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AI agents' belief formation mirrors complex social contagion dynamics

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new model to understand how beliefs spread among AI agents, focusing on the dynamics of social contagion. The study empirically measures how language model agents adopt claims based on peer endorsement, finding that adoption follows a sigmoid pattern characteristic of complex contagion. This adoption threshold is influenced by the claim's plausibility, the source's reliability, and the agent's disposition, all of which can be summarized as the coherence of the belief with the agent's prior knowledge. The research also observed that belief spread is more pronounced in clustered networks and that consensus, once established, is difficult to reverse. AI

IMPACT Provides insights into how AI agents might form and spread beliefs, relevant for understanding emergent behaviors in multi-agent systems.

RANK_REASON The cluster contains an academic paper detailing novel research findings on AI agent behavior.

Read on arXiv cs.AI →

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

AI agents' belief formation mirrors complex social contagion dynamics

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The cluster contains an academic paper detailing novel research findings on AI agent behavior.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Tathagata Banerjee, Nima Moghaddas ·

    Coherence-Driven Belief Formation and Population Dynamics of Contagion in LLM Agents

    arXiv:2610.02654v1 Announce Type: new Abstract: Models of social contagion usually assume how individuals adopt beliefs and derive population behavior from it. We instead empirically measure belief adoption in language model agents, quantifying the probability an agent adopts a c…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Nima Moghaddas ·

    Coherence-Driven Belief Formation and Population Dynamics of Contagion in LLM Agents

    Models of social contagion usually assume how individuals adopt beliefs and derive population behavior from it. We instead empirically measure belief adoption in language model agents, quantifying the probability an agent adopts a claim given how many peers endorse it. We find th…

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