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A Survey of Agent-Based Modeling Practices (January 1998 to July 2008)



In the 1990s, Agent-Based Modeling (ABM) began gaining popularity and represents a departure from the more classical simulation approaches. This departure, its recent development and its increasing application by non-traditional simulation disciplines indicates the need to continuously assess the current state of ABM and identify opportunities for improvement. To begin to satisfy this need, we surveyed and collected data from 279 articles from 92 unique publication outlets in which the authors had constructed and analyzed an agent-based model. From this large data set we establish the current practice of ABM in terms of year of publication, field of study, simulation software used, purpose of the simulation, acceptable validation criteria, validation techniques and complete description of the simulation. Based on the current practice we discuss six improvements needed to advance ABM as an analysis tool. These improvements include the development of ABM specific tools that are independent of software, the development of ABM as an independent discipline with a common language that extends across domains, the establishment of expectations for ABM that match their intended purposes, the requirement of complete descriptions of the simulation so others can independently replicate the results, the requirement that all models be completely validated and the development and application of statistical and non-statistical validation techniques specifically for ABM.

Suggested Citation

  • 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.
  • Handle: RePEc:jas:jasssj:2008-77-2

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    References listed on IDEAS

    1. John H. Miller & Scott E. Page, 2007. "Social Science in Between, from Complex Adaptive Systems: An Introduction to Computational Models of Social Life," Introductory Chapters,in: Complex Adaptive Systems: An Introduction to Computational Models of Social Life Princeton University Press.
    2. John H. Miller & Scott E. Page, 2007. "Complexity in Social Worlds, from Complex Adaptive Systems: An Introduction to Computational Models of Social Life," Introductory Chapters,in: Complex Adaptive Systems: An Introduction to Computational Models of Social Life Princeton University Press.
    3. Robert Axtell & Robert Axelrod & Joshua M. Epstein & Michael D. Cohen, 1995. "Aligning Simulation Models: A Case Study and Results," Working Papers 95-07-065, Santa Fe Institute.
    4. William T. Morris, 1967. "On the Art of Modeling," Management Science, INFORMS, vol. 13(12), pages 707-717, August.
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    1. repec:eee:ecomod:v:273:y:2014:i:c:p:284-298 is not listed on IDEAS
    2. Cathérine Grisar & Matthias Meyer, 2016. "Use of simulation in controlling research: a systematic literature review for German-speaking countries," Management Review Quarterly, Springer;Vienna University of Economics and Business, vol. 66(2), pages 117-157, April.
    3. Schubring, Sandra & Lorscheid, Iris & Meyer, Matthias & Ringle, Christian M., 2016. "The PLS agent: Predictive modeling with PLS-SEM and agent-based simulation," Journal of Business Research, Elsevier, vol. 69(10), pages 4604-4612.
    4. Robert Somogyi & Janos Vincze, 2011. "Price Rigidity and Strategic Uncertainty An Agent-based Approach," IEHAS Discussion Papers 1135, Institute of Economics, Centre for Economic and Regional Studies, Hungarian Academy of Sciences.
    5. Onggo, Bhakti Stephan & Karatas, Mumtaz, 2016. "Test-driven simulation modelling: A case study using agent-based maritime search-operation simulation," European Journal of Operational Research, Elsevier, vol. 254(2), pages 517-531.
    6. repec:eee:ecomod:v:222:y:2011:i:19:p:3486-3499 is not listed on IDEAS
    7. Roman Šperka, 2015. "Simulation Methods Comparison in Business Process Domain," Working Papers 0019, Silesian University, School of Business Administration.
    8. Andreas Deckert & Robert Klein, 2014. "Simulation-based optimization of an agent-based simulation," Netnomics, Springer, vol. 15(1), pages 33-56, July.
    9. Vincze, János & Varga, Gergely, 2016. "Megtakarítási típusok - egy adaptív-evolúciós megközelítés
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      ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(2), pages 162-187.
    10. Hassani-Mahmooei, Behrooz & Parris, Brett W., 2013. "Resource scarcity, effort allocation and environmental security: An agent-based theoretical approach," Economic Modelling, Elsevier, vol. 30(C), pages 183-192.
    11. repec:eee:tefoso:v:127:y:2018:i:c:p:38-56 is not listed on IDEAS
    12. Hansen, Mette Sanne & Rasmussen, Lauge Baungaard & Jacobsen, Peter, 2016. "Interactive foresight simulation," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 214-227.
    13. Wang, Ziyi & Wennersten, Ronald & Sun, Qie, 2017. "Outline of principles for building scenarios – Transition toward more sustainable energy systems," Applied Energy, Elsevier, vol. 185(P2), pages 1890-1898.
    14. Rixen, Martin & Weigand, Jürgen, 2014. "Agent-based simulation of policy induced diffusion of smart meters," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 153-167.
    15. repec:spr:waterr:v:31:y:2017:i:10:d:10.1007_s11269-017-1734-2 is not listed on IDEAS


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