Mark Andrew Ehlen

A Framework for Assessing the Resilience of Infrastructure and Economic Systems

Recent U.S. national mandates are shifting the country’s homeland security policy from one of asset-level critical infrastructure protection (CIP) to allhazards critical infrastructure resilience, creating the need for a unifying framework for assessing the resilience of critical infrastructure systems and the economies that rely on them. Resilience has been defined and applied in many disciplines; consequently, many disparate approaches exist. We propose a general framework for assessing the resilience of infrastructure and economic systems. The framework consists of three primary components: (1) a definition of resilience that is specific to infrastructure systems; (2) a quantitative model for measuring the resilience of systems to disruptive events through the evaluation of both impacts to system performance and the cost of recovery; and (3) a qualitative method for assessing the system properties that inherently determine system resilience, providing insight and direction for potential improvements in these systems.

A resilience assessment framework for infrastructure and economic systems: Quantitative and qualitative resilience analysis of petrochemical supply chains to a hurricane

In recent years, the nation has recognized that critical infrastructure protection should consider not only the prevention of disruptive events but also the processes that infrastructure systems undergo to maintain functionality following disruptions. This more comprehensive approach has been termed critical infrastructure resilience. Given the occurrence of a particular disruptive event, the resilience of a system to that event is the systems ability to reduce efficiently both the magnitude and duration of the deviation from targeted system performance levels. Under the direction of the U. S. Department of Homeland Securitys Science and Technology Directorate, Sandia National Laboratories has developed a comprehensive resilience assessment framework for evaluating the resilience of infrastructure and economic systems. The framework includes a quantitative methodology that measures resilience costs that result from a disruption to infrastructure function. The framework also includes a qualitative analysis methodology that assesses system characteristics affecting resilience to provide insight and direction for potential improvements. This article describes the resilience assessment framework and demonstrates the utility of the assessment framework through application to two hypothetical scenarios involving the disruption of a petrochemical supply chain by hurricanes.

Modeling interdependencies between power and economic sectors using the N-ABLE agent-based model

The nations electric power sector is highly interdependent with the economic sectors it serves; electric power needs are driven by economic activity while the economy itself depends on reliable and sustainable electric power. To advance higher level understandings of the vulnerabilities that result from these interdependencies and to identify the loss prevention and loss mitigation policies that best serve the nation, the National Infrastructure Simulation and Analysis Center is developing and using N-ABLE{trademark}, an agent-based microeconomic framework and simulation tool that models these interdependencies at the level of collections of individual economic firms. Current projects that capture components of these electric power and economic sector interdependencies illustrate some of the public policy issues that should be addressed for combined power sector reliability and national economic security.

Benefits of synchronous collaboration support for an application-centered analysis team working on complex problems: a case study

A month-long quasi-experiment was conducted using a distributed team responsible for modeling, simulation, and analysis. Six experiments of three different time durations (short, medium, and long) were performed. The primary goal was to discover if synchronous collaboration capability through a particular application improved the ability of the team to form a common mental model of the analysis problem(s) and solution(s). The results indicated that such collaboration capability did improve the formation of common mental models, both in terms of time and quality (i.e., depth of understanding), and that the improvement did not vary by time duration. In addition, common mental models were generally formed by interaction around a shared graphical image, the progress of collaboration was not linear but episodic, and tasks that required drawing and conversing at the same time were difficult to do.

Decision framework for evaluating the macroeconomic risks and policy impacts of cyber attacks

Abstract Increased reliance on the Internet for critical infrastructure and the global nature of supply chains provides an opportunity for adversaries to leverage dependencies and gain access to vital infrastructure. Traditional approaches to assessing risk in the cyber domain, including estimation of impacts, fall short due to uncertainty in how interconnected systems react to cyber attack. This paper describes a method to represent the pathways of disruption propagation, evaluate the macroeconomic impact of cyber threats and aid in selecting among various cybersecurity policies. Based on state of the art agent-based modeling, multicriteria decision analysis, and macroeconomic modeling tools, this framework provides dynamic macroeconomic, demographic and fiscal insights regarding shocks caused by cyber attacks to the regional economy over time. The interlinkage of these models will provide a robust and adaptive system that allows policy makers to evaluate complex issues such as cybersecurity threats and their impacts on the geopolitical, social, environmental, and macroeconomic landscape.

