We study a game between two firms in which each provide a service based on machine learning. The firms are presented with the opportunity to purchase a new corpus of data, which will allow them to potentially improve the quality of their products. The firms can decide whether or not they want to buy the data, as well as which learning model to build with that data. We demonstrate a reduction from this potentially complicated action space to a one-shot, two-action game in which each firm only decides whether or not to buy the data. The game admits several regimes which depend on the relative strength of the two firms at the outset and the price at which the data is being offered. We analyze the game's Nash equilibria in all parameter regimes and demonstrate that, in expectation, the outcome of the game is that the initially stronger firm's market position weakens whereas the initially weaker firm's market position becomes stronger. Finally, we consider the perspective of the users of the service and demonstrate that the expected outcome at equilibrium is not the one which maximizes the welfare of the consumers.

研究了两个公司在机器学习为基础的服务领域中的游戏，考虑购买一组新的数据对公司产品质量的提高，分析了不同参数下的 Nash 均衡情况，并指出游戏的平衡态与顾客福利最大化不一致，证明了游戏结局是较强公司的市场地位减弱而较弱公司的市场地位加强。

数据采集游戏的平衡特征描述