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.

翻译：嫉妒塑造了人类群体中的竞争与合作，但其在大型语言模型交互中的作用尚未得到充分探索。随着LLMs日益在多智能体环境中运行，研究其在社会比较下是否表现出嫉妒类偏好至关重要。我们通过两种场景评估LLM行为：（1）测试对相对收益与绝对收益敏感度的点数分配博弈；（2）通用情境与特定情境下的比较评估。为使分析植根于心理学理论，我们改编了四套成熟的涵盖通用型、领域特定型、职场型及手足竞争型嫉妒的心理测量问卷。研究结果显示，不同模型与情境中呈现出异质性的嫉妒类行为模式：部分模型会牺牲自身收益以削弱同伴优势，而其他模型则优先追求个体收益最大化。这些发现揭示了竞争性倾向应作为多智能体LLM系统设计与安全性的重要考量因素。