Dynamics of Cooperation in Spatial Prisoner’s Dilemma of Memory-Based Players

  • Chenna Reddy CotlaEmail author
Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 652)


In a population of extremely primitive players with no memory, interaction with local neighbors in a spatial array can promote the coexistence of cooperators and defectors, which is not possible in the well mixed case (Nowak, Bonhoeffer, and May, 1994). However, the applicability of this insight is unclear in the context of a social system where memory plays a significant role in the conscious decisionmaking of the members. In this paper, the problem of cooperation is analyzed in a population of players with the memory model embodied in the ACT-R cognitive architecture (Anderson and Lebiere, 1998). Using agent-based simulations, it is shown that in a population of memory-based agents, spatial structure supports higher levels of cooperation in comparison to the well mixed paradigm.


Production Rule Neighborhood Size Evolutionary Game Complex Adaptive System Procedural Memory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anderson, J. and Lebiere, C. (1998). The atomic components of thought. Lawrence Erlbaum Associates.Google Scholar
  2. 2.
    Anderson, J. and Schooler, L. (1991). Reflections of the environment in memory. Psychological Science, 2(6):396.CrossRefGoogle Scholar
  3. 3.
    Aunger, R. (2001). Darwinizing culture: The status of memetics as a science. Oxford University Press, USA.Google Scholar
  4. 4.
    Axelrod, R. (1984). The evolution of cooperation. Basic Brook, New York.Google Scholar
  5. 5.
    Axtell, R. (2000). Why agents? on the varied motivation for agent computing in the social sciences. Brookings Institution CSED Technical Report.Google Scholar
  6. 6.
    Axtell, R. (2001). Effects of interaction topology and activation regime in several multi-agent systems. Multi-Agent-Based Simulation, pages 33–48.Google Scholar
  7. 7.
    Gómez-Gardenes, J., Campillo, M., Floría, L., and Moreno, Y. (2007). Dynamical organization of cooperation in complex topologies. Physical Review Letters, 98(10):1–4.CrossRefGoogle Scholar
  8. 8.
    Gonzalez, C. and Lebiere, C. (2005). Instance-based cognitive models of decision making. In D., Z. and Courakis, A., editors, Transfer of Knowledge in Economic Decision Making. New York: Palgrave McMillan.Google Scholar
  9. 9.
    Hauert, C. (2002). Effects of space in 2× 2 games. Int. J. Bifurcat. Chaos, 12:1531–1548.CrossRefGoogle Scholar
  10. 10.
    Helbing, D. and Yu, W. (2010). The future of social experimenting. Proceedings of the National Academy of Sciences, 107(12):5265–5266.CrossRefGoogle Scholar
  11. 11.
    Huberman, B. and Glance, N. (1993). Evolutionary games and computer simulations. Proceedings of the national academy of sciences of the United States of America, 90(16):7716.CrossRefGoogle Scholar
  12. 12.
    Ishida, Y. and Mori, T. (2005). Spatial strategies in a generalized spatial prisoner?s dilemma. Artificial Life and Robotics, 9(3):139–143.CrossRefGoogle Scholar
  13. 13.
    Lebiere, C., Wallach, D., and West, R. L. (2000). A memory-based account of the prisoner’s dilemma and other 2× 2 games. In Proceedings of the 3rd International Conference on Cognitive Modeling, Groningen, Netherlands, pages 185–193.Google Scholar
  14. 14.
    Miller, J. and Page, S. (2007). Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Princeton University Press.Google Scholar
  15. 15.
    Nowak, M., Bonhoeffer, S., and May, R. (1994). Spatial games and the maintenance of cooperation. Proceedings of the National Academy of Sciences of the United States of America, 91(11):4877.Google Scholar
  16. 16.
    Nowak, M. and May, R. (1992). Evolutionary games and spatial chaos. Nature, 359(6398):826–829.CrossRefGoogle Scholar
  17. 17.
    Petrov, A. (2006). Computationally efficient approximation of the base-level learning equation in act-r. In Proceedings of the Seventh International Conference on Cognitive Modeling, pages 391–392.Google Scholar
  18. 18.
    Szilagyi, M. (2003). An investigation of n-person prisoners’ dilemmas. Complex Systems, 14(2):155–174.Google Scholar
  19. 19.
    Taatgen, N., Lebiere, C., and Anderson, J. (2006). Modeling paradigms in act-r. In Sun, R., editor, Cognition and multi-agent interaction: From cognitive modeling to social simulation, pages 29–52. Cambridge University Press.Google Scholar
  20. 20.
    Traulsen, A., Semmann, D., Sommerfeld, R., Krambeck, H., and Milinski, M. (2010). Human strategy updating in evolutionary games. Proceedings of the National Academy of Sciences, 107(7):2962.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Department of Computational Social ScienceGeorge Mason UniversitySterlingUSA

Personalised recommendations