Agent-Based Computational Modeling of Emergent Collective Intelligence

  • Vivek Kumar Singh
  • Divya Gautam
  • Rishi Raj Singh
  • Ashok K. Gupta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5796)


Collective Intelligence is a form of intelligence which emerges out of collaboration and coordination of many individual agents. A group of actors performing simple behaviours and interacting with fellow group members & the environment often produce global behaviours which seems intelligent. Understanding the emergence of intelligent collective behaviours in social systems, such as norms & conventions, higher level organizations, collective wisdom and evolution of culture from simple and predictable local interactions; has been an important research question since decades. Agent-based modeling of complex social behaviours by simulating social units as agents and modeling their interactions; provides a new generative approach to understanding the dynamics of emergence of collective intelligence behaviours. In this paper, we have presented an analytical account of nature, form and dynamics of collective intelligence, followed by some of our experimental work on evolution of collective intelligence. The paper concludes with a short discussion of the results and relevant implications for designing systems for achieving desired collective intelligence.


Collective Intelligence Agent Based Modeling Multi-agent Systems Emergent Phenomenon Socionics 


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  1. 1.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)zbMATHGoogle Scholar
  2. 2.
    Reynolds, C.W.: Flocks, Herds and Schools: A Distributed Behavioural Model. Computer Graphics 21, 25–34 (1987)CrossRefGoogle Scholar
  3. 3.
    Schelling, T.C.: Dynamic Models of Segregation. Journal of Mathematical Sociology 1, 143–186 (1971)CrossRefGoogle Scholar
  4. 4.
    Tesfatison, L.: Introduction to the special issue on Agent-based Computational Economics. Journal of Economic Dynamics and Control (2001)Google Scholar
  5. 5.
    Macal, C.M., North, M.J.: Tutorial on Agent based Modeling and Simulation. In: Kuhl, M.E., Steiger, N.M., Armstrong, F.B., Joines, J.A. (eds.) Proceedings of the 2005 Winter Simulation Conference (2005)Google Scholar
  6. 6.
    Zhengping, L., Cheng, H.S.: A Survey of Emergent Behaviour and its Impacts in Agent-based Systems. In: Proceedings of IEEE International Conference on Industrial Informatics (August 2006)Google Scholar
  7. 7.
    Namatame, A.: Adaptation and Evolution in Complex Systems. World Scientific (2006)Google Scholar
  8. 8.
    Surowiecki, J.: The Wisdom of Crowds. Anchor, New York (2005)Google Scholar
  9. 9.
    Wikipedia, Collective Intelligence (2009),
  10. 10.
    Epstein, J.M.: Generative Social Sciences: Studies in Agent-based Computational Modeling. Princeton University Press, Princeton (2007)Google Scholar
  11. 11.
    Axelrod, R.: The Complexity of Cooperation. Princeton University Press, Princeton (1997)zbMATHGoogle Scholar
  12. 12.
    Epstein, J.M., Axtell, R.: Growing Artificial Societies: Social science from bottom-up. MIT Press, Cambridge (1996)Google Scholar
  13. 13.
    Macy, M.W., Willer, R.: From factors to actors: computational sociology and agent based modeling. Annual Review of Sociology 28, 143–166 (2002)CrossRefGoogle Scholar
  14. 14.
    Goldstone, R.L., Janssen, M.A.: Computational models of collective behaviour. Trends in Cognitive Sciences 9(9) (2005)Google Scholar
  15. 15.
    Szuba, T.M.: Computational Collective Intelligence, 1st edn. Wiley-Interscience, Hoboken (2001)Google Scholar
  16. 16.
    Malone, T.W.: What is Collective Intelligence, Social Text (2005),
  17. 17.
    Banks, A., Vincent, J., Anyakoha, C.: A Review of Particle Swarm Optimization, Part-I: Background & Development. Natural Computing 6, 467–484 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Dorigo, M., Birattari, M., Stutzle, T.: Ant Colony Optimization: Artificial Ants as Computational Intelligence Technique. IEEE Computational Intelligence (November 2006)Google Scholar
  19. 19.
    Karaboga, D., Basturk, B.: On the Performance of Artificial Bee Colony Algorithm. Applied Soft Computing 8, 687–697 (2008)CrossRefGoogle Scholar
  20. 20.
    Greif, I. (ed.): Computer Supported Cooperative Work: A Book of Readings. Morgan Kaufmann, San Francisco (1988)Google Scholar
  21. 21.
    Singh, V.K., Gupta, A.K.: Collective Intelligence Based Approaches to Search and Optimization Problems. In: Proceedings of UGC Sponsored National Seminar on Soft Computing Techniques and their Applications at Howrah, India (March 2009)Google Scholar
  22. 22.
    Alag, S.: Collective Intelligence in Action. Manning, New York (2009)Google Scholar
  23. 23.
    Malone, T.W.: What is Collective Intelligence and what we will do about it? (2005), Edited transcript available at MIT Centre for Collective Intelligence,
  24. 24.
    Fehr, E., Fischbacher, U.: Social Norms and Human Cooperation. Trends in Cognitive Science 8(4), 185–190 (2004)CrossRefGoogle Scholar
  25. 25.
    Axelrod, R.: An Evolutionary Approach to Norms. The American Political Science Review 80(4), 1095–1111 (1986)CrossRefGoogle Scholar
  26. 26.
    Wilensky, U.: NetLogo Traffic Grid Model. models/ TrafficGrid, Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (2003),
  27. 27.
    Axelrod, R.: The Evolution of Cooperation. Basic, New York (1984)Google Scholar
  28. 28.
    Conte, R., Castelfranchi: Understanding the Function of Norms in Social Groups through Simulation. In: Gilbert, N., Conte, R. (eds.) Artificial Societies: The Computer Simulation of Social Life. UCL Press, London (1995)Google Scholar
  29. 29.
    Epstein, J.: Learning to be thoughtless: Social Norms and Individual Computation. Centre on Social and Economic Dynamics Working Papers, No. 6 (2000)Google Scholar
  30. 30.
    Sen, S., Airiau, S.: Emergence of Norms through Social Learning. In: IJCAI-2007, pp. 1507–1512 (2007)Google Scholar
  31. 31.
    Gigliotta, O., Miglino, O., Parisi, D.: Groups of Agents with a Leader. Journal of Artificial Societies and Social Simulation 10(4) (2007)Google Scholar
  32. 32.
    Wilensky, U.: NetLogo Voting Model. Voting, Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1998),
  33. 33.
    Gautam, D., Singh, R.R., Singh, V.K.: Multi-Agent based Models of Social Contagion and Emergent Collective Behaviour. In: Proceedings of the IEEE International Conference on Intelligent Agents and Multi-agent Systems at Chennai, India (July 2009)Google Scholar
  34. 34.
    Wilensky, U.: NetLogo. Centre for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1999),
  35. 35.
    Levy, P.: Collective Intelligence: Mankind’s Emerging World in Cyberspace. Perseus Books, Cambridge (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Vivek Kumar Singh
    • 1
  • Divya Gautam
    • 1
  • Rishi Raj Singh
    • 1
  • Ashok K. Gupta
    • 2
  1. 1.Department of Computer ScienceBanaras Hindu UniversityVaranasiIndia
  2. 2.J. K. Institute of Applied Physics & TechnologyUniversity of AllahabadAllahabadIndia

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