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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 configuration of actions in any n-person game with generic payoffs.� 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 generally understand.� The method has potential application�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.

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    File URL: http://www.economics.ox.ac.uk/materials/papers/5128/paper557.pdf
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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," Levine's Bibliography 122247000000000466, UCLA Department of Economics.
    3. 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.
    4. Young, H Peyton, 1993. "The Evolution of Conventions," Econometrica, Econometric Society, vol. 61(1), pages 57-84, January.
    5. Young, H. Peyton, 2009. "Learning by trial and error," Games and Economic Behavior, Elsevier, vol. 65(2), pages 626-643, March.
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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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