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LLM agents exhibit emotional contagion in crowd simulations, study finds

By PulseAugur Editorial · [2 sources] ·

A new paper explores how large language model (LLM) agents can exhibit emergent emotional contagion within simulated crowds. The study, which uses a multi-agent crowd simulation, found that agents perceive, appraise, and express emotions based on their prompted personality, memory, and context. The research demonstrates that emotional states can spread through these agents, with the dynamics influenced by factors like spatial layout, temporal progression, and the agents' personality profiles. The paper also notes that the observed emotional contagion is dependent on the specific LLM backend used for appraisal. AI

IMPACT This research could inform the development of more sophisticated and nuanced AI agents capable of complex social interactions.

RANK_REASON The cluster contains an academic paper detailing a novel research finding.

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLM agents exhibit emotional contagion in crowd simulations, study finds

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Funda Durupinar ·

    How Affect Propagates among LLM Agents: Emergent Emotional Contagion in Crowd Simulation

    arXiv:2607.25140v1 Announce Type: new Abstract: This paper studies the behavior of language models in a multi-agent crowd simulation, focusing on how affect propagates among agents that perceive and appraise one another. Each agent perceives its neighbors through visual, auditory…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Funda Durupinar ·

    How Affect Propagates among LLM Agents: Emergent Emotional Contagion in Crowd Simulation

    This paper studies the behavior of language models in a multi-agent crowd simulation, focusing on how affect propagates among agents that perceive and appraise one another. Each agent perceives its neighbors through visual, auditory, and tactile channels, then appraises these per…

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