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Achieving Pareto Optimality Through Distributed Learning

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  • H Peyton Young
  • Jason R. Marden and Lucy Y. Pao
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    Abstract

    We propose a simple payoff-based learning rule that is completely decentralized, and that leads to an efficient configuaration of actions in any n-person finite strategic-form game with generic payoffs.� The algorithm follows the theme of exploration versus exploitation and is hence stochastic in nature.� We prove that if all agents adhere to this algorithm, then the agents will select the action profile that maximizes the sum of the agents' payoffs a high percentage of time.� The algorithm requires no communication.� Agents respond solely to changes in their own realized payoffs, which are affected by the actions of other agents in the system in ways that they do not necessarily understand.� The method can be applied to the optimization of complex systems with many distributed components, such as the routing of information in networks and the design and control of wind farms.� The proof of the proposed learning algorithm relies on the theory of large deviations for perturbed Markov chains.

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    Bibliographic Info

    Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 557.

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    Date of creation: 01 Jul 2011
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    Handle: RePEc:oxf:wpaper:557

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    Related research

    Keywords: Learning; Optimisation; Distributed control;

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    1. Fudenberg, Drew & Maskin, Eric, 1986. "The Folk Theorem in Repeated Games with Discounting or with Incomplete Information," Econometrica, Econometric Society, vol. 54(3), pages 533-54, May.
    2. Sergiu Hart & Andreu Mas-Colell, 2004. "Stochastic Uncoupled Dynamics and Nash Equilibrium," Discussion Paper Series dp371, The Center for the Study of Rationality, Hebrew University, Jerusalem.
    3. Young, H Peyton, 1993. "The Evolution of Conventions," Econometrica, Econometric Society, vol. 61(1), pages 57-84, January.
    4. Young, H. Peyton, 2009. "Learning by trial and error," Games and Economic Behavior, Elsevier, vol. 65(2), pages 626-643, March.
    5. Foster, Dean P. & Young, H. Peyton, 2006. "Regret testing: learning to play Nash equilibrium without knowing you have an opponent," Theoretical Economics, Econometric Society, vol. 1(3), pages 341-367, September.
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    Cited by:
    1. Pradelski, Bary S.R. & Young, H. Peyton, 2012. "Learning efficient Nash equilibria in distributed systems," Games and Economic Behavior, Elsevier, vol. 75(2), pages 882-897.

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