Publications

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We present a model of behavioral dynamics that combines a social network-based opinion dynamics model with behavioral mapping. The behavioral component is discrete and history-dependent to represent situations in which an individual’s behavior is initially driven by opinion and later constrained by physiological or psychological conditions that serve to maintain the behavior. Individuals are modeled as nodes in a social network connected by directed edges. Parameter sweeps illustrate model behavior and the effects of individual parameters and parameter interactions on model results. Mapping a continuous opinion variable into a discrete behavioral space induces clustering on directed networks. Clusters provide targets of opportunity for influencing the network state; however, the smaller the network the greater the stochasticity and potential variability in outcomes. This has implications both for behaviors that are influenced by close relationships verses those influenced by societal norms and for the effectiveness of strategies for influencing those behaviors.

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Climate change, through drought, flooding, storms, heat waves, and melting Arctic ice, affects the production and flow of resource within and among geographical regions. The interactions among governments, populations, and sectors of the economy require integrated assessment based on risk, through uncertainty quantification (UQ). This project evaluated the capabilities with Sandia National Laboratories to perform such integrated analyses, as they relate to (inter)national security. The combining of the UQ results from climate models with hydrological and economic/infrastructure impact modeling appears to offer the best capability for national security risk assessments.

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This white paper makes a case for Sandia National Laboratories investments in complex adaptive systems science and technology (S&T) -- investments that could enable higher-value-added and more-robustly-engineered solutions to challenges of importance to Sandia's national security mission and to the nation. Complex adaptive systems are ubiquitous in Sandia's national security mission areas. We often ignore the adaptive complexity of these systems by narrowing our 'aperture of concern' to systems or subsystems with a limited range of function exposed to a limited range of environments over limited periods of time. But by widening our aperture of concern we could increase our impact considerably. To do so, the science and technology of complex adaptive systems must mature considerably. Despite an explosion of interest outside of Sandia, however, that science and technology is still in its youth. What has been missing is contact with real (rather than model) systems and real domain-area detail. With its center-of-gravity as an engineering laboratory, Sandia's has made considerable progress applying existing science and technology to real complex adaptive systems. It has focused much less, however, on advancing the science and technology itself. But its close contact with real systems and real domain-area detail represents a powerful strength with which to help complex adaptive systems science and technology mature. Sandia is thus both a prime beneficiary of, as well as potentially a prime contributor to, complex adaptive systems science and technology. Building a productive program in complex adaptive systems science and technology at Sandia will not be trivial, but a credible path can be envisioned: in the short run, continue to apply existing science and technology to real domain-area complex adaptive systems; in the medium run, jump-start the creation of new science and technology capability through Sandia's Laboratory Directed Research and Development program; and in the long run, inculcate an awareness at the Department of Energy of the importance of supporting complex adaptive systems science through its Office of Science.

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Difficulties in adequately characterising food supply chain topologies contribute major uncertainty to risk assessments of the food sector. The capability to trace contaminated foods forward (to consumers) and back (to providers) is needed for rapid recalls during food contamination events. The objective of this work is to develop an approach for risk mitigation that protects us from an attack on the food distribution system. This paper presents a general methodology for the stochastic mapping of fresh produce supply chains and an application to a single, relatively simple case - edible sprouts in one region. The case study demonstrates how mapping the network topology and modeling the potential relationships allows users to determine the likely contaminant pathways and sources of contamination. The stochastic network representation improves the ability to explicitly incorporate uncertainties and identify vulnerabilities. Copyright © 2012 Inderscience Enterprises Ltd.

Complex Adaptive Systems of Systems, or CASoS, are vastly complex ecological, sociological, economic and/or technical systems which must be recognized and reckoned with to design a secure future for the nation and the world. Design within CASoS requires the fostering of a new discipline, CASoS Engineering, and the building of capability to support it. Towards this primary objective, we created the Phoenix Pilot as a crucible from which systemization of the new discipline could emerge. Using a wide range of applications, Phoenix has begun building both theoretical foundations and capability for: the integration of Applications to continuously build common understanding and capability; a Framework for defining problems, designing and testing solutions, and actualizing these solutions within the CASoS of interest; and an engineering Environment required for 'the doing' of CASoS Engineering. In a secondary objective, we applied CASoS Engineering principles to begin to build a foundation for design in context of Global CASoS

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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.

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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Cigarette smoking presented the most significant public health challenge in the United States in the 20th Century and remains the single most preventable cause of morbidity and mortality in this country. A number of System Dynamics models exist that inform tobacco control policies. We reviewed them and discuss their contributions. We developed a theory of the societal lifecycle of smoking, using a parsimonious set of feedback loops to capture historical trends and explore future scenarios. Previous work did not explain the long-term historical patterns of smoking behaviors. Much of it used stock-and-flow to represent the decline in prevalence in the recent past. With noted exceptions, information feedbacks were not embedded in these models. We present and discuss our feedback-rich conceptual model and illustrate the results of a series of simulations. A formal analysis shows phenomena composed of different phases of behavior with specific dominant feedbacks associated with each phase. We discuss the implications of our society's current phase, and conclude with simulations of what-if scenarios. Because System Dynamics models must contain information feedback to be able to anticipate tipping points and to help identify policies that exploit leverage in a complex system, we expanded this body of work to provide an endogenous representation of the century-long societal lifecycle of smoking.

Pandemic influenza has become a serious global health concern; in response, governments around the world have allocated increasing funds to containment of public health threats from this disease. Pandemic influenza is also recognized to have serious economic implications, causing illness and absence that reduces worker productivity and economic output and, through mortality, robs nations of their most valuable assets - human resources. This paper reports two studies that investigate both the short- and long-term economic implications of a pandemic flu outbreak. Policy makers can use the growing number of economic impact estimates to decide how much to spend to combat the pandemic influenza outbreaks. Experts recognize that pandemic influenza has serious global economic implications. The illness causes absenteeism, reduced worker productivity, and therefore reduced economic output. This, combined with the associated mortality rate, robs nations of valuable human resources. Policy makers can use economic impact estimates to decide how much to spend to combat the pandemic influenza outbreaks. In this paper economists examine two studies which investigate both the short- and long-term economic implications of a pandemic influenza outbreak. Resulting policy implications are also discussed. The research uses the Regional Economic Modeling, Inc. (REMI) Policy Insight + Model. This model provides a dynamic, regional, North America Industrial Classification System (NAICS) industry-structured framework for forecasting. It is supported by a population dynamics model that is well-adapted to investigating macro-economic implications of pandemic influenza, including possible demand side effects. The studies reported in this paper exercise all of these capabilities.

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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.

Concepts from Complexity Science are valuable and allow a simulation approach for critical infrastructures that is flexible and has wide ranging applications.

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