An agent-based modeling optimization approach for understanding behavior of engineered complex adaptive systems

The objective of this study is to present a formal agent-based modeling (ABM) platform that enables managers to predict and partially control patterns of behaviors in certain engineered complex adaptive systems (ECASs). The approach integrates social networks, social science, complex systems, and diffusion theory into a consumer-based optimization and agent-based modeling (ABM) platform. Demonstrated on the U.S. electricity markets, ABM is integrated with normative and subjective decision behavior recommended by the U.S. Department of Energy (DOE) and Federal Energy Regulatory Commission (FERC). Furthermore, the modeling and solution methodology address shortcomings in previous ABM and Transactive Energy (TE) approaches and advances our ability to model and understand ECAS behaviors through computational intelligence. The mathematical approach is a non-convex consumer-based optimization model that is integrated with an ABM in a game environment.