Embedding System Dynamics in Agent Based Models for Complex Adaptive Systems.
DOI: 10.5591/978-1-57735-516-8/IJCAI11-421 Conference: IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011
Complex adaptive systems (CAS) are composed of interacting agents, exhibit nonlinear properties such as positive and negative feedback, and tend to produce emergent behavior that cannot be wholly explained by deconstructing the system into its constituent parts. Both system dynamics (equation-based) approaches and agent-based approaches have been used to model such systems, and each has its benefits and drawbacks. In this paper, we introduce a class of agent-based models with an embedded system dynamics model, and detail the semantics of a simulation framework for these models. This model definition, along with the simulation framework, combines agent-based and system dynamics approaches in a way that retains the strengths of both paradigms. We show the applicability of our model by instantiating it for two example complex adaptive systems in the field of Computational Sustainability, drawn from ecology and epidemiology. We then present a more detailed application in epidemiology, in which we compare a previously unstudied intervention strategy to established ones. Our experimental results, unattainable using previous methods, yield insight into the effectiveness of these intervention strategies.
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- "Developed by Jay Forrester in the late 1950s and applied to supply chains, industrial dynamics (Forrester, 1961) and urban dynamics (Forrester, 1969), main building blocks of SD are stocks and flows (Sterman, 2000; Phelan, 1999). The SD approach allows for easier model construction and validation but largely depends on assumptions about the homogeneity of modeling entities (Teose et al., 2011). Lyneis (2000) and Randers and Goluke (2007) advocate the use of SD models for forecasting in situations where there is a ''significant deterministic backbone in the system'' or dominant ''structural momentum'', which presupposes that the structure of the system determines future behavior with little uncertainty due to noise and complexity. "
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ABSTRACT: The aim of this article is to discuss how the systems science approach can be used to optimize intervention strategies in food animal systems. It advocates the idea that the challenges of maintaining a safe food supply are best addressed by integrating modeling and mathematics with biological studies critical to formulation of public policy to address these challenges. Much information on the biology and epidemiology of food animal systems has been characterized through single-discipline methods, but until now this information has not been thoroughly utilized in a fully integrated manner.Preventive Veterinary Medicine 08/2014; 118(2-3). DOI:10.1016/j.prevetmed.2014.08.013 · 2.17 Impact Factor
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