Learning Collusion-Resistant Auctions via Coalition Regret Minimization

Auctions serve as efficient frameworks for resource allocation and have profoundly influenced digital economies, but deriving optimal mechanisms for multi-bidder, multi-item auctions remains analytically intractable. Recently, machine learning (ML) has partially addressed this by designing revenue-maximizing auctions through individual regret minimization. However, these approaches primarily ensure strategy-proofness against single bidders, leaving mechanisms vulnerable to collusion, which often leads to substantial revenue loss. To address this, we introduce CoalitionNet, an ML-powered auction framework designed to minimize coalition regret. Specifically, it leverages utility externalities to evaluate joint deviations and trains a strong collusion generator for mechanism adversarial learning. Extensive experiments demonstrate that CoalitionNet achieves near-zero coalition regret while maintaining competitive revenue performance. Furthermore, it also demonstrates strong generalization across unseen coalition sizes as well as valuation distributions.