Ex-Ante Truthful Distribution-Reporting Mechanisms

2025-07-05

AI Summary

This paper studies how an auctioneer can design ex-ante incentive-compatible (IC) mechanisms to approximately maximize revenue when buyer value distributions are unknown. Traditional prior-dependent mechanisms—e.g., Myerson auctions—fail to prevent strategic misreporting of distributions by buyers. To address this, the authors introduce two novel mechanism families: threshold-augmented mechanisms, which enforce truthful distribution reporting via tunable thresholds, and Peer-Max, the first mechanism achieving a constant-factor approximation to optimal revenue under independent, non-identically distributed (i.i.d.-free) values; its revenue is at least a fixed fraction of either social welfare or second-price revenue. The paper establishes tight upper and lower bounds, proves ex-ante IC and revenue approximation guarantees, and extends results to multi-item settings. Its core contribution is the first general auction framework that simultaneously achieves ex-ante incentive compatibility and constant-factor revenue approximation under strategic distribution reporting.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements

Abstract

This paper studies mechanism design for revenue maximization in a distribution-reporting setting, where the auctioneer does not know the buyers' true value distributions. Instead, each buyer reports and commits to a bid distribution in the ex-ante stage, which the auctioneer uses as input to the mechanism. Buyers strategically decide the reported distributions to maximize ex-ante utility, potentially deviating from their value distributions. As shown in previous work, classical prior-dependent mechanisms such as the Myerson auction fail to elicit truthful value distributions at the ex-ante stage, despite satisfying Bayesian incentive compatibility at the interim stage. We study the design of ex-ante incentive compatible mechanisms, and aim to maximize revenue in a prior-independent approximation framework. We introduce a family of threshold-augmented mechanisms, which ensures ex-ante incentive compatibility while boosting revenue through ex-ante thresholds. Based on these mechanisms, we construct the Peer-Max Mechanism, which achieves an either-or approximation guarantee for general non-identical distributions. Specifically, for any value distributions, its expected revenue either achieves a constant fraction of the optimal social welfare, or surpasses the second-price revenue by a constant fraction, where the constants depend on the number of buyers and a tunable parameter. We also provide an upper bound on the revenue achievable by any ex-ante incentive compatible mechanism, matching our lower bound up to a constant factor. Finally, we extend our approach to a setting where multiple units of identical items are sold to buyers with multi-unit demands.

Problem

• Design ex-ante truthful mechanisms for revenue maximization
• Address strategic buyer behavior in distribution reporting
• Achieve prior-independent revenue approximation guarantees

Innovation

• Threshold-augmented mechanisms ensure ex-ante incentive compatibility
• Peer-Max Mechanism achieves constant fraction revenue guarantees
• Upper bound matches lower bound for ex-ante mechanisms