Inoperability Input-output Model Research Articles

Evaluating the Consequences of an Inland Waterway Port Closure With a Dynamic Multiregional Interdependence Model

As intermodal hubs connecting barge, train, and truck transportation modes, inland ports play an important role in U.S. and global commerce. Like coastal ports, inland ports face the risk of malevolent attacks, man-made accidents, and natural disasters. However, most port impact studies focus on the consequences of one of these disruptive events suddenly closing a coastal port. This paper examines the economic impact of suddenly closing an inland port by combining a simulation and a multiregional input-output model. The simulation models how companies may react if an inland waterway port suddenly closes, and the multiregional dynamic inoperability input-output model quantifies the interdependent effects of these decisions. We deploy this simulation and model on a case study involving an Oklahoma port on the Arkansas River. The case study indicates that, if a financial penalty is imposed on companies for delivering their commodities late, companies will move their products by train rather than wait for the port to reopen. These decisions save billions of dollars in production losses for the states that use the port. We discuss the implications of these results for policymakers concerned about limiting the consequences of port closures.

Fuzzy dynamic input–output inoperability model

This paper describes an extension of the input–output inoperability model (IIM) that accommodates uncertain and vague data. In the resulting “fuzzy version” of the dynamic IIM model (FD-IIM), the level of inoperability of each infrastructure and the Leontief coefficients are specified using fuzzy variables that express expert knowledge about infrastructure interdependences. An important result is that, under certain conditions, solution convergence for the fuzzy model can be inferred based on the stability properties of the “crisp” (non-fuzzy) version of the system of interest. A case study involving the Italian critical infrastructure is used to demonstrate the feasibility and utility of the approach.

From the Editors

Our July 2011 issue focuses on risk perception, food-related risks, event likelihood, and consequences. Sammy Zahran et al. examined the impact of two major U.S. hurricanes on mental health. The authors estimated the increase of what they label “poor mental health days” and economic impacts, which not surprisingly most impacted disadvantaged populations. May Lynn Tan et al. studied fish consumption advisories in California in order to identify what works and does not work. They identified the use of portion sizes that departed from commonly consumed amounts, poorly understood terminology, misleading category headings, and ineffective visual tools. The authors offer practical suggestions for how to increase public understanding. Carmen Keller used an eye tracker apparatus and a risk ladder (low exposure levels and risks are on the bottom and high exposures and risks on the top) to measure how people viewed and understood risk information about smoking, a familiar risk, and radon, a less familiar one. With a sample of 68 individuals, the author examined responses to risk communication methods, observing that grounding the risk comparisons in a familiar risk like smoking helped respondents better understand the less familiar one. Henrik Andersson compared perception of personal auto-related risk of mortality with actual values. Under- and overestimation were related to gender, age, education, and amount traveled. Wim Kellens et al. focused on flood risk perception on the Belgian coast. With a sample of over 600 residents, the authors found that perceptions were associated with actual flood risk, experience with floods, as well as respondent age and gender. Three papers are about food-related risks. Jukka Ranta et al. looked for sources of foodborne infectious disease outbreaks in Finland and Norway. Using campylobacteriosis as an illustration, they point to flaws in the data collection as hindering the development of predictive models, and indeed conclude that simple models are preferred to complicated ones that require data that are not available and have to be estimated. Isabelle Albert et al. used a variety of data sources to estimate the worldwide prevalence and probability of campylobacteriosis. We think you will find that their approach to partitioning and evaluating the data is interesting. Henrik Merkelsen interviewed risk management experts in Denmark in order to understand the role of risk information in food safety decisions. He observed that relationships among experts and their institutions drive what information is valued and communicated rather than the information itself. The last three articles are about likelihood estimates and consequences. Funded by the U.S. Department of Transportation, Samrat Chatterjee and Mark Abkowitz developed a regional terrorism risk analysis approach that is embedded within the context of other man-made and natural hazards and strongly influenced by the distribution of people and infrastructure. Their goal is to provide context for evaluating benefits of alternative investment strategies across multiple risks in a region, which in this case was the state of Tennessee. John Quigley and Matthew Revie point to the discomfort of estimating a risk likelihood of zero. They used minimax theory to develop alternative estimates, observing that they were able to approximate the results of the maximum likelihood estimate for nonzero data, as well as provide a nonzero estimate when there were no event data. The inoperability input-output model (IIM) allows economic analysts to assess the impact of disaster events in the most stressed economic sectors. Marco Percoco offers an extension of the approach that may make IIM even more useful.

