Ex-Ante Truthful Distribution-Reporting Mechanisms
Xiaotie Deng, Yanru Guan, Ningyuan Li, Zihe Wang, Jie Zhang

TL;DR
This paper designs ex-ante truthful mechanisms for revenue maximization in distribution-reporting auctions, introducing threshold-augmented and Peer-Max mechanisms that achieve strong approximation guarantees without prior knowledge of buyer distributions.
Contribution
It introduces a novel family of threshold-augmented mechanisms and the Peer-Max Mechanism, providing the first ex-ante incentive compatible mechanisms with provable revenue guarantees in distribution-reporting settings.
Findings
Peer-Max Mechanism achieves a constant-factor approximation to optimal revenue.
The mechanisms guarantee ex-ante incentive compatibility for strategic buyers.
Upper bounds match the revenue guarantees of the proposed mechanisms.
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…
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