction processing
Risk analysis
risks of terrorism
Modeling
Data flow
Data flow processing
homeland security
Brownian motion
dynamic IIM
Economy
dynamic recovery
Static model
Multiplier
input-output inoperability model
Risk management
Dynamic model
Input output model
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Summary: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
Bibliography:istex:DD0F66CCCF553A78A7D2B84E4F38BD29B1C021B4
ArticleID:SYS20051
ark:/67375/WNG-6QJNMV89-P
ISSN:1098-1241
1520-6858
DOI:10.1002/sys.20051