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The Dynamics of Collective Creativity in Human-AI Hybrid Societies
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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.
Forward citations
Cited by 2 Pith papers
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Using LLMs to Advance the Cognitive Science of Collectives
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.
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AI Agent Behavioral Science
AI agents should be studied as behavioral entities shaped by context and interaction, not only as trained models.
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