Abstract
The Iterated Prisoner's Dilemma game has been used extensively in the study of the evolution of cooperative behaviours in social and biological systems. There have been a lot of experimental studies on evolving strategies for 2-player Iterated Prisoner's Dilemma games (2IPD). However, there are many real world problems, especially many social and economic ones, which cannot be modelled by the 2IPD. The n-player Iterated Prisoner's Dilemma (NIPD) is a more realistic and general game which can model those problems. This paper presents two sets of experiments on evolving strategies for the NIPD. The first set of experiments examine the impact of the number of players in the NIPD on the evolution of cooperation in the group. Our experiments show that cooperation is less likely to emerge in a large group than in a small group. The second set of experiments study the generalisation ability of evolved strategies from the point of view of machine learning. Our experiments reveal the effect of changing the evolutionary environment of evolution on the generalisation ability of evolved strategies.
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References
A. M. Colman, Game Theory and Experimental Games, Pergamon Press, Oxford, England, 1982.
A. Rapoport, Optimal policies for the prisoner's dilemma, Technical Report 50, The Psychometric Lab., Univ. of North Carolina, Chapel Hill, NC, USA, July 1966.
G. Hardin, The tragedy of the commons, Science, 162:1243–1248, 1968.
J. H. Davis, P. R. Laughlin, and S. S. Komorita, The social psychology of small groups, Annual Review of Psychology, 27:501–542, 1976.
N. S. Glance and B. A. Huberman, The outbreak of cooperation, Journal of Mathematical Sociology, 17(4):281–302, 1993.
N. S. Glance and B. A. Huberman, The dynamics of social dilemmas, Scientific American, pages 58–63, March 1994.
R. Axelrod, The evolution of strategies in the iterated prisoner's dilemma, In L. Davis, editor, Genetic Algorithms and Simulated Annealing, chapter 3, pages 32–41. Morgan Kaufmann, San Mateo, CA, 1987.
D. M. Chess, Simulating the evolution of behaviors: the iterated prisoners' dilemma problem, Complex Systems, 2:663–670, 1988.
K. Lindgren, Evolutionary phenomena in simple dynamics, In C. G. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen, editors, Artificial Life II: SFI Studies in the Sciences of Complexity, Vol. X, pages 295–312, Reading, MA, 1991. Addison-Wesley.
D. B. Fogel, The evolution of intelligent decision making in gaming, Cybernetics and Systems: An International Journal, 22:223–236, 1991.
D. B. Fogel, Evolving behaviors in the iterated prisoner's dilemma, Evolutionary Computation, 1(1):77–97, 1993.
P. J. Darwen and X. Yao, On evolving robust strategies for iterated prisoner's dilemma, In X. Yao, editor, Proc. of the AI'93 Workshop on Evolutionary Computation, pages 49–63, Canberra, Australia, November 1993. University College, UNSW, Australian Defence Force Academy.
W. Daniel Hillis, Co-evolving parasites improve simulated evolution as an optimization procedure, In Santa Fe Institute Studies in the Sciences of Complexity, Volume 10, pages 313–323. Addison-Wesley, 1991.
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Yao, X., Darwen, P.J. (1995). An experimental study of N-Person Iterated Prisoner's Dilemma games. In: Yao, X. (eds) Progress in Evolutionary Computation. EvoWorkshops EvoWorkshops 1993 1994. Lecture Notes in Computer Science, vol 956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60154-6_50
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DOI: https://doi.org/10.1007/3-540-60154-6_50
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