Why Better Models Can Create Riskier Systems: Evidence from LLM Agents in Financial Markets

2026-09-03

AI Summary

研究探讨了在金融市场上，更强大的语言模型可能导致系统级风险增加的问题，通过代理模拟测试了模型能力与市场风险之间的关系。

Abstract

Large language models (LLMs) are being deployed at scale in consequential real-world systems, from financial markets to content moderation to hiring. We show that improving individual model capability can degrade rather than improve system-level outcomes. We hypothesize that shared training and architectures can lead more capable LLMs to behave more similarly, creating correlated actions that do not diversify away. We develop a general framework showing how this correlation creates a non-diversifiable risk floor and test its predictions in financial markets using an agent-based simulation with LLM traders of varying general-purpose capability. We find that: (1) frontier LLMs exhibit significantly correlated behavior that increases with capability; (2) when their shared reasoning is accurate, increasing agent participation reduces market-level risk; and (3) when agents share a common misinformation environment, the same correlated behavior becomes a liability. Together, these results identify a capability paradox: improving individual models does not necessarily produce better system-level outcomes. Whether the same dynamics arise in other domains is an open empirical question.

Problem

• large language models
• financial markets
• system-level outcomes
• correlated behavior
• risk

Innovation

• Large Language Models
• Correlated Behavior
• System-level Outcomes
• Capability Paradox