We propose modeling real-world data markets, where sellers post fixed prices and buyers are free to purchase from any set of sellers they please, as a simultaneous-move game between the buyers. A key component of this model is the negative externality buyers induce on one another due to purchasing similar data, a phenomenon exacerbated by its easy replicability. In the complete-information setting, where all buyers know their valuations, we characterize both the existence and the quality (with respect to optimal social welfare) of the pure-strategy Nash equilibrium under various models of buyer externality. While this picture is bleak without any market intervention, reinforcing the inadequacy of modern data markets, we prove that for a broad class of externality functions, market intervention in the form of a revenue-neutral transaction cost can lead to a pure-strategy equilibrium with strong welfare guarantees. We further show that this intervention is amenable to the more realistic setting where buyers start with unknown valuations and learn them over time through repeated market interactions. For such a setting, we provide an online learning algorithm for each buyer that achieves low regret guarantees with respect to both individual buyers' strategy and social welfare optimal. Our work paves the way for considering simple intervention strategies for existing fixed-price data markets to address their shortcoming and the unique challenges put forth by data products.

翻译：我们提出将现实世界的数据市场建模为买方之间的同时行动博弈，其中卖方设定固定价格，买方可以自由选择任意卖方组合进行购买。该模型的关键组成部分是买方因购买相似数据而产生的负外部性——这种外部性因数据的易复制性而加剧。在完全信息情境下（所有买方已知自身估值），我们刻画了不同买方外部性模型下纯策略纳什均衡的存在性及其相对于最优社会福利的质量。尽管在没有市场干预的情况下前景不容乐观（这凸显了现代数据市场的缺陷），我们证明对于一大类外部性函数，通过收入中性交易成本形式的市场干预，可以产生具有强福利保障的纯策略均衡。我们进一步证明该干预措施适用于更现实的情境：买方初始时未知自身估值，通过重复市场交互随时间学习估值。针对此类情境，我们为每个买方提供在线学习算法，该算法在个体买方策略和社会福利最优两方面均能实现低遗憾保证。我们的工作为现有固定价格数据市场设计简单干预策略以应对其缺陷及数据产品带来的独特挑战铺平了道路。