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Reinforcement Learning in Repeated Interaction Games

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Author Info
Jonathan Bendor (Stanford University)
Dilip Mookherjee (Boston University)
Debraj Ray (New York University)

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Abstract

We study long run implications of reinforcement learning when two players repeatedly interact with one another over multiple rounds to play a finite action game. Within each round, the players play the game many successive times with a fixed set of aspirations used to evaluate payoff experiences as successes or failures. The probability weight on successful actions is increased, while failures result in players trying alternative actions in subsequent rounds. The learning rule is supplemented by small amounts of inertia and random perturbations to the states of players. Aspirations are adjusted across successive rounds on the basis of the discrepancy between the average payoff and aspirations in the most recently concluded round. We define and characterize pure steady states of this model, and establish convergence to these under appropriate conditions. Pure steady states are shown to be individually rational, and are either Pareto-efficient or a protected Nash equilibrium of the stage game. Conversely, any Pareto-efficient and strictly individually rational action pair, or any strict protected Nash equilibrium, constitutes a pure steady state, to which the process converges from non-negligible sets of initial aspirations. Applications to games of coordination, cooperation, oligopoly, and electoral competition are discussed.

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Publisher Info
Article provided by Berkeley Electronic Press in its journal Advances in Theoretical Economics.

Volume (Year): 1 (2001)
Issue (Month): advances/1/1 ()
Pages: 1008-1008
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Handle: RePEc:bep:theadv:v:1:y:2001:i:advances/1/1:p:1008-1008

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Related research
Keywords: reinforcement learning aspirations bounded rationality cooperation coordination

Find related papers by JEL classification:
C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory

References listed on IDEAS
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  1. 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. [Downloadable!] (restricted)
  2. Binmore, Ken & Samuelson, Larry, 1997. "Muddling Through: Noisy Equilibrium Selection," Journal of Economic Theory, Elsevier, vol. 74(2), pages 235-265, June. [Downloadable!] (restricted)
  3. Lipman, Barton L, 1991. "How to Decide How to Decide How to. . . : Modeling Limited Rationality," Econometrica, Econometric Society, vol. 59(4), pages 1105-25, July. [Downloadable!] (restricted)
  4. Cross, John G, 1973. "A Stochastic Learning Model of Economic Behavior," The Quarterly Journal of Economics, MIT Press, vol. 87(2), pages 239-66, May. [Downloadable!] (restricted)
  5. Karandikar, Rajeeva & Mookherjee, Dilip & Ray, Debraj & Vega-Redondo, Fernando, 1998. "Evolving Aspirations and Cooperation," Journal of Economic Theory, Elsevier, vol. 80(2), pages 292-331, June. [Downloadable!] (restricted)
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  6. Dixon, Huw David, 2000. "Keeping up with the Joneses: competition and the evolution of collusion," Journal of Economic Behavior & Organization, Elsevier, vol. 43(2), pages 223-238, October. [Downloadable!] (restricted)
  7. Borgers, Tilman & Sarin, Rajiv, 1997. "Learning Through Reinforcement and Replicator Dynamics," Journal of Economic Theory, Elsevier, vol. 77(1), pages 1-14, November. [Downloadable!] (restricted)
  8. Arthur, W Brian, 1993. "On Designing Economic Agents That Behave Like Human Agents," Journal of Evolutionary Economics, Springer, vol. 3(1), pages 1-22, February.
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