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"Anarchy" Reigns: A Quantitative Analysis of Agent-Based Modelling Publication Practices in JASSS, 2001-2012

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Abstract

Agent Based Modelling (ABM), a promising scientific toolset, has received criticism from some, in part, due to a claimed lack of scientific rigour, especially in the communication of its methods and results. To test the veracity of these claims, we conduct a structured analysis of over 900 scientific objects (figures, tables, or equations) that arose from 128 ABM papers published in the Journal of Artificial Societies and Social Simulation (JASSS), during the period 2001 to 2012 inclusive. Regrettably, we find considerable evidence in support of the detractors of ABM as a scientific enterprise: elementary plotting attributes are left off more often than not; basic information such as the number of replicates or the basis behind a particular statistic are not included; and few, if any, established methodological communication standards are apparent. In short, 'anarchy reigns'. Whilst the study was confined only to ABM papers of JASSS, we conclude that if the ABM community wishes its approach to be accepted further afield, authors, reviewers, and editors should take the results of our work as a wake-up call.

Suggested Citation

  • Simon Angus & Behrooz Hassani-Mahmooei, 2015. ""Anarchy" Reigns: A Quantitative Analysis of Agent-Based Modelling Publication Practices in JASSS, 2001-2012," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(4), pages 1-16.
  • Handle: RePEc:jas:jasssj:2015-7-5
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    1. Mark Buchanan, 2009. "Economics: Meltdown modelling," Nature, Nature, vol. 460(7256), pages 680-682, August.
    2. Nigel Gilbert & Pietro Terna, 2000. "How to build and use agent-based models in social science," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 1(1), pages 57-72, March.
    3. J. Doyne Farmer & Duncan Foley, 2009. "The economy needs agent-based modelling," Nature, Nature, vol. 460(7256), pages 685-686, August.
    4. Jean-Philippe Bouchaud, 2008. "Economics needs a scientific revolution," Nature, Nature, vol. 455(7217), pages 1181-1181, October.
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    3. Lee, Ju-Sung & Filatova, Tatiana & Ligmann-Zielinska, Arika & Hassani-Mahmooei, Behrooz & Stonedahl, Forrest & Lorscheid, Iris & Voinov, Alexey & Polhill, J. Gareth & Sun, Zhanli & Parker, Dawn C., 2015. "The complexities of agent-based modeling output analysis," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 18(4).

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