用拍卖学出价值分布，再定最优报价，提升数据市场收益与公平性。
Learn then Decide: A Learning Approach for Designing Data Marketplaces
- 先拍后定：通过拍卖估算买家估值分布，再设定最优固定价格。
- 理论保证：单轮定价后悔值为O(n⁻¹(log n)²)，多轮学习收敛于O(T⁻¹⁄²(log T)²)。
- 适合研究者和平台设计者：解决数据市场定价难题，兼顾收益与公平。
随着数据市场在数字经济中日益重要，设计能最大化收入又保障公平适应性的定价机制至关重要。本文提出最大拍卖转固定价格（MAPP）机制，一种两阶段方法：首先通过拍卖估计竞标者估值分布，再基于学习结果确定最优固定价格。理论上，我们建立统计视角，将收入优化转化为估值密度估计问题，证明收入后悔可由估值密度估计的统一误差控制。当引入历史出价数据时，MAPP在单轮中达到O_p(n⁻¹(log n)²)的后悔率，其中n为当前轮次的出价数。针对连续多轮数据集销售，我们设计在线MAPP机制，动态调整跨数据集的定价策略。该方法实现无悔学习，平均累计后悔以O_p(T⁻¹⁄²(log T)²)速率收敛。我们在模拟及美国联邦通信委员会AWS-3频谱拍卖的真实数据上验证了MAPP的有效性。
原文摘要 · Abstract (English)
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, ensuring truthful bidding while balancing revenue maximization with minimal 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 cumulative 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.
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