We propose modeling real-world data markets, where sellers post fixed prices and buyers are free to purchase from any set of sellers, 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 data with a competitive advantage, a phenomenon exacerbated by data's easy replicability. We consider two settings. In the simpler complete-information setting, where all buyers know their valuations, we characterize both the existence and welfare properties of the pure-strategy Nash equilibrium in the presence of buyer externality. While this picture is bleak without any market intervention, reinforcing the limitations of current data markets, we prove that for a standard class of externality functions, market intervention in the form of a transaction cost can lead to a pure-strategy equilibrium with strong welfare guarantees. We next consider a more general setting where buyers start with unknown valuations and learn them over time through repeated data purchases. Our intervention is feasible in this regime as well, and we provide a learning algorithm for buyers in this online scenario that under some natural assumptions, achieves low regret with respect to both individual and cumulative utility metrics. Lastly, we analyze the promise and shortfalls of this intervention under a much richer model of externality. Our work paves the way for investigating simple interventions for existing data markets to address their shortcoming and the unique challenges put forth by data products.

翻译：本文提出将现实数据市场建模为买家之间的同时行动博弈，其中卖家设定固定价格，买家可自由选择任意卖家购买数据。该模型的关键在于买家因购买具有竞争优势的数据而产生的负外部性效应，这种效应因数据的易复制性而加剧。我们考虑两种场景：在简单的完全信息场景中，所有买家已知自身估值，我们刻画了存在买家外部性时的纯策略纳什均衡存在性及其福利性质。尽管缺乏市场干预时情况不容乐观，这印证了当前数据市场的局限性，但我们证明对于标准类外部性函数，以交易成本形式进行的市场干预可产生具有强福利保障的纯策略均衡。随后我们考虑更一般场景：买家初始估值未知，通过重复购买数据逐步学习估值。在此设定下，我们的干预仍具可行性，并提出一种在线学习算法，在若干自然假设下，该算法可在个体效用和累积效用指标上实现低遗憾。最后，我们在更丰富的外部性模型下分析了该干预措施的优势与不足。本研究为探索现有数据市场的简单干预措施铺平道路，以应对数据产品带来的独特挑战与市场缺陷。