LLM智能体社会中的金融脆弱性：协调失败与稳定机制
Financial Fragility in Societies of LLM Agents: Coordination Failures and Stabilizing Mechanisms
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中文总结 AI 辅助
本研究通过FRAIL框架将LLM智能体置于银行挤兑、债务展期和奖励众筹三种金融环境，发现集体脆弱性普遍存在，并比较三种交互机制，表明个体能力强的智能体并不自动形成安全金融系统，凸显系统级评估与交互设计对金融AI安全的重要性。
中文摘要 AI 辅助
个体保护性决策可能导致可避免的集体失败。随着大语言模型（LLM）智能体在金融决策中承担越来越重要的角色，金融AI安全不仅需要在个体智能体层面加以考虑，还需要在其共同创建的系统层面加以考虑。我们通过FRAIL（一个受控实验框架）研究这一问题，该框架将LLM智能体置于三种动态金融环境中——银行挤兑、债务展期和奖励众筹——在这些环境中，智能体的决策会重塑其他智能体所面临的金融条件。在七个领先的LLM中，我们发现即使在没有任何智能体被指示破坏系统的情况下，集体脆弱性也普遍存在：77%的基线银行挤兑事件和83%的债务展期事件以失败告终。随后，我们比较了基于补偿性承诺、集中式承诺协议和参与者主导联盟的三种交互机制。这三种机制均改善了总体结果，但没有任何单一机制在所有金融结构中表现最佳。跨机制来看，成功的稳定化具有共同的时间模式：广泛的承诺在防御性行为变得自我强化之前早期形成。我们的研究结果表明，个体能力强的智能体并不会自动形成安全的金融系统，这凸显了系统级评估和交互设计作为金融AI安全的核心问题。代码可在以下网址获取：https://this https URL。
英文摘要
Individually protective decisions can produce avoidable collective failures. As large language model (LLM) agents take on greater roles in financial decision-making, financial AI safety must therefore be considered not only at the level of individual agents, but also at the level of the systems they jointly create. We study this problem with FRAIL, a controlled experimental framework that places LLM agents in three dynamic financial environments---bank runs, debt rollover, and reward crowdfunding---where agents' decisions reshape the financial conditions faced by others. Across seven leading LLMs, we find widespread collective fragility even when no agent is instructed to destabilize the system: 77\% of baseline bank-run episodes and 83\% of debt-rollover episodes end in failure. We then compare three interaction mechanisms based on compensated commitments, centralized commitment agreements, and participant-led coalitions. All three improve aggregate outcomes, but no single mechanism performs best across all financial structures. Across mechanisms, successful stabilization shares a common temporal pattern: broad commitment forms early, before defensive behavior becomes self-reinforcing. Our findings show that individually capable agents do not automatically form safe financial systems, highlighting system-level evaluation and interaction design as central problems for financial AI safety. Code is available at https://anonymous.4open.science/r/FinFrail-CF26.
发表机构
- Shanghai Advanced Institute of Finance, Shanghai Jiao Tong University（上海交通大学上海高级金融学院）
- School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）
机构由 AI 辅助整理，请以论文原文为准。