Supply-Driven Dynamic Inoperability Input-Output Price Model for Interdependent Infrastructure Systems

A supply driven dynamic input-output inoperability model (SDIIM), which extends the classical dynamic inoperability input-output model (DIIM), is described in this paper. The classical DIIM was developed to help understand the infrastructure interdependencies of deliberate external attacks or unfortunate natural disasters, such as terrorist attacks or earthquakes and flooding. However, classical DIIM is a demand-driven model. More supply driven sectors exist than demand-driven sectors in interdependent infrastructure systems, but value-added perturbation is the predominant feature in supply driven sectors for an interrupt event. Moreover, compared to classical DIIM, the value added by the proposed SDIIM can be controlled more easily than the final demand of a disruptive event because the final demand perturbation may be affected by the psychological effect on consumers. A supply driven input-output inoperability model (IIM) has also been proposed to complement the classical IIM. However, the supply driven IIM is a static model, and only equilibrium values can be determined. The SDIIM presented in this paper has been formulated to model the behavior of the value-added input perturbation up to equilibrium and to characterize the required sector adjustments for achieving new levels of output values. In addition, the interdependency resilience coefficient and the interdependency index are discussed by using the SDIIM. In this paper, the large-scale snow disaster that occurred in south China in 2008 is used as a case study, in which the SDIIM is used to analyze the inoperability and dynamic output values recovery of sectors caused by a value-added disturbance. The SDIIM is shown to effectively compute interdependent inoperability and economic losses, which can provide valuable insights into risk assessment and the management of interdependent infrastructure systems and sectors of the economy.

EVALUATING UNCERTAINTY IN RISK-BASED INTERDEPENDENCY MODELING WITH INTERVAL ARITHMETIC

Several sources of uncertainty exist in the effort to quantify the efficacy of preparedness decision-making in interdependent systems. For the Inoperability Input–Output Model (IIM), a risk-based extension of the traditional Leontief model, which describes the propagation of inoperability throughout interconnected economic sectors, uncertainty is manifested in parameters describing the strength of interdependencies among sectors and in parameters describing the adverse impacts of a disruptive event, among others. As the model is used to evaluate preparedness options to reduce the impact of these disruptive events, such uncertainty can impact decision-making efforts. This paper introduces interval arithmetic as an approach for dealing with uncertainties in the IIM when probability distributions are not known and only variable bounds are available. Illustrative examples highlight the use of the approach as well as a means to improve the evaluation and comparison of risk management strategies in interdependent systems when only intervals are known.

Interdependent impacts of inoperability at multi-modal transportation container terminals

This work describes the interdependent adverse effects of disruptive events on inter-regional commodity flows resulting from disruptions at an inland port terminal. To do so we integrate the risk-based Multi-Regional Inoperability Input–Output Model, which measures the cascading regional effects of disruptions to interconnected industries, with models, which simulate port operations such as commodity arrival, unloading, sorting, and distributing. Such models capture three disruption scenarios at the port and provide measures of impact to industries that use the inland port terminal facility. A case study highlights the disruptive effects of a closure of the Port of Catoosa in Oklahoma.

On the Local Sensitivity Analysis of the Inoperability Input‐Output Model

Natural and man-made disasters are currently a source of major concern for contemporary societies. In order to understand their economic impacts, the inoperability input-output model has recently gained recognition among scholars. In a recent paper, Percoco (2006) has proposed an extension of the model to map the technologically most important sectors through so-called fields of influence. In the present note we aim to show that this importance measure also has a clear connection with local sensitivity analysis theory.

A Risk‐Based Approach for Identifying Key Economic and Infrastructure Systems

This article introduces approaches for identifying key interdependent infrastructure sectors based on the inventory dynamic inoperability input-output model, which integrates an inventory model and a risk-based interdependency model. An identification of such key sectors narrows a policymaker's focus on sectors providing most impact and receiving most impact from inventory-caused delays in inoperability resulting from disruptive events. A case study illustrates the practical insights of the key sector approaches derived from a value of workforce-centered production inoperability from Bureau of Economic Analysis data.

Agent-based input–output interdependency model

The modeling and analysis of critical infrastructures and their interdependencies are essential to discovering hidden vulnerabilities and the related threats to national and international security. Over the past few years, several approaches have been proposed to address this problem. The so-called holistic approaches are relatively abstract, but are easily validated using real economic data. Other approaches based on agent-based models provide deeper views of the interdependencies existing between subsystems of different infrastructures. However, agent-based models are often difficult to validate because quantitative data of the appropriate granularity may not be available. This paper presents an agent-based input–output inoperability model designed to overcome the limitations of the holistic and agent-based paradigms. In order to provide a detailed and expressive framework, the exchange of resources between infrastructures is explicitly modeled while inoperability becomes an internal parameter. Nevertheless, the model is easily transformed into a fine-grained, input–output inoperability model whose coefficients can be obtained based on real data.

Development of the multiregional inoperability input-output model (MRIIM) for spatial explicitness in preparedness of interdependent regions

Any given region in the US is a complex, interconnected, and interdependent economic system of systems that includes multiple stakeholders, spans multiple sub-regions, and produces a very large num...

