As the scale of machine learning models increases, trends such as scaling laws anticipate consistent downstream improvements in predictive accuracy. However, these trends take the perspective of a single model-provider in isolation, while in reality providers often compete with each other for users. In this work, we demonstrate that competition can fundamentally alter the behavior of these scaling trends, even causing overall predictive accuracy across users to be non-monotonic or decreasing with scale. We define a model of competition for classification tasks, and use data representations as a lens for studying the impact of increases in scale. We find many settings where improving data representation quality (as measured by Bayes risk) decreases the overall predictive accuracy across users (i.e., social welfare) for a marketplace of competing model-providers. Our examples range from closed-form formulas in simple settings to simulations with pretrained representations on CIFAR-10. At a conceptual level, our work suggests that favorable scaling trends for individual model-providers need not translate to downstream improvements in social welfare in marketplaces with multiple model providers.

翻译：随着机器学习模型规模的不断扩大，诸如规模定律等趋势预期会持续提升下游预测精度。然而，这些趋势仅考虑单一模型提供者孤立视角，现实中提供者时常相互竞争以争夺用户。本研究证明，竞争机制可能从根本上改变规模定律的行为特征，甚至导致用户整体预测精度随规模增长呈现非单调或下降趋势。我们定义了分类任务中的竞争模型，并通过数据表征这一视角研究规模增长的影响。在众多场景中发现，提升数据表征质量（以贝叶斯风险衡量）反而会降低竞争性模型提供者市场中用户的整体预测精度（即社会福利）。我们的实例涵盖简单场景的闭式公式推导以及基于CIFAR-10预训练表征的仿真实验。从概念层面而言，本研究揭示出：在多模型提供者的市场中，个体模型提供者有利的规模增长趋势未必能转化为社会福利的持续改善。