Inoperability Input-output Model Research Articles

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

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.

Models for risk management of systems of systems

This paper addresses the need for developing a body of prescriptive theory and methodology for modelling Systems of Systems (SoS). This will enable analysts to appropriately model and understand the SoS evolving behaviour due to the continued forced changes imposed on them. To place this need in historical perspective, the evolution of the field of systems engineering/analysis is briefly reviewed. The complexity of SoS is characterised by the highly interconnected and interdependent physical and economic infrastructure sectors of every country in the world, which introduce significant modelling challenges for the systems analysts. Economic, physical, cyber, workforce, organisational, and international intra- and interdependencies are introduced. Particular economic interdependency models (the Inoperability Input-Output Model (IIM) and its derivatives) follow. Hierarchical Holographic Modelling (HHM) for studying the multiple dimensions of risk to SoS is introduced, followed by the Phantom System Models (PSM) for modelling emergent large-scale SoS.

Supply- and Output-Side Extensions to the Inoperability Input-Output Model for Interdependent Infrastructures

The Inoperability Input-Output Model (IIM) was developed to help understand infrastructure interdependencies. Based on the Leontief Input-Output Model, the IIM approximates the physical interdependencies of infrastructures from the economic input-output transactions of sectors. The model is demand-driven, wherein perturbations to the final demand levels are considered the initiating event, and the impact to sectors’ production outputs are the direct and indirect effects resulting from sector interdependencies. This paper presents two other perspectives—supply-side and output-side—to supplement and complement the demand-driven IIM. These two perspectives help address initiating perturbations related to input factors (value added) and to the sectors’ output levels. Further, these perturbations can be in terms of either quantity or price changes, defining the demand-quantity and supply-price models. Finally, the demand-, supply-, and output-side models are integrated using a sequential perturbation approach. A case study using the Virginia 1997 I-O accounts is presented, where it was shown that the different perturbation classes (demand or supply, quantity or price) yield different rank orders of sectors impacted by the initiating event, thus providing various perspectives of impacts.

Systemic Valuation of Strategic Preparedness Through Application of the Inoperability Input‐Output Model with Lessons Learned from Hurricane Katrina

The U.S. Department of Homeland Security (DHS) has mandated all regions to "carefully weigh the benefit of each homeland security endeavor and only allocate resources where the benefit of reducing risk is worth the amount of additional cost" (DHS, 2006, p. 64). This mandate illuminates the need to develop methods for systemic valuation of preparedness measures that support strategic decision making. This article proposes an analysis method that naturally emerges from the structure of the inoperability input-output model (IIM) through which various regional- and sector-specific impact analyses can be cost-effectively integrated for natural and man-made disasters. The IIM is described extensively in a companion paper (Lian et al., 2007). Its reliance on data classifications structured by the U.S. Census Bureau and its extensive accounting of economic interdependencies enables us to decompose a risk analysis activity, perform independent assessments, and properly integrate the assessment for a systemic valuation of risk and risk management activity. In this article, we account for and assess some of the major impacts of Hurricanes Katrina and Rita to demonstrate this use of the IIM and illustrate hypothetical, reduced impacts resulting from various strategic preparedness decisions. Our results indicate the capability of the IIM to guide the decision-making processes involved in developing a preparedness strategy.

Extreme Risk Analysis of Interdependent Economic and Infrastructure Sectors

Willful attacks or natural disasters pose extreme risks to sectors of the economy. An extreme-event analysis extension is proposed for the Inoperability Input-Output Model (IIM) and the Dynamic IIM (DIIM), which are analytical methodologies for assessing the propagated consequences of initial disruptions to a set of sectors. The article discusses two major risk categories that the economy typically experiences following extreme events: (i) significant changes in consumption patterns due to lingering public fear and (ii) adjustments to the production outputs of the interdependent economic sectors that are necessary to match prevailing consumption levels during the recovery period. Probability distributions associated with changes in the consumption of directly affected sectors are generated based on trends, forecasts, and expert evidence to assess the expected losses of the economy. Analytical formulations are derived to quantify the extreme risks associated with a set of initially affected sectors. In addition, Monte Carlo simulation is used to handle the more complex calculations required for a larger set of sectors and general types of probability distributions. A two-sector example is provided at the end of the article to illustrate the proposed extreme risk model formulations.

A Risk-based Input–Output Methodology for Measuring the Effects of the August 2003 Northeast Blackout

The 2003 Northeast Blackout revealed vulnerabilities within the US electric power-grid system. With the economy so dependent on electric power for most aspects of life, a power-grid failure can have far-reaching higher-order effects and can impair the operability of other critical infrastructures. An inoperability of the power sector can result from different types of disasters (e.g., accidents, natural catastrophe, or willful attacks). This paper demonstrates the Inoperability Input-Output Model (IIM) to measure the financial and inoperability effects of the Northeast Blackout. The case study uses information from sources such as the US input–output tables and sector-specific reports to quantify losses for specific inoperability levels. The IIM estimated losses of the same magnitude as other published reports; however, with a detailed accounting of all affected economic sectors. Finally, a risk management framework is proposed to extend the IIM's capability for evaluating investment options in terms of their implementation costs and loss-reduction potentials.

