# A new sociology of humans and machines

arXiv: https://arxiv.org/abs/2402.14410 · HTML: https://mlsanity.com/paper/2402.14410/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2024.

## Built on by (most cited first, 30 of 32)

- [Emergent social conventions and collective bias in LLM populations](https://mlsanity.com/paper/2410.08948/) (2024)
- [Building Machines that Learn and Think with People](https://mlsanity.com/paper/2408.03943/) (2024)
- [Group size effects and collective misalignment in LLM multi-agent systems](https://mlsanity.com/paper/2510.22422/) (2025)
- [How Far Are AI Scientists from Changing the World?](https://mlsanity.com/paper/2507.23276/) (2025)
- [AI Agent Behavioral Science](https://mlsanity.com/paper/2506.06366/) (2025)
- [AI shares emotion with humans across languages and cultures](https://mlsanity.com/paper/2506.13978/) (2025)
- [Learning to Tune Like an Expert: Interpretable and Scene-Aware Navigation via MLLM Reasoning and CVAE-Based Adaptation](https://mlsanity.com/paper/2507.11001/) (2025)
- [Harmful Traits of AI Companions](https://mlsanity.com/paper/2511.14972/) (2025)
- [Generative AI collective behavior needs an interactionist paradigm](https://mlsanity.com/paper/2601.10567/) (2026)
- [Gender Dynamics and Homophily in a Social Network of LLM Agents](https://mlsanity.com/paper/2602.02606/) (2026)
- [Recognising, Anticipating, and Mitigating LLM Pollution of Online Behavioural Research](https://mlsanity.com/paper/2508.01390/) (2025)
- [Incentives to Build Houses, Trade Houses, or Trade House Building Skills in Simulated Worlds under Various Governing Systems or Institutions: Comparing Multi-agent Reinforcement Learning to Generative Agent-based Model](https://mlsanity.com/paper/2411.17724/) (2024)
- [Towards Operational Validation of LLM-Agent Social Simulations: A Replicated Study of a Reddit-like Technology Forum](https://mlsanity.com/paper/2508.21740/) (2025)
- [Social Simulations with Large Language Model Risk Utopian Illusion](https://mlsanity.com/paper/2510.21180/) (2025)
- [Beyond Preset Identities: How Agents Form Stances and Boundaries in Generative Societies](https://mlsanity.com/paper/2603.23406/) (2026)
- [An AI Theory of Mind Will Enhance Our Collective Intelligence](https://mlsanity.com/paper/2411.09168/) (2024)
- [A Criminology of Machines](https://mlsanity.com/paper/2511.02895/) (2025)
- [Emergence of Reputation-Based Cooperation in LLM Agents](https://mlsanity.com/paper/2608.04507/) (2026)
- [Indirect tipping: a social attack surface in AI agent populations](https://mlsanity.com/paper/2609.25194/) (2026)
- [Perspectives on How Sociology Can Advance Theorizing about Human-Chatbot Interaction and Developing Chatbots for Social Good](https://mlsanity.com/paper/2507.05030/) (2025)
- [Humans learn to prefer trustworthy AI over human partners](https://mlsanity.com/paper/2507.13524/) (2025)
- [Artificially intelligent agents in the social and behavioral sciences: A history and outlook](https://mlsanity.com/paper/2510.05743/) (2025)
- [AI-Mediated Communication Reshapes Social Structure in Opinion-Diverse Groups](https://mlsanity.com/paper/2510.21984/) (2025)
- [Developing a Grounded View of AI](https://mlsanity.com/paper/2511.14013/) (2025)
- [Bounded Normative Equivalence in Human-AI Cooperation: Group Behaviour, Not Partner Labels, Predicts Cooperation under Anonymous Aggregate Feedback](https://mlsanity.com/paper/2601.20487/) (2026)
- [Large language models for spreading dynamics in complex systems](https://mlsanity.com/paper/2602.08085/) (2026)
- [Collective Opinion Dynamics in Structured LLM Populations](https://mlsanity.com/paper/2604.11312/) (2026)
- [The Organizational Behavior of Agentic AI: Collective Intelligence in Human-Agent Workflows](https://mlsanity.com/paper/2606.30986/) (2026)
- [Learning social norms enhances compatibility in dynamic human-AI coordination](https://mlsanity.com/paper/2607.07021/) (2026)
- [A Diagnostic Framework for AI Agent Behavior](https://mlsanity.com/paper/2607.17149/) (2026)

## Abstract (arXiv)

From fake social media accounts and generative artificial intelligence chatbots to trading algorithms and self-driving vehicles, robots, bots and algorithms are proliferating and permeating our communication channels, social interactions, economic transactions and transportation arteries. Networks of multiple interdependent and interacting humans and intelligent machines constitute complex social systems for which the collective outcomes cannot be deduced from either human or machine behaviour alone. Under this paradigm, we review recent research and identify general dynamics and patterns in situations of competition, coordination, cooperation, contagion and collective decision-making, with context-rich examples from high-frequency trading markets, a social media platform, an open collaboration community and a discussion forum. To ensure more robust and resilient human-machine communities, we require a new sociology of humans and machines. Researchers should study these communities using complex system methods; engineers should explicitly design artificial intelligence for human-machine and machine-machine interactions; and regulators should govern the ecological diversity and social co-development of humans and machines.