Embedding System Dynamics in Agent Based Models for Complex Adaptive Systems.
ABSTRACT 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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ABSTRACT: Both micro- and macro-level views are essential for complex systems research.•A classification is formulated to guide efforts in integrating both views.•A new hybrid approach is developed for multimodal inter-city travel demand.•The model is validated and replicates historical trend of aviation demand.•Demand sensitivity to socioeconomics match showing the congruity of ABM and SD.Transportation Research Part E Logistics and Transportation Review 11/2014; 71. · 2.27 Impact Factor