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

Moeed Haghnevis, Ronald Askin, Hans Armbruster

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
JournalSocio-Economic Planning Sciences
DOIs
StateAccepted/In press - Feb 16 2015

Keywords

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

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Statistics, Probability and Uncertainty
  • Economics and Econometrics
  • Geography, Planning and Development

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