Modeling Animal Movement to Help Control PPRUpdate and Information Needs
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Competition is fierce and often the first to act has an advantage, especially in environments where there are excess resources. However, expanding quickly to absorb excess resources creates requirements that might be unmet in future conditions of scarcity. Different patterns of scarcity call for different strategies. We define a model of interacting specialists (entities) to analyze which sizing strategies are most successful in environments subjected to frequent periods of scarcity. We require entities to compete for a common resource whose scarcity changes periodically, then study the viability of entities following three different strategies through scarcity episodes of varying duration and intensity. The three sizing strategies are: aggressive, moderate, and conservative. Aggressive strategies are most effective when the episodes of scarcity are shorter and moderate; conversely, conservative strategies are most effective in cases of longer or more severe scarcity. © 2012 Springer-Verlag.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Complex adaptive systems (CAS) modeling has become a common tool to study the behavioral dynamics of agents in a broad range of disciplines from ecology to economics. Many modelers have studied structure's importance for a system in equilibrium, while others study the effects of perturbations on system dynamics. There is a notable absence of work on the effects of agent interaction pathways on perturbation dynamics. We present an agent-based CAS model of a competitive economic environment. We use this model to study the perturbation dynamics of simple structures by introducing a series of disruptive events and observing key system metrics. Then, we generate more complex networks by combining the simple component structures and analyze the resulting dynamics. We find the local network structure of a perturbed node to be a valuable indicator of the system response. © 2012 Springer-Verlag.
Complex Adaptive Systems of Systems, or CASoS, are vastly complex physical-socio-technical systems which we must understand to design a secure future for the nation. The Phoenix initiative implements CASoS Engineering principles combining the bottom up Complex Systems and Complex Adaptive Systems view with the top down Systems Engineering and System-of-Systems view. CASoS Engineering theory and practice must be conducted together to develop a discipline that is grounded in reality, extends our understanding of how CASoS behave and allows us to better control the outcomes. The pull of applications (real world problems) is critical to this effort, as is the articulation of a CASoS Engineering Framework that grounds an engineering approach in the theory of complex adaptive systems of systems. Successful application of the CASoS Engineering Framework requires modeling, simulation and analysis (MS and A) capabilities and the cultivation of a CASoS Engineering Community of Practice through knowledge sharing and facilitation. The CASoS Engineering Environment, itself a complex adaptive system of systems, constitutes the two platforms that provide these capabilities.
International Journal of Critical Infrastructures
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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