Uncertainty Importance and Risk-Based Decision Making in the Inoperability Input-Output Model

In this paper we apply a moment-independent sensitivity analysis technique to the Inoperability Input-Output Model to identify the most effective preparedness strategies.

Incorporating Time-Varying Perturbations Into the Dynamic Inoperability Input–Output Model

This paper introduces a methodology to extend the capabilities of the dynamic inoperability input-output model (DIIM) to account for perturbations to sectors of the economy that are time varying and probabilistic. The DIIM accounts for the interdependent nature of the economy in determining the impact of a disruption on the ability of each economic sector to satisfy the prevailing demand. While the original formulation of the DIIM only allows for a single perturbation vector, this paper extends the DIIM to allow a perturbation matrix to be utilized as inputs to the model. The underlying motivation for this paper is understanding the impact of a pandemic on a regional economy. As such, idiosyncrasies associated with a pandemic are also accounted for in the model formulation. These include translating workforce absenteeism into a measure of sector inoperability and accounting for the impact of long-term absenteeism on a sector's ability to deliver its as-planned production output.

Pandemic Recovery Analysis Using the Dynamic Inoperability Input‐Output Model

Economists have long conceptualized and modeled the inherent interdependent relationships among different sectors of the economy. This concept paved the way for input-output modeling, a methodology that accounts for sector interdependencies governing the magnitude and extent of ripple effects due to changes in the economic structure of a region or nation. Recent extensions to input-output modeling have enhanced the model's capabilities to account for the impact of an economic perturbation; two such examples are the inoperability input-output model((1,2)) and the dynamic inoperability input-output model (DIIM).((3)) These models introduced sector inoperability, or the inability to satisfy as-planned production levels, into input-output modeling. While these models provide insights for understanding the impacts of inoperability, there are several aspects of the current formulation that do not account for complexities associated with certain disasters, such as a pandemic. This article proposes further enhancements to the DIIM to account for economic productivity losses resulting primarily from workforce disruptions. A pandemic is a unique disaster because the majority of its direct impacts are workforce related. The article develops a modeling framework to account for workforce inoperability and recovery factors. The proposed workforce-explicit enhancements to the DIIM are demonstrated in a case study to simulate a pandemic scenario in the Commonwealth of Virginia.

Uncertainty Analysis of Interdependencies in Dynamic Infrastructure Recovery: Applications in Risk-Based Decision Making

The development of preparedness strategies for natural and malevolent man-made hazards and approaches with which to compare such investments is fraught with uncertainty. The dynamic inoperability input-output model (DIIM) quantifies the inoperability that propagates through interdependent sectors following a disruptive event and then diminishes with time. This approach has been shown to quantify the efficacy of preparedness strategies for interconnected sectors of the economy. Work presented in this paper strengthens the DIIM with a multiobjective approach—the uncertainty DIIM—that evaluates the inherent uncertainty in the parameters of interdependency and its impact on projected economic loss calculated using the DIIM. Preparedness strategies can then be compared based on projected economic loss and on their sensitivity to changes in the interdependent nature of infrastructure sectors. Additionally, key sector analyses are discussed, where sectors are ranked according to their sensitivity to changes in interdependent relationships. Such enumeration of key sectors allows decision makers to focus on certain sensitive infrastructure sectors for the development of preparedness strategies. The models developed in this paper are illustrated with numerical examples.

Estimating Workforce-Related Economic Impact of a Pandemic on the Commonwealth of Virginia

A pandemic outbreak is one of the major planning scenarios considered by emergency-preparedness policymakers. The consequences of a pandemic can significantly affect and disrupt a large spectrum of workforce sectors in today's society. This paper, motivated by the impact of a pandemic, extends the formulation of the dynamic inoperability input–output model (DIIM) to account for economic perturbations resulting from such an event, which creates a time-varying and probabilistic inoperability to the workforce. A pandemic is a unique disaster, because the majority of its direct impacts are workforce related and it does not create significant direct impact to infrastructure. In light of this factor, this paper first develops a method of translating unavailable workforce into a measure of economic-sector inoperability. While previous formulations of the DIIM only allowed for the specification of an initial perturbation, this paper incorporates the fact that a pandemic can cause direct effects to the workforce over the recovery period. Given the uncertainty associated with the impact of a pandemic, this paper develops a simulation framework to account for the possible variations in realizations of the pandemic. The enhancements to the DIIM formulation are incorporated into a MatLab program and then applied to a case study to simulate a pandemic scenario in the Commonwealth of Virginia.

