As Large Language Models (LLMs) become increasingly integrated into financial systems, understanding their behavioural properties is crucial. Do LLMs conform to the rational expectations paradigm, do they exhibit human-like "animal spirits", or do they instead manifest distinct "machine spirits"? We investigate these questions with a simulated financial market, exploring the behaviour of 15 LLMs spanning a range of sizes, capabilities, and providers. Our results show that LLMs exhibit a spectrum of economic behaviours, from stable coordination on the fundamental value to human-like speculative bubbles. These behaviours are generally inconsistent with the rational expectations hypothesis. We also consider an ecology of heterogeneous agents, a more realistic setting compared to markets with identical LLM agents. These mixed markets can produce outcomes which vary substantially across repeated simulations. Even the most advanced models fail to consistently stabilise the market, with price bubbles sometimes forming despite only a minority of agents naturally forming bubbles. Instead, advanced models in mixed markets adapt their forecasting strategies to the behaviour of other agents. This adaptation can allow them to successfully exploit less sophisticated counterparts and achieve higher profits, but can also contribute to increased market volatility. These findings suggest that the introduction of AI agents into financial markets fundamentally reshapes their ecology. In particular, heterogeneous populations of LLMs can generate endogenous instability, while individual-level adaptation may amplify, rather than mitigate, market volatility.

翻译：随着大型语言模型（LLMs）日益融入金融系统，理解其行为特性至关重要。LLMs是否遵循理性预期范式？它们是否表现出类似人类的“动物精神”？抑或展现出独特的“机器精神”？我们通过一个模拟金融市场对这些问题展开研究，探讨了15个涵盖不同规模、能力与提供商的LLM的行为。结果表明，LLM表现出从围绕基础价值稳定协调到类似人类投机泡沫的一系列经济行为，这些行为通常与理性预期假设不一致。我们还考虑了异质性代理的生态系统——相较于由同质LLM代理构成的市场，这一设定更具现实性。这些混合市场在不同模拟迭代中可能产生差异显著的博弈结果。即使是最先进的模型也无法始终稳定市场，价格泡沫有时仅因少数天然倾向于形成泡沫的代理出现而生成。相反，混合市场中的先进模型会调整其预测策略以适应其他代理的行为。这种适应能力使其能成功利用能力较弱的对手并获取更高利润，但也可能加剧市场波动。这些发现表明，将AI代理引入金融市场将从根本上重塑其生态结构。特别是，异质性LLM种群可能引发内生不稳定性，而个体层面的适应行为非但未能缓解，反而可能放大市场波动。