Chemical supply chain modeling for analysis of homeland security events

Abstract The potential impacts of man-made and natural disasters on chemical plants, complexes, and supply chains are of great importance to homeland security. To be able to estimate these impacts, we developed an agent-based chemical supply chain model that includes: chemical plants with enterprise operations such as purchasing, production scheduling, and inventories; merchant chemical markets, and multi-modal chemical shipments. Large-scale simulations of chemical-plant activities and supply chain interactions, running on desktop computers, are used to estimate the scope and duration of disruptive-event impacts, and overall system resilience, based on the extent to which individual chemical plants can adjust their internal operations (e.g., production mixes and levels) versus their external interactions (market sales and purchases, and transportation routes and modes). To illustrate how the model estimates the impacts of a hurricane disruption, a simple example model centered on 1,4-butanediol is presented.

Thoughts on critical infrastructure collaboration

In this paper, we describe what we believe to be the characteristics of the collaborations required in the domain of critical infrastructure modeling, based on our experiences to date. We adopt a knowledge management philosophy, which imposes two classes of requirements, contextual who, when, and why), and semantic what interactions are conducted around). We observe that infrastructure models can often engender more insight when used as the basis for a meaningful discussion between the disparate stakeholder groups (private industry, trade organizations, industry lobbying groups, etc.) than when exercised computationally.

Risk-based decision making for staggered bioterrorist attacks: Resource allocation and risk reduction in "reload" scenarios

Staggered bioterrorist attacks with aerosolized pathogens on population centers present a formidable challenge to resource allocation and response planning. The response and planning will commence immediately after the detection of the first attack and with no or little information of the second attack. In this report, we outline a method by which resource allocation may be performed. It involves probabilistic reconstruction of the bioterrorist attack from partial observations of the outbreak, followed by an optimization-under-uncertainty approach to perform resource allocations. We consider both single-site and time-staggered multi-site attacks (i.e., a reload scenario) under conditions when resources (personnel and equipment which are difficult to gather and transport) are insufficient. Both communicable (plague) and non-communicable diseases (anthrax) are addressed, and we also consider cases when the data, the time-series of people reporting with symptoms, are confounded with a reporting delay. We demonstrate how our approach develops allocations profiles that have the potential to reduce the probability of an extremely adverse outcome in exchange for a more certain, but less adverse outcome. We explore the effect of placing limits on daily allocations. Further, since our method is data-driven, the resource allocation progressively improves as more data becomes available.

Comparative analysis of the transportation of select petrochemicals

Large volumes of hazardous cargo move across US roadways daily. Field counts are conducted on sections of roadway that provide aggregated volumes. A high level of interest exists in acquiring the capability to replicate these periodic field counts through a computer-simulated programme. Base data and computer simulation models were identified, which could be coordinated with field counts. The corridor traversing the core of Houston’s petrochemical industry served as the study area. Simulation data on producers and chemicals within the area were analysed, and alternate means of transportation were identified, to select chemicals most likely to be transported to the geographic area by truck. The likely frequency of shipments was calculated and compared to the field data. The study outcome reflected a good fit between the simulation and the field counts, providing positive conditions for application of the model to other corridors and showing strong potential for its predictive value.

REAcct: a scenario analysis tool for rapidly estimating economic impacts of major natural and man-made hazards

The REAcct (for Regional Economic Accounting) tool was developed for estimating order-of-magnitude economic impacts within DHS scenario analysis. REAcct uses input–output modeling, geo-spatial data computational tools, and publically available economic data and allows for detailed specification of sectors, regions, and disruption intervals. Direct impacts are estimated as changes in output and employment; total (direct plus indirect) impacts are calculated using regional total and final demand multipliers. In total, the REAcct framework and software allow for scenario analysts to understand and assess the interdependent relationships between critical infrastructures, economic industries, and consumers that are essential to broader homeland security scenario analysis.