A Classification of Paradigmatic Models for Agent-Based Social Simulation | SpringerLink
Skip to main content

A Classification of Paradigmatic Models for Agent-Based Social Simulation

  • Conference paper
Multi-Agent-Based Simulation III (MABS 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2927))

Abstract

Given the strong interdisciplinary character of Agent-Based Social Simulation (ABSS), and the difficulties related to ambiguous terminological and methodological assumptions, there is an increasing need to make more explicit the modelling paradigm underlying each research paper or project. In this paper we propose a classification of paradigmatic models in ABSS, which characterise different ontological assumptions and pragmatic criteria with respect to their targets. The classification is composed by different classes of models at different levels of abstraction, in a layered architecture that enables switching among levels. Each class is based on different kinds of assumptions, which possibly call for different logics of scientific research. The present proposal is interesting, since the taxonomy was well validated with researchers in the field. It is a good analytical tool to characterise or compare models according to various criteria, such as methodological, philosophical, or simply pragmatic and usability criteria.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Caldas, J.C., Coelho, H.: The Origin of Institutions: Socio-Economic Processes, Choice, Norms and Conventions. Journal of Artificial Societies and Social Simulation (JASSS) 2(2) (1999), http://www.soc.surrey.ac.uk/JASSS/2/2/1.html

  2. Castelfranchi, C., Miceli, M., Cesta, A.: Dependence Relations among Autonomous Agents. In: Castelfranchi, C., Werner, E. (eds.) MAAMAW 1992. LNCS, vol. 830, pp. 215–227. Springer, Heidelberg (1994)

    Google Scholar 

  3. Castelfranchi, C., Conte, R., Paolucci, M.: Normative Reputation and the Cost of Compliance. Journal of Artificial Societies and Social Simulation 1(3) (1998), http://www.soc.survey.ac.uk/JASSS/1/3/3.html

  4. Conte, R., Sichman, J.S.: DEPNET: How to benefit from social dependence. Journal of Mathematical Sociology 20, 161–177 (1995)

    Article  Google Scholar 

  5. Conte, R., Dignum, F.: From Social Monitoring to Normative Influence. Journal of Artificial Societies and Social Simulation 4(2) (2001), http://www.soc.survey.ac.uk/JASSS/4/2/7.html

  6. Conte, R., Edmonds, B., Moss, S., Sawyer, K.: Sociology and Social Theory in Agent Based Social Simulation: A Symposium. Computational and Mathematical Organization Theory 7(3), 183–205 (2001)

    Article  Google Scholar 

  7. CORMAS. Common-Pool Resources and Multi-Agent System (2003), http://cormas.cirad.fr

  8. Davidsson, P., Boman, M.: Saving Energy and Providing Value Added Services in Intelligent Building: A MAS Approach. In: Mobile Agents and Applications, pp. 166–177. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  9. Dean, J.S., Gumerman, G.J., Epstein, J.M., Axtell, R., Swedland, A.C., Parker, M.T., McCarpoel, S.: Undertanding Anasaki Culture Change Through Agent-Based Modeling. In: Modeling Small Scale Societies, Oxford University Press, New York (1999)

    Google Scholar 

  10. Drogoul, A., Fukuda, T., Tambe, M. (eds.): CRW 1998. LNCS, vol. 1456. Springer, Heidelberg (1998)

    Google Scholar 

  11. Edmonds, B.: The Use of Models – Making MABS more Informative. In: Moss, S., Davidsson, P. (eds.) MABS 2000. LNCS (LNAI), vol. 1979, pp. 15–31. Springer, Heidelberg (1979)

    Chapter  Google Scholar 

  12. El hadouaj, S., Drogoul, A., Espié, S.: How to Combine Reactivity and Anticipation: The Case of Conflicts Resolution in a Simulated Road Traffic. In: Moss, S., Davidsson, P. (eds.) MABS 2000. LNCS (LNAI), vol. 1979, pp. 82–96. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  13. Epstein, J.M., Axtell, R.: Growing Artificial Societies: Social Science from the Bottom Up. MIT Press, Cambridge (1996)

    Google Scholar 

  14. Fischwick, P.A.: A Taxonomy for Simulation Modeling Based on Programming Language Principles. IIE Transactions on IE Research 30, 811–820 (1995)

    Google Scholar 

  15. Fowler Jr., F.J.: Survey Research Methods. Sage, Thousand Oaks (1984)

    Google Scholar 

  16. Gross, D., Strand, R.: Can Agent-Based Models Assist Decisions on Large-Scale Practical Problems? A Philosophical Analysis Complexity 5(6), 26–33 (2000)

    Google Scholar 

  17. Hales, D.: An Open Mind is not an Empty Mind: Experiments in the Meta-Noosphere. Journal of Artificial Societies and Social Simulation 1(4) (1998), http://www.soc.surrey.ac.uk/JASSS/1/4/2.html

  18. Hemelrijk, C.K.: Sexual Attraction and Inter-Sexual Dominance Among Virtual Agents. In: Moss, S., Davidsson, P. (eds.) MABS 2000. LNCS (LNAI), vol. 1979, pp. 167–180. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  19. Lovelock, J.: A Numerical Model for Biodiversity. Phil. Trans. R. Soc. Lond. 338, 365–373 (1992)

    Article  Google Scholar 

  20. Muller, H.J., Malsch, T., Schulz-Schaeffer, I.: SOCIONICS: Introduction and Potential. JASSS 1(3) (1998), http://www.soc.survey.ac.uk/JASSS/1/3/5.html

  21. RoboCup: Robocup Oficial Site (2003), http://www.robocup.org

  22. Schelling, T.S.: Dynamic Models of Segregation. Journal of Mathematical Sociology 1(2), 143–186 (1971)

    Article  Google Scholar 

  23. Sichman, J.: Du Raisonnement Social Chez les Agents. PhD Thesis, Polytechnique LAFORIA, Grenoble, France (1995) (in French)

    Google Scholar 

  24. SimCog. Simulation of Cognitive Agents (2003), http://www.lti.pcs.usp.br/SimCog

  25. SOAR. The SOAR Home Page (2003), http://ai.eecs.umich.edu/soar/

  26. Stalles, A., Petta, P.: Introducing Emotions into the Computational Study of Social Norms: A First Evaluation. Journal of Artificial Societies and Social Simulation 4(1) (2001), http://www.soc.surrey.ac.uk/JASSS/4/1/2.html

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Marietto, M.B., David, N., Sichman, J.S., Coelho, H. (2003). A Classification of Paradigmatic Models for Agent-Based Social Simulation. In: Hales, D., Edmonds, B., Norling, E., Rouchier, J. (eds) Multi-Agent-Based Simulation III. MABS 2003. Lecture Notes in Computer Science(), vol 2927. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24613-8_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-24613-8_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20736-8

  • Online ISBN: 978-3-540-24613-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics