改进拍卖机制支付方式，提升相关估值下的收益表现。
Enhancing Affine Maximizer Auctions with Correlation-Aware Payment
- 引入关联感知支付，扩展经典拍卖机制表达能力。
- 理论证明在相关估值下可逼近最优收益，且违背理性约束极少。
- 适用于需高收益的自动化拍卖场景，尤其适合有估值关联的市场。
仿射最大化拍卖（AMAs）作为VCG机制的推广，在自动化机制设计中广泛应用，因其具备主导策略激励相容（DSIC）和个体理性（IR）特性。然而由于支付形式固定，其表达能力受限，尤其在投标人估值相关时表现不佳。本文提出关联感知仿射最大化拍卖（CA-AMA），通过引入新的关联感知支付机制，保持DSIC性质，并将最优CA-AMA的求解建模为满足IR约束的约束优化问题。理论上刻画了经典AMAs在某些分布下可能表现极差，而CA-AMA可达到最优收益的情形。针对优化问题，设计了一种两阶段训练算法，推导出目标函数连续性及偏离严格IR的泛化界。大量实验表明，该算法能在多种分布下快速找到近似最优的CA-AMA，显著提升收益且仅轻微违反IR。
原文摘要 · Abstract (English)
Affine Maximizer Auctions (AMAs), a generalized mechanism family from VCG, are widely used in automated mechanism design due to their inherent dominant-strategy incentive compatibility (DSIC) and individual rationality (IR). However, as the payment form is fixed, AMA's expressiveness is restricted, especially in distributions where bidders' valuations are correlated. In this paper, we propose Correlation-Aware AMA (CA-AMA), a novel framework that augments AMA with a new correlation-aware payment. We show that any CA-AMA preserves the DSIC property and formalize finding optimal CA-AMA as a constraint optimization problem subject to the IR constraint. Then, we theoretically characterize scenarios where classic AMAs can perform arbitrarily poorly compared to the optimal revenue, while the CA-AMA can reach the optimal revenue. For optimizing CA-AMA, we design a practical two-stage training algorithm. We derive that the target function's continuity and the generalization bound on the degree of deviation from strict IR. Finally, extensive experiments showcase that our algorithm can find an approximate optimal CA-AMA in various distributions with improved revenue and a low degree of violation of IR.
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