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

Author

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  • Haghnevis, Moeed
  • Askin, Ronald G.
  • Armbruster, Dieter

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.

Suggested Citation

  • Haghnevis, Moeed & Askin, Ronald G. & Armbruster, Dieter, 2016. "An agent-based modeling optimization approach for understanding behavior of engineered complex adaptive systems," Socio-Economic Planning Sciences, Elsevier, vol. 56(C), pages 67-87.
    • Handle: RePEc:eee:soceps:v:56:y:2016:i:c:p:67-87
    DOI: 10.1016/j.seps.2016.04.003
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    as
    1. Derek W. Bunn and Fernando Oliveira, 2001. "An Application of Agent-based Simulation to the New Electricity Trading Arrangements of England and Wales," Computing in Economics and Finance 2001 93, Society for Computational Economics.
    2. Bunn, Derek W. & Oliveira, Fernando S., 2007. "Agent-based analysis of technological diversification and specialization in electricity markets," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1265-1278, September.
    3. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
    4. Bunn, Derek W. & Martoccia, Maria, 2005. "Unilateral and collusive market power in the electricity pool of England and Wales," Energy Economics, Elsevier, vol. 27(2), pages 305-315, March.
    5. Kathleen Spees & Lester Lave, 2008. "Impacts of Responsive Load in PJM: Load Shifting and Real Time Pricing," The Energy Journal, , vol. 29(2), pages 101-122, April.
    6. Page, Scott E, 2001. "Self Organization and Coordination," Computational Economics, Springer;Society for Computational Economics, vol. 18(1), pages 25-48, August.
    7. Luis A. Nunes Amaral & Brian Uzzi, 2007. "Complex Systems--A New Paradigm for the Integrative Study of Management, Physical, and Technological Systems," Management Science, INFORMS, vol. 53(7), pages 1033-1035, July.
    8. Derek Bunn & Fernando Oliveira, 2003. "Evaluating Individual Market Power in Electricity Markets via Agent-Based Simulation," Annals of Operations Research, Springer, vol. 121(1), pages 57-77, July.
    9. Li, Hongyan & Tesfatsion, Leigh S., 2009. "The AMES Wholesale Power Market Test Bed: A Computational Laboratory for Research, Teaching, and Training," Staff General Research Papers Archive 13073, Iowa State University, Department of Economics.
    10. Nobuyuki Hanaki & Alexander Peterhansl & Peter S. Dodds & Duncan J. Watts, 2007. "Cooperation in Evolving Social Networks," Management Science, INFORMS, vol. 53(7), pages 1036-1050, July.
    11. Bunn, Derek W. & Day, Christopher J., 2009. "Computational modelling of price formation in the electricity pool of England and Wales," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 363-376, February.
    12. Faruqui, Ahmad & Hledik, Ryan & Newell, Sam & Pfeifenberger, Hannes, 2007. "The Power of 5 Percent," The Electricity Journal, Elsevier, vol. 20(8), pages 68-77, October.
    13. J. M. Ottino, 2004. "Engineering complex systems," Nature, Nature, vol. 427(6973), pages 399-399, January.
    14. Allcott, Hunt, 2011. "Social norms and energy conservation," Journal of Public Economics, Elsevier, vol. 95(9-10), pages 1082-1095, October.
    15. Zhang, T. & Nuttall, W.J., 2008. "Evaluating Government’s Policies on Promoting Smart Metering in Retail Electricity Markets via Agent Based Simulation," Cambridge Working Papers in Economics 0842, Faculty of Economics, University of Cambridge.
    16. Weidlich, Anke & Veit, Daniel, 2008. "A critical survey of agent-based wholesale electricity market models," Energy Economics, Elsevier, vol. 30(4), pages 1728-1759, July.
    17. Hazhir Rahmandad & John Sterman, 2008. "Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models," Management Science, INFORMS, vol. 54(5), pages 998-1014, May.
    18. Ian Ayres & Sophie Raseman & Alice Shih, 2009. "Evidence from Two Large Field Experiments that Peer Comparison Feedback Can Reduce Residential Energy Usage," NBER Working Papers 15386, National Bureau of Economic Research, Inc.
    19. Ma, Tieju & Nakamori, Yoshiteru, 2009. "Modeling technological change in energy systems – From optimization to agent-based modeling," Energy, Elsevier, vol. 34(7), pages 873-879.
    20. Allcott, Hunt, 2011. "Social norms and energy conservation," Journal of Public Economics, Elsevier, vol. 95(9), pages 1082-1095.
    21. Brown, Richard E. & Koomey, Jonathan G., 2003. "Electricity use in California: past trends and present usage patterns," Energy Policy, Elsevier, vol. 31(9), pages 849-864, July.
    22. Vijay Mahajan & Eitan Muller & Frank M. Bass, 1995. "Diffusion of New Products: Empirical Generalizations and Managerial Uses," Marketing Science, INFORMS, vol. 14(3_supplem), pages 79-88.
    23. Sensfuß, Frank & Ragwitz, Mario & Genoese, Massimo & Möst, Dominik, 2007. "Agent-based simulation of electricity markets: a literature review," Working Papers "Sustainability and Innovation" S5/2007, Fraunhofer Institute for Systems and Innovation Research (ISI).
    24. Dan Braha & Yaneer Bar-Yam, 2007. "The Statistical Mechanics of Complex Product Development: Empirical and Analytical Results," Management Science, INFORMS, vol. 53(7), pages 1127-1145, July.
    25. Bower, John & Bunn, Derek W. & Wattendrup, Claus, 2001. "A model-based analysis of strategic consolidation in the German electricity industry," Energy Policy, Elsevier, vol. 29(12), pages 987-1005, October.
    26. Catherine Tucker, 2008. "Identifying Formal and Informal Influence in Technology Adoption with Network Externalities," Management Science, INFORMS, vol. 54(12), pages 2024-2038, December.
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