Myerson-guided Machine Learning for Near-Optimal Auction Design

Myerson characterizes the revenue-optimal auction for single-item settings, but the optimal mechanism remains unknown in general multi-bidder, multi-item environments. Neural mechanisms provide an expressive approach to this problem, yet some existing methods achieve only limited revenue gains over Item-wise Myerson in larger auction settings. Our profile-level analysis of ALGNet reveals complementary strengths: its gains on low-competition profiles are consistent with cross-item interactions and probabilistic allocations, but it increasingly underperforms Item-wise Myerson as item-level competition intensifies. Motivated by this observation, we propose Myerson-guided Machine Learning (M2L), a three-stage framework that uses Item-wise Myerson as a structural teacher. M2L first imitates Myerson's allocation and payment rules, then autonomously explores cross-item interactions and probabilistic allocations, and finally targets profiles on which the learned mechanism still underperforms Myerson while preserving its advantages elsewhere. Experiments across multi-item benchmarks show that M2L is competitive with, and often outperforms, existing neural approaches in expected revenue, substantially reduces below-Myerson profiles, and maintains low empirically estimated ex-post regret.