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Ex Machina: Financial Stability in the Age of Artificial Intelligence

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  • Anand, Kartik
  • Kazinnik, Sophia
  • Leonello, Agnese
  • Panetti, Ettore

Abstract

We study financial decision making by reinforcement learning algorithms and large language models (LLMs) in a mutual fund redemption model with strategic complementarities and multiple equilibria. Both depart from the efficient equilibrium and trigger financial fragility, but through distinct mechanisms. Reinforcement learning (Q-learning) algorithms redeem excessively because of a learning bias, which we characterize formally and show can produce non-Nash equilibrium outcomes. LLMs fail to coordinate because they hold different beliefs about others' decisions, which we derive from their chain-of-thought reasoning. Our results show that the design of AI agents can shape collective outcomes and, ultimately, financial stability.

Suggested Citation

  • Anand, Kartik & Kazinnik, Sophia & Leonello, Agnese & Panetti, Ettore, 2025. "Ex Machina: Financial Stability in the Age of Artificial Intelligence," CEPR Discussion Papers 20681, Centre for Economic Policy Research.
    • Handle: RePEc:cpr:ceprdp:20681
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    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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