Coordination in Noncooperative Multiplayer Matrix Games via Reduced Rank Correlated Equilibria
For game theory and multiagent systems, this provides a scalable coordination method that avoids exponential complexity while maintaining near-optimal performance.
The paper introduces reduced rank correlated equilibria, a coordination mechanism that reduces joint action complexity from O(m^n) to O(mn) in multiplayer games. Applied to air traffic queue management, it solves problems with 4000 times more joint actions than correlated equilibria, achieving similar or better fairness (up to 99.5% improvement) and up to 50.4% lower average delay cost compared to Nash equilibria, with a maximum optimality gap of 0.066%.
Coordination in multiplayer games enables players to avoid the lose-lose outcome that often arises at Nash equilibria. However, designing a coordination mechanism typically requires the consideration of the joint actions of all players, which becomes intractable in large-scale games. We develop a novel coordination mechanism, termed reduced rank correlated equilibria, which reduces the number of joint actions to be considered and thereby mitigates computational complexity. The idea is to approximate the set of all joint actions with the actions used in a set of pre-computed Nash equilibria via a convex hull operation. In a game with n players and each player having m actions, the proposed mechanism reduces the number of joint actions considered from O(m^n) to O(mn). We demonstrate the application of the proposed mechanism to an air traffic queue management problem. Compared with the correlated equilibrium-a popular benchmark coordination mechanism-the proposed approach is capable of solving a problem involving four thousand times more joint actions while yielding similar or better performance in terms of a fairness indicator and showing a maximum optimality gap of 0.066% in terms of the average delay cost. In the meantime, it yields a solution that shows up to 99.5% improvement in a fairness indicator and up to 50.4% reduction in average delay cost compared to the Nash solution, which does not involve coordination.