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The Dynamics of Collective Creativity in Human-AI Hybrid Societies

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arxiv 2502.17962 v3 pith:GHPGCD32 submitted 2025-02-25 cs.SI

classification cs.SI
keywords networkshuman-aihybridstoriescollectivecreativityhumansocial
verification ladder T0 review T1 audit T2 compute T3 formal
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Generative AI is shaping an increasingly hybrid society, where ideas and cultural artefacs are created both by humans and intelligent machines. Human creativity is influenced in complex, nonlinear ways by the actions of AI-driven agents within their social networks, but these influences are difficult to measure using traditional methods. This study examines how human-AI interactions shape the evolution of collective creation within large-scale social network experiments, where human and AI participants collectively create stories. Participants (either humans or AI) joined 5x5 grid-based networks in which stories were selected, modified, and shared over many iterations. Initially, AI-only networks showed greater creativity (rated by a separate group of human raters) and collective diversity of stories than human-only and human-AI networks. However, over time, hybrid human-AI networks became more diverse in their creations than AI-only networks. In part, this is because AI agents retained little from the original stories, while human-only networks preserved continuity. These findings highlight the value of experimental social networks in understanding human-AI hybrid societies.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Using LLMs to Advance the Cognitive Science of Collectives

    q-bio.NC 2025-05 conditional novelty 5.0 of 10

    A position paper arguing that LLMs can help cognitive scientists study collective behavior along structural, interactional, and individual complexity axes, with cautions about bias and reproducibility.

  2. AI Agent Behavioral Science

    q-bio.NC 2025-06 conditional novelty 4.0 of 10

    AI agents should be studied as behavioral entities shaped by context and interaction, not only as trained models.

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