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.

该论文提出了一种模拟现实世界数据市场的模型，研究了买家之间存在的负向外部性问题，进而探讨了不同干预市场方式下的 Nash 均衡状态及社会福利最优的情况，并给出了一种适用于未知估值的在线学习算法。