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Simulation-based optimization of an agent-based simulation


  • Andreas Deckert


  • Robert Klein



Optimization of an agent-based simulation (ABS) bears specific challenges. It is demonstrated in this paper that mainstream simulation-based optimization (SBO) approaches often do not perform well in such a setting, sometimes hardly outperforming a mere random search. Two new algorithms for SBO which combine superior solution quality with high resource efficiency and reliability for such problems are presented: an evolutionary algorithm called “neighbourhood elite selection” (NELS) with a specific selection mechanism which prevents premature clustering, and a hybrid algorithm which combines NELS with the popular best-in-class algorithm Simultaneous Perturbation Stochastic Approximation (SPSA). Those two algorithms are designed to perform well for problems which show typical properties of an agent-based simulation, a field that has largely been neglected so far, but should structurally also be universally applicable for other simulation-based optimization problems as well. In contrast to present literature, specific emphasis lies on the dynamic control of how many replications of the simulation are required for each solution brought up during the optimization run in order to make efficient use of the scarce simulation resources. The algorithms are benchmarked against the academic best-in-class optimization algorithm SPSA. A sketch of practical case studies is provided, showing how the optimization of an ABS can be used to help solve business decision problems like price optimization for a mobile phone operator. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Andreas Deckert & Robert Klein, 2014. "Simulation-based optimization of an agent-based simulation," Netnomics, Springer, vol. 15(1), pages 33-56, July.
  • Handle: RePEc:kap:netnom:v:15:y:2014:i:1:p:33-56
    DOI: 10.1007/s11066-013-9083-7

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    References listed on IDEAS

    1. Cha, Kyoung Cheon & Jun, Duk Bin & Wilson, Amy R. & Park, Young Sun, 2008. "Managing and modeling the price reduction effect in mobile telecommunications traffic," Telecommunications Policy, Elsevier, vol. 32(7), pages 468-479, August.
    2. Luigi Bonaventura, 2011. "Enforcement of regulation, irregular sector, and firm performance: a computational agent-based model," Netnomics, Springer, vol. 12(2), pages 99-113, July.
    3. Daniel Birke & G. Swann, 2006. "Network effects and the choice of mobile phone operator," Journal of Evolutionary Economics, Springer, vol. 16(1), pages 65-84, April.
    4. Hélène Le Cadre & Mustapha Bouhtou & Bruno Tuffin, 2009. "Consumers’ preference modeling to price bundle offers in the telecommunications industry: a game with competition among operators," Netnomics, Springer, vol. 10(2), pages 171-208, October.
    5. Corrocher, Nicoletta & Zirulia, Lorenzo, 0. "Me and you and everyone we know: An empirical analysis of local network effects in mobile communications," Telecommunications Policy, Elsevier, vol. 33(1-2), pages 68-79, February.
    6. Brian Heath & Raymond Hill & Frank Ciarallo, 2009. "A Survey of Agent-Based Modeling Practices (January 1998 to July 2008)," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 1-9.
    7. Gruber,Harald, 2005. "The Economics of Mobile Telecommunications," Cambridge Books, Cambridge University Press, number 9780521843270.
    8. Sebastian Schutte, 2010. "Optimization and Falsification in Empirical Agent-Based Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(1), pages 1-2.
    9. Arnon Tonmukayakul & Martin Weiss, 2008. "A study of secondary spectrum use using agent-based computational economics," Netnomics, Springer, vol. 9(2), pages 125-151, October.
    10. Sven Schade & Thorsten Frey & Nezar Mahmoud, 2009. "Simulating Discount-Pricing Strategies for the GSM-Mobile Market," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 1(4), pages 289-300, August.
    11. Theodore Tsekeris & Klimis Vogiatzoglou & Stelios Bekiros, 2011. "Multi-Regional Agent-Based Modeling of Household and Firm Location Choices with Endogenous Transport Costs," ERSA conference papers ersa10p479, European Regional Science Association.
    12. Tesfatsion, Leigh S., 2002. "Agent-Based Computational Economics: Growing Economies from the Bottom Up," Staff General Research Papers Archive 5075, Iowa State University, Department of Economics.
    13. Thiago Noronha & Mauricio Resende & Celso Ribeiro, 2011. "A biased random-key genetic algorithm for routing and wavelength assignment," Journal of Global Optimization, Springer, vol. 50(3), pages 503-518, July.
    14. Schade, Sven & Frey, Thorsten & Mahmoud, Nezar, 2009. "Simulating Discount-Pricing Strategies for the GSM-Mobile Market," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 50717, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    15. Lin, Xiaocang & Lee, Loo Hay, 2006. "A new approach to discrete stochastic optimization problems," European Journal of Operational Research, Elsevier, vol. 172(3), pages 761-782, August.
    16. Theodore Tsekeris & Klimis Vogiatzoglou, 2011. "Spatial agent-based modeling of household and firm location with endogenous transport costs," Netnomics, Springer, vol. 12(2), pages 77-98, July.
    17. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, March.
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