测试大模型在博弈中的公平与风险偏好，发现其行为受性别提示影响。
Decision and Gender Biases in Large Language Models: A Behavioral Economic Perspective
- 用博弈实验检验模型决策行为
- 模型有轻微损失厌恶和公平关切
- 性别提示会引发微妙偏差，适合关注AI伦理者
大型语言模型（LLMs）越来越多地参与经济与组织流程，如客户支持、招聘、投资建议和政策分析。这些系统常被假定为理性决策，但其训练数据来自人类语言语料库，可能包含认知与社会偏见。本研究从行为经济学视角，通过两个经典实验——最后通牒博弈和赌博游戏——考察谷歌Gemma7B和Qwen两款先进模型在中性及性别条件提示下的决策表现。通过估计不公平厌恶和损失厌恶参数，并与人类基准对比，发现模型虽表现出理性程度减弱，但仍存在中度公平关切、轻微损失厌恶以及细微的性别相关差异。
原文摘要 · Abstract (English)
Large language models (LLMs) increasingly mediate economic and organisational processes, from automated customer support and recruitment to investment advice and policy analysis. These systems are often assumed to embody rational decision making free from human error; yet they are trained on human language corpora that may embed cognitive and social biases. This study investigates whether advanced LLMs behave as rational agents or whether they reproduce human behavioural tendencies when faced with classic decision problems. Using two canonical experiments in behavioural economics, the ultimatum game and a gambling game, we elicit decisions from two state of the art models, Google Gemma7B and Qwen, under neutral and gender conditioned prompts. We estimate parameters of inequity aversion and loss-aversion and compare them with human benchmarks. The models display attenuated but persistent deviations from rationality, including moderate fairness concerns, mild loss aversion, and subtle gender conditioned differences.
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