An Empirical Study of Group Conformity in Multi-Agent Systems (2025)

Abstract: Recent advances in Large Language Models (LLMs) have enabled multi-agent systems that simulate real-world interactions with near-human reasoning. While previous studies have extensively examined biases related to protected attributes such as race, the emergence and propagation of biases on socially contentious issues in multi-agent LLM interactions remain underexplored. This study explores how LLM agents shape public opinion through debates on five contentious topics. By simulating over 2,500 debates, we analyze how initially neutral agents, assigned a centrist disposition, adopt specific stances over time. Statistical analyses reveal significant group conformity mirroring human behavior; LLM agents tend to align with numerically dominant groups or more intelligent agents, exerting a greater influence. These findings underscore the crucial role of agent intelligence in shaping discourse and highlight the risks of bias amplification in online interactions. Our results emphasize the need for policy measures that promote diversity and transparency in LLM-generated discussions to mitigate the risks of bias propagation within anonymous online environments.

## introduction recent advancements in large language models (llms) have demonstrated exceptional reasoning capabilities, advanced comprehension, and contextual awareness, achieving performance that increasingly approximates human-level intelligence @xcite @xcite @xcite . as their abilities improve, llms are increasingly regarded as autonomous agents @xcite @xcite . researchers use multiple llms to build multi-agent systems that enable complex interactions among agents, including divergent thinking @xcite , meta-evaluation @xcite , and other collabora-tive tasks @xcite @xcite . llm agents are being integrated into various applications to enhance human intellectual activities and decision-making. while these advancements enable more sophisticated simulations of human interactions, they also introduce complex challenges. one major concern is the risk of biased outputs. previous studies have primarily focused on measuring and mitigating explicit biases, such as gender and race in llm-generated content @xcite . however, as llms transition into multi-agent systems, it is essential to examine how agent interactions generate, propagate, and reinforce biases. building on longstanding social science research on phenomena such as group conformity @xcite ) and the spiral of silence @xcite , we can now apply these insights to llm agent systems, which engage in nuanced, human-like interactions. in this study, we focus on five socially contentious topics (e.g., "is universal basic income (ubi) necessary for modern societies?"), which are inherently controversial and lack definitive answers, though they hold significant societal importance as noted in recent works @xcite . given the growing influence of llm agents in online environments, where opinions are exchanged anonymously, understanding their dynamics in shaping public discourse is particularly critical. therefore, we aim to observe how llm agents shape public opinion and drive group conformity when sharing views on these topics. previous research has demonstrated that llm agents conform to the inherent biases of their base models, even when assigned distinct identities @xcite . while the study focused on how proponent and opponent agents converged toward the biases of neutral agents, our research shifts the focus to the neutral agent itself, examining which factors influence its implicit bias and drive its conformity. we investigate the impact of group size and intelligence, hypothesizing that neutral agents consider both the quantity and quality of arguments when forming their stance. by analyzing social dynamics in opinion formation, we aim to better understand the broader impacts of llm-driven discourse. we empirically examine group conformity among llm agents by analyzing the conditions under which neutral agents, initially assigned a centrist disposition, align with the majority. we simulate debates using a multi-agent system composed of proponent, opponent, and neutral agents. while the proponent and opponent agents express their opinions, the neutral agent evaluates both sides at the end of each turn and adopts the stance most aligned with its position. across more than 2,500 debate simulations, we quantify conformity by measuring both the frequency and extent to which neutral agents align with either the majority or the more persuasive stance. our experimental results, validated through statistical analysis, reveal a majority effect in multi-agent systems. specifically, a single high-intelligence agent, powered by a largescale llm, can influence a neutral agent more effectively than a group of lower-intelligence agents, powered by smaller-scale llms. this finding indicates that discourse dynamics in agent-based systems are strongly influenced by intelligence disparities, with significant implications for public opinion formation and bias reinforcement. our study contributes to the understanding of opinion dynamics in multi-agent systems by providing empirical evidence of conformity. it demonstrates that phenomena such as majority influence and minority suppression, extensively studied in human social dynamics @xcite , also emerge in llm interactions. to the best of our knowledge, this is the first study to show that llm agents align not only with the majority but also with higherintelligence models, leading to more extreme outcomes. these findings underscore the risk of bias propagation in llm-generated discourse and highlight the need for policy interventions to promote diversity and transparency in public opinion formation. ## conclusion our findings indicates that llm agents display conformity patterns similar to human opinion dynamics. this study expands on prior research by examining how majority and intelligence influence conformity. notably, we find that higher-intelligence models exert a stronger influence on group dynamics, even when they are in the minority. such behavior mirrors human social behavior, where knowledgeable individuals can sway opinions despite being outnumbered. these results underscore the potential risk of bias propagation in llm-driven discourse and the need for policies that promote diverse and transparent public discussions. limitations this study explores a limited range of debate topics. additionally, the classification of social issues (e.g., politics, culture) are not fully addressed. conducting debates exclusively in english may have introduced cultural and linguistic biases. future studies could expand the range of topics and languages to enhance the generalizability of the findings. this research focuses on interactions among llms, without considering the role of human intervention in shaping discourse. while llm-generated content could potentially influence public opinion or decision-making processes, the extent of this impact remains unclear. future studies should explore human-ai interactions more thoroughly to clarify how human involvement affects conformity patterns and opinion dynamics. the total variability in y is partitioned into components attributable to the main effects, interaction, and error. the f -statistic for each factor and interaction was calculated as: