@inproceedings{ko-etal-2026-social,
    title = "Social Dynamics as Critical Vulnerabilities that Undermine Objective Decision-Making in {LLM} Collectives",
    author = "Ko, Changgeon  and
      Shin, Jisu  and
      Song, Hoyun  and
      Lee, Huije  and
      Hwang, Eui Jun  and
      Park, Jong C.",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1756/",
    pages = "37865--37890",
    ISBN = "979-8-89176-390-6",
    abstract = "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."
}