Social physics in the age of artificial intelligence
The Anh Han, Joel Z. Leibo, Tom Lenaerts, Iyad Rahwan, Fernando Santos, Matja\v{z} Perc, Valerio Capraro

TL;DR
This paper proposes a new research agenda for social physics focusing on the co-evolution of humans and AI, using models like evolutionary game theory and LLM simulations to understand societal dynamics.
Contribution
It introduces six key research directions for studying the societal impact of AI through a social physics framework, integrating cultural evolution and strategic interactions.
Findings
Identifies six research directions for human-AI societal dynamics.
Highlights the importance of modeling co-evolution of language, behavior, and regulation.
Proposes using social physics to anticipate and influence AI's societal impact.
Abstract
Artificial intelligence (AI) systems are rapidly becoming more capable, autonomous, and deeply embedded in social life. As humans increasingly interact, cooperate, and compete with AI, we move from purely human societies to hybrid human-AI societies whose collective dynamics cannot be captured by existing behavioural models alone. Drawing on evolutionary game theory, cultural evolution, and Large Language Models (LLMs) powered simulations, we argue that these developments open a new research agenda for social physics centred on the co-evolution of humans and machines. We outline six key research directions. First, modelling the evolutionary dynamics of social behaviours (e.g. cooperation, fairness, trust) in hybrid human-AI populations. Second, understanding machine culture: how AI systems generate, mediate, and select cultural traits. Third, analysing the co-evolution of language and…
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Taxonomy
TopicsLanguage and cultural evolution · AI in Service Interactions · Embodied and Extended Cognition