Availability of healthcare services in a network-based scenario

In this paper, we illustrate how the third millennium healthcare system is strongly related to many networked infrastructures. This contributes to improving its efficiency and efficacy but, at the same time, introduces new and very dangerous elements of vulnerability that should be carefully taken into account. These vulnerabilities are mainly induced by the presence of different, and many times hidden or poorly known, dependencies and interdependencies existing among the different infrastructures. These interdependencies induce an augmentation of possible threats and the occurrence that negative consequences of a failure might be largely amplified owing to the presence of the cascade and/or feedback phenomena. To better illustrate the possible catastrophic scenarios induced by this framework, the Input-output Inoperability Model (IIM) has been applied on a modern hospital to compare its behaviour, in the presence of a failure in the IP network, with that of a more classical structure.

Multi‐period model for disruptive events in interdependent systems

AbstractThis paper develops the Multi‐Period Model for Disruptive Events in Interdependent Systems (MPMDEIS). The analysis is divided into two principal periods: (1) an event period that lasts for the duration of the active perturbation and results in the degradation of the interdependent systems, and (2) a recovery period during which the systems evolves to a new equilibrium. The event period perturbation is modeled as a shock that cascades among the interdependent systems and reduces their production capacity. The state of the systems at the end of the event period is specified by its operational unavailability vector, which is given by the initial perturbation strength vector multiplied by an exponential matrix with the interdependency matrix P as the exponent. The coefficients of P represent the relative contribution of the loss of capacity of one system to the loss of another system as the perturbation propagates from one to the other. The quantification of the shock and interdependencies is critical for the realistic assessment of the inflicted damage and formulating effective defensive strategies against terrorist attacks, natural hazards, and technological failures or accidents. The recovery period depends on the implemented response actions and it can be modeled using a systems dynamics approach. The standard economic input‐output tables are unlikely to be appropriate for the events of interest. However, there are probabilistic risk analysis techniques such as eliciting expert opinions and analytic methods which can be used to reliably assess these data. The MPMDEIS is compared with the Inoperability Input‐Output Model (IIM) and Dynamic Input‐Output Inoperability Model (DIIM) and it is demonstrated to avoid IIM and DIIM assumptions that can lead to significant errors for disruptive events in interdependent systems. © 2006 Wiley Periodicals, Inc. Syst Eng 9: 281– 295, 2006

Managing the risk of terrorism to interdependent infrastructure systems through the dynamic inoperability inputoutput model

This paper discusses the Dynamic InputOutput Inoperability Model (DIIM), which is an extension to the static Inoperability InputOutput Model (IIM). Based on Wassily Leontief's InputOutput (IO) mode...

A Note on the Inoperability Input‐Output Model

In this note, we propose some comments and some extensions of the inoperability input-output model (IIOM), as recently proposed by Santos and Haimes (2004). In particular, we propose the use of some analytic tools capable of providing information on the reaction of sectors subsequent to a terrorist attack on infrastructure service sectors. These tools, namely, the field of influence and the multiplier product matrix, provide information on the way sectors react to a shock on the aggregate demand and/or to a (temporary or permanent) change of production function coefficients. Finally, using the 2003 65 sectors input-output matrix for the U.S. economy, a simple empirical example is presented.

Inoperability input-output modeling of disruptions to interdependent economic systems

In this study, the Inoperability Input-Output Model (IIM) is deployed for assessing the impacts of disruptive events on interconnected economic systems. The IIM is based on Wassily Leontief's input...

Inoperability input‐output modeling of disruptions to interdependent economic systems

AbstractIn this study, the Inoperability Input‐Output Model (IIM) is deployed for assessing the impacts of disruptive events on interconnected economic systems. The IIM is based on Wassily Leontief's input‐output model which is capable of describing the ripple effects of disruptions to interdependent systems. Besides describing economic impact in financial terms, the “inoperability” metric is also used in the IIM to quantify the percentage of a system's production that is affected relative to the desired level. To analyze the magnitude and extent of system linkages, an interdependency matrix based on the North American Industry Classification System (NAICS) is constructed. The study highlights four key features of the IIM. First, demand patterns in the aftermath of such events are modeled and analyzed using available sector‐based performance data. Second, the NAICS‐based capital flow data released for the first time in 2003 by the US Bureau of Economic Analysis enable applying a dynamic IIM to describe the temporal behavior of economic impacts associated with disruptive events. Third, a discussion on utilizing other sources of data, such as consumer confidence for forecasting system‐specific demand disruptions, is presented. Fourth, a visualization tool is presented for conducting a multi‐criteria ranking of the most‐affected systems using both economic loss and inoperability metrics. Ultimately, the study offers insights on describing the sensitivity of economic systems to various classes of disruptions. In the broader perspective, this can provide guidance toward policymaking activities. © 2006 Wiley Periodicals, Inc. Syst Eng 9: 20–34, 2006

