Learn Then Decide: A Learning Approach for Designing Data Marketplaces

• 成果类型: Article; Early Access
• 署名作者: Gao, Yingqi; Xu, Wenlu; Zhou, Jin J.; Zhou, Hua; Chen, Yong; Dai, Xiaowu
• 署名单位: University of California System; University of California Los Angeles; University of California System; University of California Los Angeles; University of Pennsylvania; Pennsylvania Medicine
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2026.2655549
• 发表日期: 2026-06-06
• 关键词: Auctions; Data marketplace; Nonparametric density estimation; online learning; revenue maximization; reserve prices; auctions; MARKETS

摘要

As data marketplaces become increasingly central to the digital economy, it is crucial to design efficient pricing mechanisms that optimize revenue while ensuring fair and adaptive pricing. We introduce the Maximum Auction-to-Posted Price (MAPP) mechanism, a novel two-stage approach that first estimates the bidders' value distribution through auctions and then determines the optimal posted price based on the learned distribution. We establish that MAPP is individually rational and incentive-compatible, which ensures truthful bidding while balancing revenue maximization and minimizing price discrimination. On the theoretical side, we establish a statistical viewpoint that recasts revenue optimization as a valuation density estimation problem: we show that revenue regret can be controlled by uniform error in estimating the valuation density. MAPP achieves a regret of O-p(n(-1)( log n)(2)) when incorporating historical bid data, where n is the number of bids in the current round. For sequential dataset sales over T rounds, we propose an online MAPP mechanism that dynamically adjusts pricing across datasets with varying value distributions. Our approach achieves no-regret learning, with the average regret converging at a rate of O-p(T-1/2( log T)(2)). We validate the effectiveness of MAPP through simulations and real-world data from the FCC AWS-3 spectrum auction. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.