Why Better Models Can Create Riskier Systems: Evidence from LLM Agents in Financial Markets
J. Ross, E. So, Z. De Simone, C. Pozniak, A. W. Lo · 2026
Ce que cela signifie pour vous
Des analystes qui tournent sur le même modèle et lisent les mêmes news pèsent moins que des avis vraiment distincts, car la vraie diversité vient d'informations indépendantes et pas seulement de rôles différents.
Résumé
Argues that as language models grow more capable, their errors become more correlated with one another, so a market populated by many LLM agents can carry a risk floor that adding more agents never diversifies away. Across the models studied, the correlation of residual decisions between pairs of models rises with capability, while sharing a provider shows no significant effect. In a simulated single-asset market, more LLM traders improve price discovery under normal conditions, but when every agent reads the same misleading commentary, tracking error rises well above a noise-trader baseline for two of the three model families tested. The authors also show that mixing model families removes only the family-specific part of the correlation; the part that comes from a shared information environment remains.