A Classification of Paradigmatic Models for Agent-Based Social Simulation

  • Maria Bruno Marietto
  • Nuno David
  • Jaime Simão Sichman
  • Helder Coelho
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

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References

  1. 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. 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. 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. 4.
    Conte, R., Sichman, J.S.: DEPNET: How to benefit from social dependence. Journal of Mathematical Sociology 20, 161–177 (1995)CrossRefGoogle Scholar
  5. 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. 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)CrossRefGoogle Scholar
  7. 7.
    CORMAS. Common-Pool Resources and Multi-Agent System (2003), http://cormas.cirad.fr
  8. 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)CrossRefGoogle Scholar
  9. 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. 10.
    Drogoul, A., Fukuda, T., Tambe, M. (eds.): CRW 1998. LNCS, vol. 1456. Springer, Heidelberg (1998)Google Scholar
  11. 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)CrossRefGoogle Scholar
  12. 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)CrossRefGoogle Scholar
  13. 13.
    Epstein, J.M., Axtell, R.: Growing Artificial Societies: Social Science from the Bottom Up. MIT Press, Cambridge (1996)Google Scholar
  14. 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. 15.
    Fowler Jr., F.J.: Survey Research Methods. Sage, Thousand Oaks (1984)Google Scholar
  16. 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. 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. 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)CrossRefGoogle Scholar
  19. 19.
    Lovelock, J.: A Numerical Model for Biodiversity. Phil. Trans. R. Soc. Lond. 338, 365–373 (1992)CrossRefGoogle Scholar
  20. 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. 21.
    RoboCup: Robocup Oficial Site (2003), http://www.robocup.org
  22. 22.
    Schelling, T.S.: Dynamic Models of Segregation. Journal of Mathematical Sociology 1(2), 143–186 (1971)CrossRefGoogle Scholar
  23. 23.
    Sichman, J.: Du Raisonnement Social Chez les Agents. PhD Thesis, Polytechnique LAFORIA, Grenoble, France (1995) (in French)Google Scholar
  24. 24.
    SimCog. Simulation of Cognitive Agents (2003), http://www.lti.pcs.usp.br/SimCog
  25. 25.
    SOAR. The SOAR Home Page (2003), http://ai.eecs.umich.edu/soar/
  26. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Maria Bruno Marietto
    • 1
  • Nuno David
    • 1
    • 2
  • Jaime Simão Sichman
    • 1
  • Helder Coelho
    • 3
  1. 1.Intelligent Techniques LaboratoryUniversity of São PauloBrazil
  2. 2.Department of Information Science and TechnologyISCTE/DCTILisbonPortugal
  3. 3.Department of InformaticsUniversity of LisbonPortugal

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