A new sociology of humans and machines.

2024-02-22

Nature Human Behaviour

AI Summary

This paper addresses the dynamic evolution of emergent socio-technical systems characterized by deep human–machine entanglement, focusing on core mechanisms—competition, cooperation, information diffusion, and collective decision-making—within real-world contexts including high-frequency trading, social media, and open-source communities. Method: We propose “human–machine sociology” as an interdisciplinary paradigm that transcends traditional boundaries between sociology and AI research, emphasizing cross-agent coupling (human–machine and machine–machine) and emergent phenomena. Integrating complex systems science, computational social science, multi-agent modeling, and empirical network analysis, we systematically investigate co-evolutionary dynamics. Contribution/Results: We identify universal dynamical patterns underlying human–machine co-evolution. The framework provides theoretically grounded principles for designing adaptive AI systems, governing human–machine collaboration, and enhancing resilience in technological ecosystems—bridging foundational theory with actionable governance and design pathways.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Understanding complex human-machine social systems dynamics
• Designing AI for human-machine and machine-machine interactions
• Governing ecological diversity in human-machine co-development

Innovation

• Study human-machine communities using complex system methods
• Design AI for human-machine and machine-machine interactions
• Govern ecological diversity and social co-development