Enhancing Affine Maximizer Auctions with Correlation-Aware Payment

2026-02-10

AI Summary

This work addresses the challenge of achieving optimal revenue in traditional affine maximizer auctions (AMA) when bidders’ valuations exhibit interdependencies. The authors propose a correlation-aware CA-AMA mechanism that, for the first time, incorporates valuation correlation information directly into the AMA payment rule. By formulating a revenue-maximization optimization model subject to individual rationality (IR) constraints, the mechanism enhances auction revenue while preserving dominant-strategy incentive compatibility (DSIC) and IR. Theoretical analysis demonstrates that CA-AMA attains revenue levels unattainable by classical AMA mechanisms. Empirical evaluations across multiple valuation distributions confirm that CA-AMA consistently yields significantly higher revenue with negligible violations of IR constraints.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Affine Maximizer Auctions
• valuation correlation
• revenue optimization
• mechanism design
• incentive compatibility

Innovation

• Correlation-Aware Payment
• Affine Maximizer Auctions
• Dominant-Strategy Incentive Compatibility
• Mechanism Design
• Individual Rationality