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Reinforcement Learning Dynamics in Social Dilemmas

In this paper we replicate and advance Macy and Flache's (2002; Proc. Natl. Acad. Sci. USA, 99, 7229–7236) work on the dynamics of reinforcement learning in 2×2 (2-player 2-strategy) social dilemmas. In particular, we provide further insight into the solution concepts that they describe, illustrate some recent analytical results on the dynamics of their model, and discuss the robustness of such results to occasional mistakes made by players in choosing their actions (i.e. trembling hands). It is shown here that the dynamics of their model are strongly dependent on the speed at which players learn. With high learning rates the system quickly reaches its asymptotic behaviour; on the other hand, when learning rates are low, two distinctively different transient regimes can be clearly observed. It is shown that the inclusion of small quantities of randomness in players' decisions can change the dynamics of the model dramatically.

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File URL: http://jasss.soc.surrey.ac.uk/11/2/1/1.pdf
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Article provided by Journal of Artificial Societies and Social Simulation in its journal Journal of Artificial Societies and Social Simulation.

Volume (Year): 11 (2008)
Issue (Month): 2 ()
Pages: 1

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Handle: RePEc:jas:jasssj:2007-11-2
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  1. Binmore, K. & Samuelson, L., 1993. "An Economist's Perspective on the Evolution of Norms," Working papers 9323, Wisconsin Madison - Social Systems.
  2. Margaret Edwards & Sylvie Huet & François Goreaud & Guillaume Deffuant, 2003. "Comparing an Individual-Based Model of Behaviour Diffusion with Its Mean Field Aggregate Approximation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 6(4), pages 9.
  3. Glenn Ellison, 2000. "Basins of Attraction, Long-Run Stochastic Stability, and the Speed of Step-by-Step Evolution," Review of Economic Studies, Oxford University Press, vol. 67(1), pages 17-45.
  4. Izquierdo, Luis R. & Izquierdo, Segismundo S. & Gotts, Nicholas M. & Polhill, J. Gary, 2007. "Transient and asymptotic dynamics of reinforcement learning in games," Games and Economic Behavior, Elsevier, vol. 61(2), pages 259-276, November.
  5. Andreas Flache & Michael W. Macy, 2002. "Stochastic Collusion and the Power Law of Learning," Journal of Conflict Resolution, Peace Science Society (International), vol. 46(5), pages 629-653, October.
  6. John G. Cross, 1973. "A Stochastic Learning Model of Economic Behavior," The Quarterly Journal of Economics, Oxford University Press, vol. 87(2), pages 239-266.
  7. Barry Sopher & Dilip Mookherjee, 1997. "Learning and Decision Costs in Experimental Constant Sum Games," Departmental Working Papers 199527, Rutgers University, Department of Economics.
  8. José Manuel Galán & Luis R. Izquierdo, 2005. "Appearances Can Be Deceiving: Lessons Learned Re-Implementing Axelrod's 'Evolutionary Approach to Norms'," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(3), pages 2.
  9. Yan Chen & Fang-Fang Tang, 1998. "Learning and Incentive-Compatible Mechanisms for Public Goods Provision: An Experimental Study," Journal of Political Economy, University of Chicago Press, vol. 106(3), pages 633-662, June.
  10. Sylvie Huet & Margaret Edwards & Guillaume Deffuant, 2007. "Taking into Account the Variations of Neighbourhood Sizes in the Mean-Field Approximation of the Threshold Model on a Random Network," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(1), pages 10.
  11. T. Borgers & R. Sarin, 2010. "Learning Through Reinforcement and Replicator Dynamics," Levine's Working Paper Archive 380, David K. Levine.
  12. Debraj Ray & Dilip Mookherjee & Fernando Vega Redondo & Rajeeva L. Karandikar, 1996. "Evolving aspirations and cooperation," Working Papers. Serie AD 1996-06, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  13. Gary J. Polhill & Luis R. Izquierdo, 2005. "Lessons Learned from Converting the Artificial Stock Market to Interval Arithmetic," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(2), pages 2.
  14. Youngse Kim, 1999. "Satisficing and optimality in 2þ2 common interest games," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 13(2), pages 365-375.
  15. Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
  16. Fernando Vega-Redondo & Frédéric Palomino, 1999. "Convergence of aspirations and (partial) cooperation in the prisoner's dilemma," International Journal of Game Theory, Springer;Game Theory Society, vol. 28(4), pages 465-488.
  17. Erev, Ido & Bereby-Meyer, Yoella & Roth, Alvin E., 1999. "The effect of adding a constant to all payoffs: experimental investigation, and implications for reinforcement learning models," Journal of Economic Behavior & Organization, Elsevier, vol. 39(1), pages 111-128, May.
  18. Mookherjee Dilip & Sopher Barry, 1994. "Learning Behavior in an Experimental Matching Pennies Game," Games and Economic Behavior, Elsevier, vol. 7(1), pages 62-91, July.
  19. Luis R. Izquierdo & Gary J. Polhill, 2006. "Is Your Model Susceptible to Floating-Point Errors?," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(4), pages 4.
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