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My research focuses on the complex adaptive behavior that emerges in social systems. The goal of this work is to understand the principles by which aggregate patterns emerge from the simple interactions of individual adaptive agents. The nonlinear and disequilibrium nature of complex adaptive systems often necessitates new methodological and theoretical directions. Methodologically, computational methods provide a convenient tool for modeling such systems. Theoretically, standard analytic tools, based on both linearity and equilibrium behavior, may be ill-tuned to further our understanding of complex systems. Thus, new approaches that emphasize nonlinearities and dynamics are needed.
To understand the behavior of complex adaptive systems, I have relied on the analysis of computational models composed of interacting artificial adaptive agents. The behavior of each agent in the system is dictated by a simple learning algorithm (e.g., genetic algorithm) that allows the agent to adaptively modify its actions from a set of behaviors rich in possibilities. This paradigm allows the analysis of flexible, yet precise, models of well-defined agents in an environment that can be easily and rapidly replicated and recovered. The ability to interact directly with such open-ended models allows one to quickly generate, develop and test new hypotheses.
My research focuses on the complex adaptive behavior that emerges in social systems. The goal of this work is to understand the principles by which aggregate patterns emerge from the simple interactions of individual adaptive agents. The nonlinear and disequilibrium nature of complex adaptive systems often necessitates new methodological and theoretical directions. Methodologically, computational methods provide a convenient tool for modeling such systems. Theoretically, standard analytic tools, based on both linearity and equilibrium behavior, may be ill-tuned to further our understanding of complex systems. Thus, new approaches that emphasize nonlinearities and dynamics are needed.
To understand the behavior of complex adaptive systems, I have relied on the analysis of computational models composed of interacting artificial adaptive agents. The behavior of each agent in the system is dictated by a simple learning algorithm (e.g., genetic algorithm) that allows the agent to adaptively modify its actions from a set of behaviors rich in possibilities. This paradigm allows the analysis of flexible, yet precise, models of well-defined agents in an environment that can be easily and rapidly replicated and recovered. The ability to interact directly with such open-ended models allows one to quickly generate, develop and test new hypotheses.
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Research Square (Research Square) (2023)
Heather E. Stefanski,Jennifer Newman, Jennifer Novakovich,Jacklyn Barten, Jason Oakes,Meghann Cody,Jeffery James Auletta,Huy Pham,John P. Miller,Steven M. Devine
Nektarios Vasilottos, Awaneesh Kumar, Cody Mccoy, Tanyanan Tanawuttiwat,John M. Miller,Mithilesh Das
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