Publications

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Interactions between individuals, both economic and social, are increasingly mediated by technological systems. Such platforms facilitate interactions by controlling and regularizing access, while extracting rent from users. The relatively recent idea of two-sided markets has given insights into the distinctive economic features of such arrangements, arising from network effects and the power of the platform operator. Simplifications required to obtain analytical results, while leading to basic understanding, prevent us from posing many important questions. For example we would like to understand how platforms can be secured when the costs and benefits of security differ greatly across users and operators, and when the vulnerabilities of particular designs may only be revealed after they are in wide use. We define an agent-based model that removes many constraints limiting existing analyses (such as uniformity of users, free and perfect information), allowing insights into a much larger class of real systems. © 2012 Springer-Verlag.

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.

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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.

Changes in climate can lead to instabilities in physical and economic systems, particularly in regions with marginal resources. Global climate models indicate increasing global mean temperatures over the decades to come and uncertainty in the local to national impacts means perceived risks will drive planning decisions. Agent-based models provide one of the few ways to evaluate the potential changes in behavior in coupled social-physical systems and to quantify and compare risks. The current generation of climate impact analyses provides estimates of the economic cost of climate change for a limited set of climate scenarios that account for a small subset of the dynamics and uncertainties. To better understand the risk to national security, the next generation of risk assessment models must represent global stresses, population vulnerability to those stresses, and the uncertainty in population responses and outcomes that could have a significant impact on U.S. national security.

Current protection strategies against insider adversaries are expensive, intrusive, not systematically implemented, and operate independently; too often, these strategies are defeated. The authors discuss the development of methods for a systems-based approach to insider security. To investigate insider evolution within an organization, they use system dynamics to develop a preliminary model of the employee life cycle that defines and analyzes the employee population's interactions with insider security protection strategies. The authors exercised the model for an example scenario that focused on human resources and personnel security activitiesspecifically, prehiring screening and security clearance processes. The model provides a framework for understanding important interactions, interdependencies, and gaps in insider protection strategies. This work provides the basis for developing an integrated systems-based process for buildingthat is, designing, evaluating, and operatinga system for effective insider security. © 2009 IEEE.

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The telecommunication network is recognized by the federal government as one of the critical national infrastructures that must be maintained and protected against debilitating attacks. We have previously shown how failures in the telecommunication network can quickly lead to telecommunication congestion and to extended delays in successful call completion. However, even if the telecom network remains fully operational, the special telecommunication demands that materialize at times of emergencies, and dynamically change based on subscriber behavior, can also adversely affect the performance of the overall telecommunication network. The Network Simulation Modeling and Analysis Research Tool (N-SMART) has been developed by Bell Labs as part of its work with the National Infrastructure Simulation and Analysis Center. This center is a joint program at Sandia National Laboratories and Los Alamos National Laboratory, funded and managed by the Department of Homeland Security's (DHS) Preparedness Directorate. N-SMART is a discrete event (call level) telecom model that simulates capacities, blocking levels, retrials, and time to complete calls for both wireline and wireless networks. N-SMART supports the capability of simulating subscriber reattempt behaviour under various scenarios. Using this capability we show how the network can be adversely impacted by sudden changes in subscriber behavior. We also explore potential solutions and ways of mitigating those impacts.

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