用博弈论分析多个模型在异构数据源中的竞争关系
Heterogeneous Data Game: Characterizing the Model Competition Across Multiple Data Sources
- 构建异构数据博弈框架，模拟多模型竞争
- 发现均衡可为同质或异质，取决于数据源特征
- 适合关注市场竞争与政策设计的研究者
真实世界机器学习中，多个数据源间的异构性普遍存在。尽管许多方法聚焦于单个模型处理多样化数据，但实际市场往往由多个竞争的机器学习提供商构成。本文提出一个博弈论框架——异构数据博弈，用于分析这些提供商在异构数据源之间的竞争行为。我们研究了纯纳什均衡（PNE）的存在性，发现其可能不存在，也可能为同质（所有提供者采用相同模型）或异质（各提供者专精于不同数据源）。分析涵盖垄断、双寡头及更一般的市场结构，揭示了数据源选择模型的“温度”以及某些数据源的主导性如何影响均衡结果。本文提供了对同质与异质均衡的理论洞察，为竞争性机器学习市场的监管政策与实践策略提供指导。
原文摘要 · Abstract (English)
Data heterogeneity across multiple sources is common in real-world machine learning (ML) settings. Although many methods focus on enabling a single model to handle diverse data, real-world markets often comprise multiple competing ML providers. In this paper, we propose a game-theoretic framework -- the Heterogeneous Data Game -- to analyze how such providers compete across heterogeneous data sources. We investigate the resulting pure Nash equilibria (PNE), showing that they can be non-existent, homogeneous (all providers converge on the same model), or heterogeneous (providers specialize in distinct data sources). Our analysis spans monopolistic, duopolistic, and more general markets, illustrating how factors such as the "temperature" of data-source choice models and the dominance of certain data sources shape equilibrium outcomes. We offer theoretical insights into both homogeneous and heterogeneous PNEs, guiding regulatory policies and practical strategies for competitive ML marketplaces.
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