Social physics in the age of artificial intelligence

• URL: http://arxiv.org/abs/2603.16900v1
• Date: Wed, 04 Mar 2026 10:41:35 GMT
• Title: Social physics in the age of artificial intelligence
• Abstract summary: Artificial intelligence systems are rapidly becoming more capable, autonomous, and deeply embedded in social life.<n>As humans increasingly interact, cooperate, and compete with AI, we move from purely human societies to hybrid human-AI societies.<n>We argue that these developments open a new research agenda for social physics centred on the co-evolution of humans and machines.
• Score: 38.42884650966473
• License: http://creativecommons.org/licenses/by/4.0/
• 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 behaviour when LLMs frame and participate in decisions. Fourth, studying the evolution of AI delegation: how responsibilities and control are negotiated between humans and machines. Fifth, formalising and comparing the distinct epistemic pipelines that generate human and AI behaviour. Sixth, modelling the co-evolution of AI development and regulation in a strategic ecosystem of firms, users, and institutions. Together, these directions define a programme for using social physics to anticipate and steer the societal impact of advanced AI.

Related papers

• Aligning Generalisation Between Humans and Machines [74.120848518198]
  AI technology can support humans in scientific discovery and forming decisions, but may also disrupt democracies and target individuals.<n>The responsible use of AI and its participation in human-AI teams increasingly shows the need for AI alignment.<n>A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise.
  arXiv  Detail & Related papers  (2024-11-23T18:36:07Z)
• Artificial Theory of Mind and Self-Guided Social Organisation [1.8434042562191815]
  One of the challenges artificial intelligence (AI) faces is how a collection of agents coordinate their behaviour to achieve goals that are not reachable by any single agent. We make the case for collective intelligence in a general setting, drawing on recent work from single neuron complexity in neural networks. We show how our social structures are influenced by our neuro-physiology, our psychology, and our language.
  arXiv  Detail & Related papers  (2024-11-14T04:06:26Z)
• Shifting the Human-AI Relationship: Toward a Dynamic Relational Learning-Partner Model [0.0]
  We advocate for a shift toward viewing AI as a learning partner, akin to a student who learns from interactions with humans. We suggest that a "third mind" emerges through collaborative human-AI relationships.
  arXiv  Detail & Related papers  (2024-10-07T19:19:39Z)
• AI's Social Forcefield: Reshaping Distributed Cognition in Human-AI Teams [6.386909552513031]
  We show that AI actively reshapes the social and cognitive fabric of collaboration.<n>We show that AI participation reorganizes the distributed cognitive architecture of teams.<n>We argue for rethinking AI in teams as a socially influential actor.
  arXiv  Detail & Related papers  (2024-07-03T13:46:00Z)
• Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions [67.60397632819202]
  Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal. We identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI.
  arXiv  Detail & Related papers  (2024-04-17T02:57:42Z)
• AI-enhanced Collective Intelligence [2.5063318977668465]
  Humans and AI possess complementary capabilities that can surpass the collective intelligence of either humans or AI in isolation. This review incorporates perspectives from complex network science to conceptualize a multilayer representation of human-AI collective intelligence. We explore how agents' diversity and interactions influence the system's collective intelligence and analyze real-world instances of AI-enhanced collective intelligence.
  arXiv  Detail & Related papers  (2024-03-15T16:11:15Z)
• Human-AI Coevolution [48.74579595505374]
  Coevolution AI is a process in which humans and AI algorithms continuously influence each other. This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science.
  arXiv  Detail & Related papers  (2023-06-23T18:10:54Z)
• World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges [51.92834011423463]
  We focus on the two concepts of world models and predictive coding. In neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment.
  arXiv  Detail & Related papers  (2023-01-14T06:38:14Z)
• On the Philosophical, Cognitive and Mathematical Foundations of Symbiotic Autonomous Systems (SAS) [87.3520234553785]
  Symbiotic Autonomous Systems (SAS) are advanced intelligent and cognitive systems exhibiting autonomous collective intelligence. This work presents a theoretical framework of SAS underpinned by the latest advances in intelligence, cognition, computer, and system sciences.
  arXiv  Detail & Related papers  (2021-02-11T05:44:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.