Financial Fragility in Societies of LLM Agents: Coordination Failures and Stabilizing Mechanisms

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Highly relevant for understanding systemic risks in automated trading environments where multiple AI agents interact. Provides insights into how individual protective strategies can lead to collective failures (e.g., flash crashes, liquidity spirals) and how institutional design can mitigate these risks.

Requires setting up a stateful simulation engine with specific financial accounting rules, commitment enforcement, and information visibility controls. Integrating multiple LLM APIs and managing stochastic sampling adds complexity.

Code, configuration files, prompt corpus, and analysis scripts are released in an anonymized repository. The simulation engine architecture and game-theoretic formalizations are fully documented in appendices.