neuralFOMO: LLMs能否处理第二好？在多智能体设置中测量类似嫉妒的偏好
neuralFOMO: Can LLMs Handle Being Second Best? Measuring Envy-Like Preferences in Multi-Agent Settings
AI总结：
neuralFOMO研究了LLMs在多智能体环境中是否表现出类似嫉妒的偏好，通过点分配游戏和比较评估揭示了不同模型在竞争与合作中的不同表现。
摘要：
嫉妒塑造了人类群体中的竞争与合作，但其在大语言模型互动中的作用仍 largely 未被探索。随着 LLMs 越来越多地在多智能体设置中运行，有必要检查它们是否在社会比较下表现出类似嫉妒的偏好。我们评估了 LLM 在两个场景中的行为：(1) 一个测试对相对收益与绝对收益敏感度的点分配游戏，以及 (2) 在一般和上下文设置中的比较评估。为了使我们的分析扎根于心理理论，我们适应了四个已建立的心理测量问卷，涵盖一般、领域特定、工作场所和兄弟姐妹基于的嫉妒。我们的结果揭示了模型和情境之间的异质嫉妒样模式，一些模型牺牲个人收益以减少同伴的优势，而其他模型则优先考虑个人最大化。这些发现突显了竞争倾向作为多智能体 LLM 系统的设计和安全考虑。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
