测试大模型在多人协作中是否会产生类似嫉妒的心理，发现不同模型行为差异大。
neuralFOMO: Can LLMs Handle Being Second Best? Measuring Envy-Like Preferences in Multi-Agent Settings
- 用心理量表改造评估模型在比较中的反应
- 部分模型宁愿自己少得也不愿对手占优
- 揭示多智能体系统设计中的竞争倾向风险
嫉妒影响人类群体的竞争力与合作，但在大语言模型交互中的作用仍不清楚。随着大模型越来越多地应用于多智能体场景，有必要考察它们在社会比较下是否表现出类似嫉妒的偏好。我们通过两种情境评估模型行为：(1) 点数分配游戏，检验对相对收益与绝对收益的敏感性；(2) 在通用和上下文情境下的对比评估。为结合心理学理论，我们改编了四套成熟量表，涵盖一般性、领域特定、职场及兄弟姐妹间的嫉妒维度。结果揭示模型间存在异质性的嫉妒类模式：一些模型会牺牲自身利益以缩小对手优势，另一些则更倾向于最大化个人收益。这些发现强调了竞争倾向应作为多智能体大模型系统的设计与安全考量因素。
原文摘要 · Abstract (English)
Envy shapes competitiveness and cooperation in human groups, yet its role in large language model interactions remains largely unexplored. As LLMs increasingly operate in multi-agent settings, it is important to examine whether they exhibit envy-like preferences under social comparison. We evaluate LLM behavior across two scenarios: (1) a point-allocation game testing sensitivity to relative versus absolute payoff, and (2) comparative evaluations across general and contextual settings. To ground our analysis in psychological theory, we adapt four established psychometric questionnaires spanning general, domain-specific, workplace, and sibling-based envy. Our results reveal heterogeneous envy-like patterns across models and contexts, with some models sacrificing personal gain to reduce a peer's advantage, while others prioritize individual maximization. These findings highlight competitive dispositions as a design and safety consideration for multi-agent LLM systems.
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