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

作     者：Haghnevis, Moeed Askin, Ronald G. Armbruster, Dieter 

作者机构：Arizona State Univ Sch Comp Informat & Decis Syst Engn Tempe AZ 85287 USA Arizona State Univ Sch Math & Stat Sci Tempe AZ 85287 USA 

出 版 物：《SOCIO-ECONOMIC PLANNING SCIENCES》 (社会－经济计划科学)

年 卷 期：2016年第56卷

页      面：67-87页

核心收录：

学科分类：0303[法学-社会学] 12[管理学] 1204[管理学-公共管理] 03[法学] 0201[经济学-理论经济学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 030301[法学-社会学] 

主　　题：Agent-based modeling and simulation Complex adaptive systems Non-linear complexity US electricity markets Demand response Transactive energy 

摘      要：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. (C) 2016 Elsevier Ltd. All rights reserved.