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: https://www.jasss.org/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. 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, April.
    4. William T. Morris, 1967. "On the Art of Modeling," Management Science, INFORMS, vol. 13(12), pages 707-717, August.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.
    2. Gräbner, Claudius, 2016. "From realism to instrumentalism - and back? Methodological implications of changes in the epistemology of economics," MPRA Paper 71933, University Library of Munich, Germany.
    3. Luis R. Izquierdo & Segismundo S. Izquierdo & José Manuel Galán & José Ignacio Santos, 2009. "Techniques to Understand Computer Simulations: Markov Chain Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-6.
    4. van de Kaa, Geerten & de Bruijn, Hans, 2015. "Platforms and incentives for consensus building on complex ICT systems: The development of WiFi," Telecommunications Policy, Elsevier, vol. 39(7), pages 580-589.
    5. Pluchino, Alessandro & Rapisarda, Andrea & Garofalo, Cesare, 2010. "The Peter principle revisited: A computational study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(3), pages 467-472.
    6. Christopher S. Ruebeck & Leanne J. Ussher & Jason M. Barr, 2017. "Introduction to the Symposium on Agent-based Modeling," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 43(2), pages 189-191, March.
    7. Changkun Zhao & Ryan Kaulakis & Jonathan H. Morgan & Jeremiah W. Hiam & Frank E. Ritter & Joesph Sanford & Geoffrey P. Morgan, 2015. "Building social networks out of cognitive blocks: factors of interest in agent-based socio-cognitive simulations," Computational and Mathematical Organization Theory, Springer, vol. 21(2), pages 115-149, June.
    8. Held, Fabian P. & Wilkinson, Ian F. & Marks, Robert E. & Young, Louise, 2014. "Agent-based Modelling, a new kind of research," Australasian marketing journal, Elsevier, vol. 22(1), pages 4-14.
    9. Jeannette A. Colyvas & Spiro Maroulis, 2015. "Moving from an Exception to a Rule: Analyzing Mechanisms in Emergence-Based Institutionalization," Organization Science, INFORMS, vol. 26(2), pages 601-621, April.
    10. Rand, William & Rust, Roland T., 2011. "Agent-based modeling in marketing: Guidelines for rigor," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 181-193.
    11. Florian Chávez-Juárez, 2017. "On the Role of Agent-based Modeling in the Theory of Development Economics," Review of Development Economics, Wiley Blackwell, vol. 21(3), pages 713-730, August.
    12. Andrew W. Bausch, 2014. "Evolving intergroup cooperation," Computational and Mathematical Organization Theory, Springer, vol. 20(4), pages 369-393, December.
    13. Situngkir, Hokky & Lumbantobing, Andika Bernad, 2020. "The Pandemics in Artificial Society: Agent-Based Model to Reflect Strategies on COVID-19," MPRA Paper 102075, University Library of Munich, Germany.
    14. Wolfram Elsner, 2019. "Policy and state in complexity economics," Chapters, in: Nikolaos Karagiannis & John E. King (ed.), A Modern Guide to State Intervention, chapter 1, pages 13-48, Edward Elgar Publishing.
    15. James Caton, 2017. "Entrepreneurship, search costs, and ecological rationality in an agent-based economy," The Review of Austrian Economics, Springer;Society for the Development of Austrian Economics, vol. 30(1), pages 107-130, March.
    16. H. Fan, 2012. "Distribution Of Producer Size In Globalized Market," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 15(07), pages 1-24.
    17. Ning Nan & Robert Zmud & Emre Yetgin, 2014. "A complex adaptive systems perspective of innovation diffusion: an integrated theory and validated virtual laboratory," Computational and Mathematical Organization Theory, Springer, vol. 20(1), pages 52-88, March.
    18. Pluchino, Alessandro & Rapisarda, Andrea & Garofalo, Cesare, 2011. "Efficient promotion strategies in hierarchical organizations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3496-3511.
    19. An, Li, 2012. "Modeling human decisions in coupled human and natural systems: Review of agent-based models," Ecological Modelling, Elsevier, vol. 229(C), pages 25-36.
    20. Citera, Emanuele & Sau, Lino, 2019. "Complexity, Conventions and Instability: the role of monetary policy," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201924, University of Turin.

    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.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Francesco Renzini (email available below). General contact details of provider: .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.