IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this article or follow this journal

A Common Protocol for Agent-Based Social Simulation

Traditional (i.e. analytical) modelling practices in the social sciences rely on a very well established, although implicit, methodological protocol, both with respect to the way models are presented and to the kinds of analysis that are performed. Unfortunately, computer-simulated models often lack such a reference to an accepted methodological standard. This is one of the main reasons for the scepticism among mainstream social scientists that results in low acceptance of papers with agent-based methodology in the top journals. We identify some methodological pitfalls that, according to us, are common in papers employing agent-based simulations, and propose appropriate solutions. We discuss each issue with reference to a general characterization of dynamic micro models, which encompasses both analytical and simulation models. In the way, we also clarify some confusing terminology. We then propose a three-stage process that could lead to the establishment of methodological standards in social and economic simulations.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://jasss.soc.surrey.ac.uk/9/1/15/15.pdf
Download Restriction: no

Article provided by Journal of Artificial Societies and Social Simulation in its journal Journal of Artificial Societies and Social Simulation.

Volume (Year): 9 (2006)
Issue (Month): 1 ()
Pages: 15

as
in new window

Handle: RePEc:jas:jasssj:2005-86-1
Contact details of provider:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. Nelson Minar & Rogert Burkhart & Chris Langton & Manor Askenazi, 1996. "The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations," Working Papers 96-06-042, Santa Fe Institute.
  2. Jorgen W. Weibull, 1997. "Evolutionary Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262731215, June.
  3. Weisbuch, G. & Kirman, A.P. & Herreiner, D., 1996. "Market Organisation," G.R.E.Q.A.M. 96a20, Universite Aix-Marseille III.
  4. Schelling, Thomas C, 1969. "Models of Segregation," American Economic Review, American Economic Association, vol. 59(2), pages 488-93, May.
  5. Kleijnen, J.P.C., 1995. "Sensitivity analysis and optimization of system dynamics models : Regression analysis and statistical design of experiments," Discussion Paper 1995-4, Tilburg University, Center for Economic Research.
  6. Joachim Merz, 1994. "Microsimulation - A Survey of Methods and Applications for Analyzing Economic and Social Policy," FFB-Discussionpaper 09, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)), LEUPHANA University L√ľneburg.
  7. Thomas Brenner & Claudia Werker, 2004. "Empirical Calibration of Simulation Models," Computing in Economics and Finance 2004 89, Society for Computational Economics.
  8. Attanasio, Orazio P & Weber, Guglielmo, 1993. "Consumption Growth, the Interest Rate and Aggregation," Review of Economic Studies, Wiley Blackwell, vol. 60(3), pages 631-49, July.
  9. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, EconWPA, revised 15 Aug 2002.
  10. Arifovic, Jasmina, 1995. "Genetic algorithms and inflationary economies," Journal of Monetary Economics, Elsevier, vol. 36(1), pages 219-243, August.
  11. Arifovic, Jasmina, 1996. "The Behavior of the Exchange Rate in the Genetic Algorithm and Experimental Economies," Journal of Political Economy, University of Chicago Press, vol. 104(3), pages 510-41, June.
  12. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-37, February.
  13. Leigh Tesfatsion, 2000. "Agent-Based Computational Economics: A Brief Guide to the Literature," Computational Economics 0004001, EconWPA.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:jas:jasssj:2005-86-1. 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)

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 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.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.