Firms' algorithm development practices are often homogeneous. Whether firms train algorithms on similar data, aim at similar benchmarks, or rely on similar pre-trained models, the result is correlated predictions. We model the impact of correlated algorithms on competition in the context of personalized pricing. Our analysis reveals that (1) higher correlation diminishes consumer welfare and (2) as consumers become more price sensitive, firms are increasingly incentivized to compromise on the accuracy of their predictions in exchange for coordination. We demonstrate our theoretical results in a stylized empirical study where two firms compete using personalized pricing algorithms. Our results underscore the ease with which algorithms facilitate price correlation without overt communication, which raises concerns about a new frontier of anti-competitive behavior. We analyze the implications of our results on the application and interpretation of US antitrust law.

翻译：企业算法开发实践往往呈现同质化特征。无论企业使用相似数据进行算法训练、追求相似性能基准，还是依赖相似的预训练模型，最终都会导致预测结果产生相关性。本文在个性化定价场景下构建了相关性算法对竞争影响的模型。分析表明：(1) 算法相关性增强会降低消费者福利；(2) 随着消费者价格敏感度提升，企业更倾向于牺牲预测精度以换取协调效应。我们通过一个程式化的实证研究验证理论结果：两家企业使用个性化定价算法展开竞争时，算法能在无需显性沟通的情况下轻易实现价格关联，这引发了对新型反竞争行为的担忧。最后，我们探讨了研究结果对美国反垄断法律适用与解释的启示。