Critical infrastructure dependency assessment using the input–output inoperability model

The input–output inoperability model (IIM) is a simple, but powerful, mechanism for analyzing the cascading effects induced by critical infrastructure dependencies and interdependencies. IIM typically uses financial data as a measure of the dependency phenomena. Since financial data is only one of the many dimensions for analyzing dependency phenomena, the quality of IIM parameters and, thus, the reliability of IIM results can be affected negatively. This paper proposes a methodology for evaluating IIM parameters based on technical and operational data. The data is collected by interviewing experts and is processed using a fuzzy set based methodology. A case study involving Italian critical infrastructure sectors is used to demonstrate the effectiveness of the methodology.

Measuring the efficacy of inventory with a dynamic input–output model

This paper extends the recently developed dynamic inoperability input–output model (DIIM) for assessing productivity degradations due to disasters. Inventory policies are formulated and incorporated within the DIIM to evaluate the impact of inventories on the resilience of disrupted interdependent systems. The Inventory DIIM can provide practical insights to preparedness decision making through explicit tradeoff analysis of multiple objectives, including inventory costs and economic loss reductions. The model is demonstrated in several illustrative examples to depict various nuances of inventory policies. The paper then culminates in a case study that utilizes input–output and inventory accounts from the Bureau of Economic Analysis.

Development of the multiregional inoperability input‐output model (MRIIM) for spatial explicitness in preparedness of interdependent regions

AbstractAny given region in the US is a complex, interconnected, and interdependent economic system of systems that includes multiple stakeholders, spans multiple sub‐regions, and produces a very large number of commodities and services. This paper provides a holistic, methodological framework with which to model the multisectoral and multiregional economic interdependencies inherent in such a large‐scale and complex system, factors essential to making strategic preparedness decisions. The framework we present extends the Inoperability Input‐Output Model (IIM) [Santos and Haimes, 2004], a model which has been developed to study these large‐scale systems and which has been deployed in various studies. The basic IIM yields only average estimates across geography, and thus may provide insufficient information with which to make decisions about preparedness allocation within and across regions. Such average estimates may lead planners and policy makers to overlook geographically concentrated risks and significant cross‐regional interdependencies, which are critical in evaluating strategic preparedness options. This paper extends the IIM to model the multiregional interdependencies among the various regions in the US by introducing and developing the Multiregional IIM (MRIIM) and by presenting the spatially explicit concepts of intraregional and multiregional interdependency matrices, A* and T*, respectively. The MRIIM possesses important properties that are derived from the databases that support the model and from the construction of the model itself. These properties guarantee unique solutions when studying the cascade of perturbation across regions and guarantee convergence when computational methods are applied. These properties also lead to a notion of resilience that is gained from cross‐regional interdependencies. The major contribution of this paper is to demonstrate the importance of spatial explicitness in interdependency analysis through an example case study. © 2009 Wiley Periodicals, Inc. Syst Eng

International Trade Inoperability Input‐Output Model (IT‐IIM): Theory and Application

The inoperability input-output model (IIM) has been used for analyzing disruptions due to man-made or natural disasters that can adversely affect the operation of economic systems or critical infrastructures. Taking economic perturbation for each sector as inputs, the IIM provides the degree of economic production impacts on all industry sectors as the outputs for the model. The current version of the IIM does not provide a separate analysis for the international trade component of the inoperability. If an important port of entry (e.g., Port of Los Angeles) is disrupted, then international trade inoperability becomes a highly relevant subject for analysis. To complement the current IIM, this article develops the International Trade-IIM (IT-IIM). The IT-IIM investigates the resulting international trade inoperability for all industry sectors resulting from disruptions to a major port of entry. Similar to traditional IIM analysis, the inoperability metrics that the IT-IIM provides can be used to prioritize economic sectors based on the losses they could potentially incur. The IT-IIM is used to analyze two types of direct perturbations: (1) the reduced capacity of ports of entry, including harbors and airports (e.g., a shutdown of any port of entry); and (2) restrictions on commercial goods that foreign countries trade with the base nation (e.g., embargo).

Transportation-Based Framework for Deriving Perturbations to the Inoperability Input-Output Model

The Inoperability Input-Output Model (IIM) provides a mechanism to enable examination of interdependencies among economic sectors to estimate the effects of a disruptive event. In previous IIM applications, parametric analyses were used to represent uncertain model inputs that created ambiguity in physical interpretations. This paper develops a transportation-based framework for decomposing the IIM inputs into two components: (1) workforce and (2) commodity flows. For workforce analysis, we estimate the number of workers using a particular asset, the normal income level, and the reduced income resulting from a disruption. For commodity analysis, we estimate the value of goods traveling across a particular asset and the resulting reduction due to a disruption. The workforce and commodity formulations were demonstrated using an ex post analysis of the 2003 closure of Virginia’s Midtown Tunnel. The study utilized a computer tool to execute the model and to perform sensitivity analysis to account for the data uncertainties.