The Emergence of Reactive Strategies in Simulated Heterogeneous Populations
- Ilan Fischer
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The computer simulation study explores the impact of the duration of social impact on the generation and stabilization of cooperative strategies. Rather than seeding the simulations with a finite set of strategies, a continuous distribution of strategies is being defined. Members of heterogeneous populations were characterized by a pair of probabilistic reactive strategies: the probability to respond to cooperation by cooperation and the probability to respond to defection by cooperation. This generalized reactive strategy yields the standard TFT mechanism, the All-Cooperate, All-Defect and Bully strategies as special cases. Pairs of strategies interacted through a Prisoner's Dilemma game and exerted social influence on all other members. Manipulating: (i) the initial distribution of populations' strategies, and (ii) the duration of social influence, we monitored the conditions leading to the emergence and stabilization of cooperative strategies. Results show that: (1) The duration of interactions between pairs of strategies constitutes a crucial factor for the emergence and stabilization of cooperative strategies, (2) Unless sufficient learning intervals are provided, initializing the simulations with cooperative populations does not guarantee that cooperation will sustain.
- Auman, R.J. and Maschler, M.B. (1995), Repeated Games with Incomplete Information, The MIT Press, Cambridge, Massachusetts, p.236.
- Axelrod, R. (1980a), Effective choice in the Prisoner's Dilemma, Journal of Conflict Resolution 24, 3–25.
- Axelrod, R. (1980b), More effective choice in the Prisoner's Dilemma, Journal of Conflict Resolution, 24, 379–403.
- Axelrod, R. (1981), The emergence of cooperation among egoists, The American Political Science Review, 75, 306–318.
- Axelrod, R. (1984), The Evolution of Cooperation, Basic Books, New York.
- Axelrod, R. (1997), Evolving new strategies: The evolution of strategies in the iterated Prisoner's Dilemma, in R. Axelrod (ed.), The Complexity of Cooperation Princeton University Press: New Jersey, pp. 41–29.
- Bendor, J. (1987), In good times and bad: Reciprocity in an uncertain world, American Journal of Political Science 31, 531–538.
- Bendor, J., Kramer, R.M., and Stout, S. (1991), When in doubt: Cooperation in a noisy Prisoner's Dilemma, Journal of Conflict Resolution 35, 691–719.
- Chattoe, E. (1998), Just How (Un)realistic are Evolutionary Algorithms as Representations of Social Process? Journal of Artificial Societies and Social 1, 3, Simulation, available online http://www.soc.surrey.ac.uk/JASSS/1/3/2.html>.
- Derman, C., Gleser, L.J. and Olkin, I. (1973), A Guide to Probability Theory and Application, Holt, Rinehart, and Winston: New York.
- Dugatkin, L.A. and Alfieri, M. (1991), Tit-for-tat in guppies (Poecilla reticulata): the relative nature of cooperation and defection during predator inspection, Evolutionary Ecology 5, 300–309.
- Fischer, I. (2003), Evolutionary development and learning: two facets of strategy generation, Journal of Artificial Societies and Social Simulations 6(1), available online http://jasss.soc.surrey.ac.uk/6/1/7.html.
- Fischer, I. and Suleiman, R., (1997), Mutual cooperation in a simulated intergroup conflict, Journal of Conflict Resolution, 41(4), 483–508.
- Fogel, D.B. (1993), Evolutionary Computation: Towards a New Philosophy of Machine Intelligence, IEEE Press, New York.
- Gorrini, V. and Dorigo, M. (1996), An application of evolutionary algorithms to the scheduling of robotic operations, in Alliot, J.M., Lutton, E., Ronald, E., and Schoenauer, M. (eds.), Artificial Evolution. AnchorBooks, New York.
- Heinsohn, R. and Packer, C. (1995), Complex cooperative strategies in groupterritorial african lions, Science 269(1), 1260–1262.
- Hirshleifer, J. and Martinez Coll, J.C. (1988), What strategies can support the evolutionary emergence of cooperation, Journal of Conflict Resolution, 32(2), 367–398.
- Hoffman, R. (2000), Twenty years on: The evolution of cooperation revisited, Journal of Artificial Societies and Social Simulation, (On-Line), 3(2). Available: http://www.soc.surrey.ac.uk/JASSS/3/2/forum/1.html.
- Holland, J.H. (1975), Adaption in Natural and Artificial Systems, University of Michigan Press: Ann Arbor, MI.
- Johnson, N.L. and Kotz, S. (1969), Distributions in Statistics, Houghton Mifflin: Boston, MA.
- Latane, B. (1981), The psychology of social impact, American Psychologist, 36, 343–365.
- Lomborg, B. (1992), Game theory vs. multiple agents: the iterated prisoner's dilemma, In Castelfranchi, C., and Werner, E. (eds.), Artificial Social Systems, Springer-Verlag, pp.69–93.
- Macy, M.W. and Flache, A. (2002), Learning dynamics in social dilemmas. Proceedings of the National Academy of Sciences U.S.A. May 14; 99(10): 7229–36.
- Marinoff, L. (1992), Maximizing expected utilities in Prisoner's dilemma, Journal of Conflict Resolution 36, 183–216.
- Marshall, G.C. (1945), Biennial report of the chief of staff of the US army, The United States News, October 10, 1945.
- Milinski, M., (1987), Tit fortat in sticklebacks and the evolution of cooperation, Nature 325(29) 433–435.
- Nowak, M. and Sigmund, K. (1993), A strategy of win-stay, lose-shift that outperforms tit-for-tat in the prisoner's dilemma game, Nature 364, 56–58. CrossRef
- Roberts, G. and Sherratt, T.N. (1998), Development of cooperative relationships through increasing investment, Nature 394, 175–179.
- Sigmund, K. (1993), Games of Life, Oxford University Press, Oxford.
- Shubik, M. (1970), Game theory, behavior, and the paradox of the prisoner's dilemma: three solutions, Journal of Conflict Resolution, 14, 181–193.
- Suleiman, R. and Fischer, I. (1996), The Evolution of cooperation in a simulated intergroup conflict, in W.B.G. Liebrand, and D.M. Messick (eds.), Frontiers in Social Dilemma Research, Springer, Berlin, pp. 419–438.
- Suleiman, R. and Fischer, I. (2000), When one decides for many: The effect of delegation methods on cooperation in simulated inter-group conflicts, Journal of Artificial Societies and Social Simulation, (On-Line), 3 (4). Available: http://www.soc.surrey.ac.uk/JASSS/3/4/1.html.
- Wilson, D.S. and Dugatkin, L.A. (1997), Group selection and assorative interactions, The American Naturalist, 149(2), 336–351.
- The Emergence of Reactive Strategies in Simulated Heterogeneous Populations
Theory and Decision
Volume 55, Issue 4 , pp 289-314
- Cover Date
- Print ISSN
- Online ISSN
- Kluwer Academic Publishers
- Additional Links
- computer simulation
- duration of interaction
- heterogeneous populations
- Prisoner's Dilemma
- probabilistic Tit For Tat
- reactive strategies
- social impact
- Industry Sectors
- Ilan Fischer (1)
- Author Affiliations
- 1. Department of Behavioral Sciences, Ben-Gurion University of the Negev, P.O. Box 65, Beer Sheva, 84105, Israel. E-mail