内容公司竟愿付费让AI平台使用自己数据，揭示生成式AI时代的新型数据博弈。
Data Sharing with a Generative AI Competitor
- 构建斯塔克尔伯格博弈模型，分析内容公司与GenAI平台的数据共享策略。
- 发现公司可能倒贴钱让平台使用数据，形成高成本共享均衡。
- 适用于平台设计、政策制定者，理解数据交易的经济驱动力。
随着生成式AI平台发展，其对竞争方内容的依赖及对第三方数据源的获取能力，给数据共享决策带来新挑战。本文建立内容创作企业与可从外部专家获取内容的GenAI平台之间的数据共享模型，采用斯塔克尔伯格博弈框架：企业首先决定向GenAI共享其专有数据集的规模，随后GenAI决定从外部专家采购额外数据的数量。双方效用取决于用户流量、货币转移以及从外部获取数据的成本。我们刻画了唯一的子博弈完美均衡，并发现一个反直觉现象：企业可能愿意支付费用以促使平台共享自身数据，导致高成本数据共享均衡。我们进一步刻画了帕累托改进的数据定价集合，证明此类改进仅在企业付费共享数据时成立。最后，研究了如何设定价格以实现不同设计目标，如促进企业数据共享、专家数据获取或两者平衡。结果揭示了生成式AI时代数据合作背后的经济动因，为平台、监管机构和政策制定者提供数据交换机制设计的参考。
原文摘要 · Abstract (English)
As GenAI platforms grow, their dependence on content from competing providers, combined with access to alternative data sources, creates new challenges for data-sharing decisions. In this paper, we provide a model of data sharing between a content creation firm and a GenAI platform that can also acquire content from third-party experts. The interaction is modeled as a Stackelberg game: the firm first decides how much of its proprietary dataset to share with GenAI, and GenAI subsequently determines how much additional data to acquire from external experts. Their utilities depend on user traffic, monetary transfers, and the cost of acquiring additional data from external experts. We characterize the unique subgame perfect equilibrium of the game and uncover a surprising phenomenon: The firm may be willing to pay GenAI to share the firm's own data, leading to a costly data-sharing equilibrium. We further characterize the set of Pareto improving data prices, and show that such improvements occur only when the firm pays to share data. Finally, we study how the price can be set to optimize different design objectives, such as promoting firm data sharing, expert data acquisition, or a balance of both. Our results shed light on the economic forces shaping data-sharing partnerships in the age of GenAI, and provide guidance for platforms, regulators and policymakers seeking to design effective data exchange mechanisms.
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