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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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    Keywords: Learning; Optimisation; Distributed control;

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    1. 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.
    2. Hart, Sergiu & Mas-Colell, Andreu, 2006. "Stochastic uncoupled dynamics and Nash equilibrium," Games and Economic Behavior, Elsevier, vol. 57(2), pages 286-303, November.
    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. 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.
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