An agent-based simulation laboratory for economics and infrastructure interdependency

Schoenwald, David A.; Barton, Dianne C.; Ehlen, Mark Andrew

doi:10.23919/acc.2004.1386752

Cited by 15 publications

(6 citation statements)

References 3 publications

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“…Other modeling and simulation techniques such as agent based modeling (ABM) and system dynamics (SD) have also been employed to model interdependencies and complex adaptive behavior of CIS [30]. Some tools based on ABM and SD models include Aspen, Aspen-EE, CommAspen, and N-ABLE developed by Sandia National Laboratories [31][32][33][34][35][36][37]. While the above-mentioned methodologies are valuable, their application is restrictive because lack of CIS-related data hampers calibration of model parameters and functions, and inhibits validation of results [38].…”

Section: Introductionmentioning

confidence: 99%

Interconnectedness and interdependencies of critical infrastructures in the US economy: Implications for resilience

Chopra

Khanna

2015

Physica A: Statistical Mechanics and its Applications

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Section: Introductionmentioning

confidence: 99%

Interconnectedness and interdependencies of critical infrastructures in the US economy: Implications for resilience

Chopra

Khanna

2015

Physica A: Statistical Mechanics and its Applications

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“…Agents can learn how to behave based on a learning classifier system. ASPEN-EE and N-ABLE were later developed based on the ASPEN framework to extend its features and make it more powerful Schoenwald et al 2004). A more recent approach to the simulation of the interdependencies of critical infrastructure systems has resulted in an agent based simulation tool called CISIA (Panzieri et al 2005).…”

Section: Critical Infrastructure Modeling: Related Workmentioning

confidence: 99%

UML-CI: A reference model for profiling critical infrastructure systems

Bagheri

Ghorbani

2008

Inf Syst Front

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The study of critical infrastructure systems organization and behavior has drawn great attention in the recent years. This is in part due to their great influence on the ordinary life of every citizen. In this paper, we study critical infrastructures' characteristics and propose a reference model based on the Unified Modeling Language (UML). This reference model attempts to provide suitable means for the task of modeling an infrastructure system through offering five major metamodels. We introduce each of these metamodels and explain how it is possible to integrate them into a unique representation to characterize various aspects of an infrastructure system. Based on the metamodels of UML-CI, infrastructure system knowledge bases can be built to aid the process of infrastructure system modeling, profiling, and management.

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“…This happened because this substation was the only grid supply point to the underlying networks of dependent assets. To understand such disruptions across interconnected infrastructure different modeling techniques have been employed, which include, among others, agent‐based models of complex adaptive systems (Schoenwald, Barton, & Ehlen, ), empirical analysis (Utne, Hokstad, & Vatn, ), system dynamics approaches (Bush et al., ), network‐science based models (Zio, ), input–output (IO) economics (Oughton, Skelton, Horne, Thomson, & Gaunt, ; Santos et al., ), macroeconomic econometric methods (Oughton et al., ), and computational general equilibrium (CGE) models (Rose, Oladosu, & Liao, ; Rose & Wei, ). For a detailed literature review on different modeling techniques, see Ouyang ().…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic Counterfactual Risk Analysis for the Vulnerability Assessment of Cyber‐Physical Attacks on Electricity Distribution Infrastructure Networks

et al. 2019

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In December 2015, a cyber‐physical attack took place on the Ukrainian electricity distribution network. This is regarded as one of the first cyber‐physical attacks on electricity infrastructure to have led to a substantial power outage and is illustrative of the increasing vulnerability of Critical National Infrastructure to this type of malicious activity. Few data points, coupled with the rapid emergence of cyber phenomena, has held back the development of resilience analytics of cyber‐physical attacks, relative to many other threats. We propose to overcome data limitations by applying stochastic counterfactual risk analysis as part of a new vulnerability assessment framework. The method is developed in the context of the direct and indirect socioeconomic impacts of a Ukrainian‐style cyber‐physical attack taking place on the electricity distribution network serving London and its surrounding regions. A key finding is that if decision‐makers wish to mitigate major population disruptions, then they must invest resources more‐or‐less equally across all substations, to prevent the scaling of a cyber‐physical attack. However, there are some substations associated with higher economic value due to their support of other Critical National Infrastructures assets, which justifies the allocation of additional cyber security investment to reduce the chance of cascading failure. Further cyber‐physical vulnerability research must address the tradeoffs inherent in a system made up of multiple institutions with different strategic risk mitigation objectives and metrics of value, such as governments, infrastructure operators, and commercial consumers of infrastructure services.

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An agent-based simulation laboratory for economics and infrastructure interdependency

Cited by 15 publications

References 3 publications

Interconnectedness and interdependencies of critical infrastructures in the US economy: Implications for resilience

Interconnectedness and interdependencies of critical infrastructures in the US economy: Implications for resilience

UML-CI: A reference model for profiling critical infrastructure systems

Stochastic Counterfactual Risk Analysis for the Vulnerability Assessment of Cyber‐Physical Attacks on Electricity Distribution Infrastructure Networks

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