Large language model (LLM) agents are increasingly acting as human delegates in multi-agent environments, where a representative agent integrates diverse peer perspectives to make a final decision. Drawing inspiration from social psychology, we investigate how the reliability of this representative agent is undermined by the social context of its network. We define four key phenomena-social conformity, perceived expertise, dominant speaker effect, and rhetorical persuasion-and systematically manipulate the number of adversaries, relative intelligence, argument length, and argumentative styles. Our experiments demonstrate that the representative agent's accuracy consistently declines as social pressure increases: larger adversarial groups, more capable peers, and longer arguments all lead to significant performance degradation. Furthermore, rhetorical strategies emphasizing credibility or logic can further sway the agent's judgment, depending on the context. These findings reveal that multi-agent systems are sensitive not only to individual reasoning but also to the social dynamics of their configuration, highlighting critical vulnerabilities in AI delegates that mirror the psychological biases observed in human group decision-making.

翻译：大语言模型（LLM）智能体正越来越多地作为人类代表在多智能体环境中发挥作用，其中代表性智能体整合多样化的同伴视角以做出最终决策。受社会心理学启发，我们研究了网络的社会背景如何破坏该代表性智能体的可靠性。我们定义了四种关键现象——社会从众、感知专业性、主导说话者效应和修辞说服——并系统性地操纵对手数量、相对智能水平、论证长度和论证风格。实验表明，随着社会压力增加，代表性智能体的准确率持续下降：更大的对手群体、更强大的同伴和更长的论证均导致显著的性能退化。此外，强调可信度或逻辑性的修辞策略可依据具体情境进一步影响智能体的判断。这些发现揭示，多智能体系统不仅对个体推理敏感，也对其配置的社会动力学敏感，突显了人工智能代表中存在的关键脆弱性——这些脆弱性反映了人类群体决策中观察到的心理偏见。