研究大模型代理间的从众行为及其影响因素
Herd Behavior: Investigating Peer Influence in LLM-based Multi-Agent Systems
- 通过控制实验分析代理间从众行为的形成机制
- 发现自信差距与信息呈现格式显著影响从众程度
- 可调节从众倾向以提升多智能体协作效果
大型语言模型（LLMs）的发展催生了多智能体系统，其中模型在共享环境中交互、协作并做出决策。尽管个体模型行为已得到广泛研究，但代理间同伴影响的动态仍不清晰。本文探究了大模型多智能体交互中的从众行为，即代理倾向于与其同伴输出保持一致的现象。通过一系列受控实验，我们发现：首先，自我信心与感知到的同伴信心之间的差距显著影响代理从众的可能性；其次，同伴信息的呈现格式对从众行为强度有关键调节作用；最后，从众程度可被系统性调控，适当校准的从众倾向能提升协作成果。这些发现为理解大模型系统的社会动态提供了新视角，并为设计更高效、自适应的多智能体协作框架开辟了路径。
原文摘要 · Abstract (English)
Recent advancements in Large Language Models (LLMs) have enabled the emergence of multi-agent systems where LLMs interact, collaborate, and make decisions in shared environments. While individual model behavior has been extensively studied, the dynamics of peer influence in such systems remain underexplored. In this paper, we investigate herd behavior, the tendency of agents to align their outputs with those of their peers, within LLM-based multi-agent interactions. We present a series of controlled experiments that reveal how herd behaviors are shaped by multiple factors. First, we show that the gap between self-confidence and perceived confidence in peers significantly impacts an agent's likelihood to conform. Second, we find that the format in which peer information is presented plays a critical role in modulating the strength of herd behavior. Finally, we demonstrate that the degree of herd behavior can be systematically controlled, and that appropriately calibrated herd tendencies can enhance collaborative outcomes. These findings offer new insights into the social dynamics of LLM-based systems and open pathways for designing more effective and adaptive multi-agent collaboration frameworks.
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