Achieving pareto optimality through distributed learning

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 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.