Achieving Pareto Optimality through Distributed Learning
H. Peyton Young, Jason R. Marden, Lucy Y. Pao · 2024
Abstract This chapter introduces a simple payoff-based learning rule that is completely decentralized and leads to an efficient configuration of actions in any n-person finite strategic-form game with generic payoffs. 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 methodcan 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.