IDEAS home Printed from https://ideas.repec.org/a/jas/jasssj/2008-77-2.html
   My bibliography  Save this article

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

Author

Abstract

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
    as

    Download full text from publisher

    File URL: http://jasss.soc.surrey.ac.uk/12/4/9/9.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    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, pages 707-717.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Wang, Ziyi & Wennersten, Ronald & Sun, Qie, 2017. "Outline of principles for building scenarios – Transition toward more sustainable energy systems," Applied Energy, Elsevier, pages 1890-1898.
    4. 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.
    5. Vincze, János & Varga, Gergely, 2016. "Megtakarítási típusok - egy adaptív-evolúciós megközelítés
      [Types of saving - an adaptive-evolutionary approach]
      ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(2), pages 162-187.
    6. 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.
    7. repec:spr:waterr:v:31:y:2017:i:10:d:10.1007_s11269-017-1734-2 is not listed on IDEAS
    8. repec:eee:ecomod:v:273:y:2014:i:c:p:284-298 is not listed on IDEAS
    9. 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.
    10. Roman Šperka, 2015. "Simulation Methods Comparison in Business Process Domain," Working Papers 0019, Silesian University, School of Business Administration.
    11. 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.
    12. repec:eee:ecomod:v:222:y:2011:i:19:p:3486-3499 is not listed on IDEAS
    13. Andreas Deckert & Robert Klein, 2014. "Simulation-based optimization of an agent-based simulation," Netnomics, Springer, vol. 15(1), pages 33-56, July.
    14. Hansen, Mette Sanne & Rasmussen, Lauge Baungaard & Jacobsen, Peter, 2016. "Interactive foresight simulation," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 214-227.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jas:jasssj:2008-77-2. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Flaminio Squazzoni). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.