Human-machine social systems

• URL: http://arxiv.org/abs/2402.14410v2
• Date: Fri, 12 Jul 2024 09:29:36 GMT
• Title: Human-machine social systems
• Authors: Milena Tsvetkova, Taha Yasseri, Niccolo Pescetelli, Tobias Werner,
• Abstract summary: We review recent research from across a range of disciplines and identify general dynamics and patterns in situations of competition, coordination, cooperation, contagion, and collective decision-making. To ensure more robust and resilient human-machine communities, researchers should study them using complex-system methods. Engineers should explicitly design AI for human-machine and machine-machine interactions, and regulators should govern the ecological diversity and social co-evolution of humans and machines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: From fake social media accounts and generative-AI chatbots to financial 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 autonomous machines constitute complex social systems where the collective outcomes cannot be deduced from either human or machine behavior alone. Under this paradigm, we review recent research from across a range of disciplines 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, researchers should study them using complex-system methods, engineers should explicitly design AI for human-machine and machine-machine interactions, and regulators should govern the ecological diversity and social co-evolution of humans and machines.

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