Embedding System Dynamics in Agent Based Models for Complex Adaptive Systems
Kiyan Ahmadizadeh, Maarika Teose, Eoin O’Mahony and Carla Gomes
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 traditional practice to a novel intervention strategy. Our experimental results, unattainable using previous methods, yield insight into the effectiveness of these intervention strategies. Finally, to illustrate the potential for exploiting the strengths of this class of models we formulate two optimization problems focused on identifying optimal intervention strategies.