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A general model of resource production and exchange in systems of interdependent specialists

Beyeler, Walter E.; Glass, Robert J.; Finley, Patrick D.; Quach, Tu-Thach Q.

Infrastructures are networks of dynamically interacting systems designed for the flow of information, energy, and materials. Under certain circumstances, disturbances from a targeted attack or natural disasters can cause cascading failures within and between infrastructures that result in significant service losses and long recovery times. Reliable interdependency models that can capture such multi-network cascading do not exist. The research reported here has extended Sandia's infrastructure modeling capabilities by: (1) addressing interdependencies among networks, (2) incorporating adaptive behavioral models into the network models, and (3) providing mechanisms for evaluating vulnerability to targeted attack and unforeseen disruptions. We have applied these capabilities to evaluate the robustness of various systems, and to identify factors that control the scale and duration of disruption. This capability lays the foundation for developing advanced system security solutions that encompass both external shocks and internal dynamics.

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Complex Adaptive System of Systems (CASoS) Engineering Applications. Version 1.0

Brown, Theresa J.; Glass, Robert J.; Beyeler, Walter E.; Ames, Arlo L.; Linebarger, John M.

Complex Adaptive Systems of Systems, or CASoS, are vastly complex eco-socio-economic-technical systems which we must understand to design a secure future for the nation and the world. Perturbations/disruptions in CASoS have the potential for far-reaching effects due to highly-saturated interdependencies and allied vulnerabilities to cascades in associated systems. The Phoenix initiative approaches this high-impact problem space as engineers, devising interventions (problem solutions) that influence CASoS to achieve specific aspirations. CASoS embody the world's biggest problems and greatest opportunities: applications to real world problems are the driving force of our effort. We are developing engineering theory and practice together to create a discipline that is grounded in reality, extends our understanding of how CASoS behave, and allows us to better control those behaviors. Through application to real-world problems, Phoenix is evolving CASoS Engineering principles while growing a community of practice and the CASoS engineers to populate it.

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Phoenix : Complex Adaptive System of Systems (CASoS) engineering version 1.0

Glass, Robert J.; Ames, Arlo L.; Brown, Theresa J.; Linebarger, John M.; Beyeler, Walter E.

Complex Adaptive Systems of Systems, or CASoS, are vastly complex ecological, sociological, economic and/or technical systems which we must understand to design a secure future for the nation and the world. Perturbations/disruptions in CASoS have the potential for far-reaching effects due to pervasive interdependencies and attendant vulnerabilities to cascades in associated systems. Phoenix was initiated to address this high-impact problem space as engineers. Our overarching goals are maximizing security, maximizing health, and minimizing risk. We design interventions, or problem solutions, that influence CASoS to achieve specific aspirations. Through application to real-world problems, Phoenix is evolving the principles and discipline of CASoS Engineering while growing a community of practice and the CASoS engineers to populate it. Both grounded in reality and working to extend our understanding and control of that reality, Phoenix is at the same time a solution within a CASoS and a CASoS itself.

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Congestion and cascades in payment systems

Physica A: Statistical Mechanics and its Applications

Beyeler, Walter E.; Glass, Robert J.; Bech, Morten L.; Soramäki, Kimmo

We develop a parsimonious model of the interbank payment system. The model incorporates an endogenous instruction arrival process, a scale-free topology of payments between banks, a fixed total liquidity which limits banks' capacity to process arriving instructions, and a global market that distributes liquidity. We find that at low liquidity the system becomes congested and payment settlement loses correlation with payment instruction arrival, becoming coupled across the network. The onset of congestion is evidently related to the relative values of three characteristic times: the time for banks' net position to return to 0, the time for a bank to exhaust its liquidity endowment, and the liquidity market relaxation time. In the congested regime settlement takes place in cascades having a characteristic length scale. A global liquidity market substantially attenuates congestion, requiring only a small fraction of the payment-induced liquidity flow to achieve strong beneficial effects. © 2007 Elsevier B.V. All rights reserved.

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Toward modeling and simulation of critical national infrastructure interdependencies

Proposed for publication in IIE Transactions.

Beyeler, Walter E.; Brown, Theresa J.

Modern society's physical health depends vitally upon a number of real, interdependent, critical infrastructure networks that deliver power, petroleum, natural gas,water, and communications. Its economic health depends on a number of other infrastructure networks, some virtual and some real, that link residences, industries, commercial sectors, and transportation sectors. The continued prosperity and national security of the US depends on our ability to understand the vulnerabilities of and analyze the performance of both the individual infrastructures and the entire interconnected system of infrastructures. Only then can we respond to potential disruptions in a timely and effective manner. Collaborative efforts among Sandia, other government agencies, private industry, and academia have resulted in realistic models for many of the individual component infrastructures. In this paper, we propose an innovative modeling and analysis framework to study the entire system of physical and economic infrastructures. That framework uses the existing individual models together with system dynamics, functional models, and nonlinear optimization algorithms. We describe this framework and demonstrate its potential use to analyze, and propose a response for, a hypothetical disruption.

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Results 76–100 of 104
Results 76–100 of 104