Herd Behavior: Investigating Peer Influence in LLM-based Multi-Agent Systems

• URL: http://arxiv.org/abs/2505.21588v1
• Date: Tue, 27 May 2025 12:12:56 GMT
• Title: Herd Behavior: Investigating Peer Influence in LLM-based Multi-Agent Systems
• Authors: Young-Min Cho, Sharath Chandra Guntuku, Lyle Ungar,
• Abstract summary: We investigate the dynamics of peer influence in multi-agent systems based on Large Language Models (LLMs)<n>We show that the gap between self-confidence and perceived confidence in peers significantly impacts an agent's likelihood to conform.<n>We find that the format in which peer information is presented plays a critical role in modulating the strength of herd behavior.
• Score: 7.140644659869317
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

Related papers

• Evaluating LLM Agent Collusion in Double Auctions [1.3194391758295114]
  We study the behavior of large language models (LLMs) acting as sellers in simulated double auction markets.<n>We find that direct seller communication increases collusive tendencies, the propensity to collude varies across models, and environmental pressures, such as oversight and urgency from authority figures, influence collusive behavior.
  arXiv  Detail & Related papers  (2025-07-02T07:06:49Z)
• Assessing Collective Reasoning in Multi-Agent LLMs via Hidden Profile Tasks [5.120446836495469]
  We introduce the Hidden Profile paradigm from social psychology as a diagnostic testbed for multi-agent LLM systems.<n>By distributing critical information asymmetrically across agents, the paradigm reveals how inter-agent dynamics support or hinder collective reasoning.<n>We find that while cooperative agents are prone to over-coordination in collective settings, increased contradiction impairs group convergence.
  arXiv  Detail & Related papers  (2025-05-15T19:22:54Z)
• Do as We Do, Not as You Think: the Conformity of Large Language Models [46.23852835759767]
  This paper presents a study on conformity in large language models (LLMs) driven collaborative AI systems.<n>We focus on three aspects: the existence of conformity, the factors influencing conformity, and potential mitigation strategies.<n>Our analysis delves into factors influencing conformity, including interaction time and majority size, and examines how the subject agent rationalizes its conforming behavior.
  arXiv  Detail & Related papers  (2025-01-23T04:50:03Z)
• Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs [64.9693406713216]
  Internal mechanisms that contribute to the effectiveness of RAG systems remain underexplored. Our experiments reveal that several core groups of experts are primarily responsible for RAG-related behaviors. We propose several strategies to enhance RAG's efficiency and effectiveness through expert activation.
  arXiv  Detail & Related papers  (2024-10-20T16:08:54Z)
• PersLLM: A Personified Training Approach for Large Language Models [66.16513246245401]
  We propose PersLLM, a framework for better data construction and model tuning.<n>For insufficient data usage, we incorporate strategies such as Chain-of-Thought prompting and anti-induction.<n>For rigid behavior patterns, we design the tuning process and introduce automated DPO to enhance the specificity and dynamism of the models' personalities.
  arXiv  Detail & Related papers  (2024-07-17T08:13:22Z)
• Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning [51.52387511006586]
  We propose Hierarchical Opponent modeling and Planning (HOP), a novel multi-agent decision-making algorithm. HOP is hierarchically composed of two modules: an opponent modeling module that infers others' goals and learns corresponding goal-conditioned policies. HOP exhibits superior few-shot adaptation capabilities when interacting with various unseen agents, and excels in self-play scenarios.
  arXiv  Detail & Related papers  (2024-06-12T08:48:06Z)
• AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents [65.16893197330589]
  Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-character social interactions has yet to be fully explored. We introduce the Multi-Agent Interaction Evaluation Framework (AntEval), encompassing a novel interaction framework and evaluation methods.
  arXiv  Detail & Related papers  (2024-01-12T11:18:00Z)
• Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning [18.054709749075194]
  We propose a novel MARL algorithm named Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning (SCIC) Our approach aims to detect inter-agent causal influences in specific situations based on the criterion using causal intervention and conditional mutual information. The resulting update links coordinated exploration and intrinsic reward distribution, which enhance overall collaboration and performance.
  arXiv  Detail & Related papers  (2023-12-15T05:09:32Z)
• DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning [84.22561239481901]
  We propose a new approach that enables agents to learn whether their behaviors should be consistent with that of other agents. We evaluate DCIR in multiple environments including Multi-agent Particle, Google Research Football and StarCraft II Micromanagement.
  arXiv  Detail & Related papers  (2023-12-10T06:03:57Z)
• The Machine Psychology of Cooperation: Can GPT models operationalise prompts for altruism, cooperation, competitiveness and selfishness in economic games? [0.0]
  We investigated the capability of the GPT-3.5 large language model (LLM) to operationalize natural language descriptions of cooperative, competitive, altruistic, and self-interested behavior. We used a prompt to describe the task environment using a similar protocol to that used in experimental psychology studies with human subjects. Our results provide evidence that LLMs can, to some extent, translate natural language descriptions of different cooperative stances into corresponding descriptions of appropriate task behaviour.
  arXiv  Detail & Related papers  (2023-05-13T17:23:16Z)
• Multi-Agent Imitation Learning with Copulas [102.27052968901894]
  Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions. In this paper, we propose to use copula, a powerful statistical tool for capturing dependence among random variables, to explicitly model the correlation and coordination in multi-agent systems. Our proposed model is able to separately learn marginals that capture the local behavioral patterns of each individual agent, as well as a copula function that solely and fully captures the dependence structure among agents.
  arXiv  Detail & Related papers  (2021-07-10T03:49:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.