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Ex Machina: Financial Stability in the Age of Artificial Intelligence
Does artificial intelligence (AI) pose a threat to financial stability? This paper develops a simulation-based framework to study how AI agents behave in a mutual-fund redemption game with strategic complementarities and multiple equilibria. Different AI technologies, namely Q-learning (QL) algorithms and large language models (LLMs), generate distinct redemption profiles. QL-investors coordinate among themselves but exhibit a bias toward excessive early redemption that amplifies fund fragility. LLM-investors instead internalize the equilibrium structure of the problem and better align with theoretical predictions. However, their belief heterogeneity weakens coordination, thereby making their redemptions less predictable. Thus, our findings highlight that the design of AI systems is material for financial stability.
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