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

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

Application of the inoperability inputoutput model (IIM) for systemic risk assessment and management of interdependent infrastructures

Our modern era is characterized by a large-scale web of interconnected and interdependent economic and infrastructure systems, coupled with threats of terrorism. This paper demonstrates the value o...

Inoperability Input-Output Model for Interdependent Infrastructure Sectors. I: Theory and Methodology

The paper discusses the theory and methodology supporting the development of the inoperability input-output model (IIM). The IIM is based on Leontief’s input-output model, which characterizes interdependencies among sectors in the economy and analyzes initial disruptions to a set of sectors and the resulting ripple effects. An advantage of building on Leontief’s model is that it is supported by publications of the Bureau of Economic Analysis. Independent computer runs of the IIM can represent the entire nation or sectors within particular U.S. regions. A dynamic extension of the IIM analyzes different temporal frames of recovery and characterizes the required sector adjustments for achieving new production levels. The IIM can systemically prioritize and manage the sectors deemed to be economically critical and also identify those sectors whose continued operability is critical during recovery. A companion paper demonstrates applying the IIM to attacks on electric power and telecommunications.

Inoperability Input-Output Model for Interdependent Infrastructure Sectors. II: Case Studies

The paper discusses case studies of the inoperability input-output model (IIM) for modeling impacts of willful attacks on interdependent sectors. The IIM is a model for assessing sector vulnerabilities using the inoperability and economic loss impact metrics. The case studies focus on high-altitude electromagnetic pulse (HEMP) attack scenarios. HEMP is an intense electromagnetic blast induced from high-elevation nuclear explosions, potentially causing damage to electronic and electrical systems. Parametric and uncertainty analyses are conducted for assessing (1) intensity of initial disruptions, (2) sector recovery characteristics, (3) economic loss reduction policies for critical sectors, and (4) regional scope of an attack. Sectors susceptible to a HEMP attack have been identified, including electric power, electronic equipment, and workforce. Trade-off analyses are performed to analyze the efficacy of resource allocation strategies associated with recovery. Recommendations from this paper include developing cost-benefit-risk balanced solutions for managing and expediting recovery time from potential terrorist attacks.

A Roadmap for Quantifying the Efficacy of Risk Management of Information Security and Interdependent SCADA Systems

Many sectors of the economy and other critical infrastructures are highly coupled and their interdependencies render them at risk to cyber terrorist attacks. This fact is further exacerbated because they are often remotely controlled and managed through supervisory control and data acquisition (SCADA) systems, which are vulnerable to such cyber intrusion. The myriad sources of risk to SCADA systems identified through hierarchical holographic modeling (HHM)serve as the impetus to the roadmap for quantifying the efficacy of risk management of interdependent SCADA systems presented in this paper. Central to this quantification metric is the deployment of the inoperability input-output model (IIM). This is a Leontief-based model that enables accounting for both the intra-and interconnectedness within each economic sector and infrastructure. At the core of the IIM is the notion of risk of inoperability, which describes a critical infrastructure's expected level of dysfunction. The input to the system is an initial perturbation triggered by an attack of terrorism, an accidental event, or a natural disaster. The outputs of the system are the resulting risks of inoperability of different infrastructures due to their connections to one another. These outputs are presented in two different metrics: (1) economic inoperability measured in dollars lost for each interdependent sector of the economy, and (2) functional inoperability measured in each sector's percentage of dysfunctionality. This model addresses the equilibrium state of the system in the event of an attack, provided that the interdependency matrix is known. The national interdependency database provided by the Bureau of Economic Analysis (BEA), US Department of Commerce, constitutes the core database for the IIM interdependency matrix. The national database consists of 483 sectors and the regional of 37 sectors. The metric used for quantifying the efficacy of risk management of interdependent SCADA systems builds on the economic losses generated by the IIM resulting from a cyber attack with and then without risk management, and considering as well the cost of risk management. A scenario of a cyber attack on telecommunications and electric power infrastructures is discussed, using national BEA data. The results are analyzed, followed by a summary and conclusions.

Application of the inoperability input—output model (IIM) for systemic risk assessment and management of interdependent infrastructures

Application of the inoperability input—output model (IIM) for systemic risk assessment and management of interdependent infrastructures