AI 中文总结
研究机器学习的均衡效应，主体用周和刘树处理信息并应用于分散信息资产市场，发现价格机制难聚合算法信息，虽有局部均衡收益，但均衡价格聚合信息少于理性均衡，即便主体事前相同，均衡仍具多样性。
AI 中文摘要
我们引入一个框架来研究机器学习的均衡效应。主体使用周和刘（1968）树来处理信息，这是一种广泛使用且有闭式解的机器学习程序。我们将该模型应用于基于赫尔维格（1980）的分散信息资产市场。价格机制无法聚合算法提取的信息，即使是近似聚合也做不到。虽有算法带来的局部均衡收益，但均衡价格聚合的信息少于理性均衡。即使主体事前相同，均衡通常也具有多样的世界模型、需求和效用。
英文摘要
We introduce a framework for studying the equilibrium effects of machine learning. Agents process information using a Chow and Liu (1968) tree, a widely-used machine learning procedure that admits a closed-form solution. We apply the model to an asset market with dispersed information based on Hellwig (1980). The price mechanism fails to aggregate the information extracted by the algorithm, even approximately. While there are partial equilibrium benefits from access to algorithms, the equilibrium price aggregates less information than the rational equilibrium. Equilibrium typically features diverse world-models, demands, and utilities, even with ex ante identical agents.
