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    <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.</abstract>
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%0 Conference Proceedings
%T Social Dynamics as Critical Vulnerabilities that Undermine Objective Decision-Making in LLM Collectives
%A Ko, Changgeon
%A Shin, Jisu
%A Song, Hoyun
%A Lee, Huije
%A Hwang, Eui Jun
%A Park, Jong C.
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F ko-etal-2026-social
%X 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.
%U https://aclanthology.org/2026.acl-long.1756/
%P 37865-37890

Markdown (Informal)

[Social Dynamics as Critical Vulnerabilities that Undermine Objective Decision-Making in LLM Collectives](https://aclanthology.org/2026.acl-long.1756/) (Ko et al., ACL 2026)

• Social Dynamics as Critical Vulnerabilities that Undermine Objective Decision-Making in LLM Collectives (Ko et al., ACL 2026)

ACL

• Changgeon Ko, Jisu Shin, Hoyun Song, Huije Lee, Eui Jun Hwang, and Jong C. Park. 2026. Social Dynamics as Critical Vulnerabilities that Undermine Objective Decision-Making in LLM Collectives. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 37865–37890, San Diego, California, United States. Association for Computational Linguistics.