Circumventing the Price of Anarchy: Leading Dynamics to Good Behavior

Many natural games can have a dramatic difference between the quality of their best and worst Nash equilibria, even in pure strategies. Yet, nearly all work to date on dynamics shows only convergence to some equilibrium, especially within a polynomial number of steps. In this work we study how agents with some knowledge of the game might be able to quickly (within a polynomial number of steps) find their way to states of quality close to the best equilibrium. We consider two natural learning models in which players choose between greedy behavior and following a proposed good but untrusted strategy and analyze two important classes of games in this context, fair cost-sharing and consensus games. Both games have extremely high Price of Anarchy and yet we show that behavior in these models can efficiently reach low-cost states.