Achieving Pareto Optimality Through Distributed Learning
AbstractWe 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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Bibliographic InfoPaper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 557.
Date of creation: 01 Jul 2011
Date of revision:
Learning; Optimisation; Distributed control;
Find related papers by JEL classification:
- C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
- C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
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