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。

固定价格数据市场中的均衡与学习：外部性

Equilibrium and Learning in Fixed-Price Data Markets with Externality

Motivated by recent progress on pricing in the AI literature, we study
marketplaces that contain multiple vendors offering identical or similar
products and unit-demand buyers with different valuations on these vendors. The
objective of each vendor is to set the price of its product to a fixed value so
that its profit is maximized. The profit depends on the vendor's price itself
and the total volume of buyers that find the particular price more attractive
than the price of the vendor's competitors. We model the behaviour of buyers
and vendors as a two-stage full-information game and study a series of
questions related to the existence, efficiency (price of anarchy) and
computational complexity of equilibria in this game. To overcome situations
where equilibria do not exist or exist but are highly inefficient, we consider
the scenario where some of the vendors are subsidized in order to keep prices
low and buyers highly satisfied.

本篇论文研究了包含多个卖家和具有不同对这些卖家估值的单册买家的市场，分析了其定价等问题，并探讨了含补贴的情形以提高效率。