Equilibrium Information Aggregation under Machine Learning

2026-07-15

AI Summary

This study investigates the impact of machine learning algorithms on information aggregation in asset markets with dispersed information, focusing on whether price mechanisms can fully reflect the information extracted by such algorithms. The authors introduce the Chow-Liu tree into a general equilibrium framework à la Hellwig (1980), constructing an equilibrium model in which agents employ this algorithm for Bayesian inference. The analysis reveals that even when agents are initially homogeneous, they endogenously develop heterogeneous beliefs, demand functions, and utilities. More importantly, while machine learning enhances information processing in partial equilibrium, it leads to less informative prices in general equilibrium compared to the rational expectations benchmark, indicating that market prices fail to efficiently aggregate the information uncovered by machine learning.

Abstract

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.

Problem

• Information Aggregation
• Machine Learning
• Equilibrium
• Asset Market
• Chow-Liu Tree

Innovation

• machine learning
• information aggregation
• equilibrium
• Chow-Liu tree
• asset markets