Chance-constrained correlated equilibrium enables coordination of noncooperative agents under cost uncertainty through probabilistic incentive-compatibility guarantees. However, computing such equilibria becomes intractable in large-scale systems due to the exponential growth of the joint action space. We develop an approximation method for computing chance-constrained correlated equilibria by showing that these equilibria admit a representation as convex combinations of a finite set of chance-constrained pure Nash equilibria, enabling tractable computation without solving the full correlated equilibrium program. Numerical experiments on large-scale multi-airline coordination scenarios demonstrate substantial reductions in computation time while achieving lower system delay costs compared to current operational practice. Under cost uncertainty, the proposed method consistently achieves lower deviation rate compared to the full formulation while achieving comparable coordination performance.

翻译：带机会约束的关联均衡通过概率激励相容性保证，实现了成本不确定条件下非合作智能体的协调。然而，由于联合动作空间的指数级增长，在大规模系统中计算此类均衡变得难以处理。我们通过证明此类均衡可表示为有限个带机会约束纯策略纳什均衡的凸组合，发展了一种近似计算方法，从而无需求解完整的关联均衡规划即可实现可处理的计算。针对大规模多航空公司协调场景的数值实验表明，与当前实际运行方案相比，该方法在显著降低计算时间的同时实现了更低的系统延迟成本。在成本不确定性条件下，所提方法在保持相当协调性能的同时，始终比完整规划方案达到更低的偏离率。