Online auction is a cornerstone of e-commerce, and a key challenge is designing incentive-compatible mechanisms that maximize expected revenue. Existing approaches often assume known bidder value distributions and fixed sets of bidders and items, but these assumptions rarely hold in real-world settings where bidder values are unknown, and the number of future participants is uncertain. In this paper, we introduce the Conformal Online Auction Design (COAD), a novel mechanism that maximizes revenue by quantifying uncertainty in bidder values without relying on known distributions. COAD incorporates both bidder and item features, using historical data to design an incentive-compatible mechanism for online auctions. Unlike traditional methods, COAD leverages distribution-free uncertainty quantification techniques and integrates machine learning methods, such as random forests, kernel methods, and deep neural networks, to predict bidder values while ensuring revenue guarantees. Moreover, COAD introduces bidder-specific reserve prices, based on the lower confidence bounds of bidder valuations, contrasting with the single reserve prices commonly used in the literature. We demonstrate the practical effectiveness of COAD through an application to real-world eBay auction data. Theoretical results and extensive simulation studies further validate the properties of our approach.

翻译：在线拍卖是电子商务的基石，其核心挑战在于设计激励相容的机制以最大化期望收益。现有方法通常假设竞拍者价值分布已知，且竞拍者与商品集合固定，但这些假设在现实场景中往往难以成立——竞拍者价值未知，未来参与者数量亦不确定。本文提出一种新颖的机制——保形在线拍卖设计（COAD），该机制通过量化竞拍者价值的不确定性以最大化收益，且无需依赖已知分布假设。COAD融合竞拍者与商品特征，利用历史数据为在线拍卖设计激励相容机制。与传统方法不同，COAD采用分布无关的不确定性量化技术，并结合随机森林、核方法、深度神经网络等机器学习方法预测竞拍者价值，同时确保收益保障。此外，COAD引入了基于竞拍者估值置信下界的个性化保留价机制，区别于文献中普遍采用的单一保留价策略。我们通过实际eBay拍卖数据的应用验证了COAD的实践有效性。理论结果与大量仿真研究进一步证实了该方法的优越特性。