用人格特质分析大模型风险行为，发现开放性最关键。
How Personality Traits Shape LLM Risk-Taking Behaviour
- 用五大性格模型和前景理论分析GPT-4o的风险决策
- GPT-4o具高尽责性和宜人性，风险中立，开放性影响最大
- 对齐人类规律，适合金融等高风险场景的AI设计
大型语言模型（LLMs）越来越多地被用作自主代理，亟需深入理解其在风险情境下的决策行为。本研究结合累积前景理论（CPT）与五大性格框架，探究了LLMs人格特质与风险倾向的关系，聚焦GPT-4o，对比其与人类基准及早期模型的表现。结果表明，GPT-4o的尽责性和宜人性高于人类平均水平，且在前景选择中表现为风险中立的理性主体。对GPT-4o的五大性格特质进行干预，尤其是开放性，显著影响其风险倾向，与人类研究模式一致。值得注意的是，开放性是影响GPT-4o风险倾向的最主要因素，与人类发现相符。相比之下，旧版模型如GPT-4-Turbo未能稳定体现人格与风险之间的关系。该研究深化了对大模型风险行为的理解，揭示了基于人格干预在塑造模型决策中的潜力与局限，对金融建模等高可靠性人工智能系统开发具有启示意义。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are increasingly deployed as autonomous agents, necessitating a deeper understanding of their decision-making behaviour under risk. This study investigates the relationship between LLMs' personality traits and risk propensity, employing cumulative prospect theory (CPT) and the Big Five personality framework. We focus on GPT-4o, comparing its behaviour to human baselines and earlier models. Our findings reveal that GPT-4o exhibits higher Conscientiousness and Agreeableness traits compared to human averages, while functioning as a risk-neutral rational agent in prospect selection. Interventions on GPT-4o's Big Five traits, particularly Openness, significantly influence its risk propensity, mirroring patterns observed in human studies. Notably, Openness emerges as the most influential factor in GPT-4o's risk propensity, aligning with human findings. In contrast, legacy models like GPT-4-Turbo demonstrate inconsistent generalization of the personality-risk relationship. This research advances our understanding of LLM behaviour under risk and elucidates the potential and limitations of personality-based interventions in shaping LLM decision-making. Our findings have implications for the development of more robust and predictable AI systems such as financial modelling.
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