A Roadmap for Addressing Critical Challenges in Human-Machine Social Systems

This is Part 12 of a 12-part series based on the research paper “Human-Machine Social Systems.” Table of Links Abstract and Introduction Human-machine interactions Collective outcomes Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Discussion Implications for research Implications for design Implications for policy Conclusion, Acknowledgments, References, and Competing interests Conclusion This survey synthesizes relatively disparate literature based on agent-based models, controlled experiments, online field interventions, and observational analyses from human-computer interaction, robotics, web science, financial economics, and computational social science under a common theoretical framework: human-machine social systems. We identify common dynamics and patterns that emerge from the interactions of humans and autonomous machines regardless of the specific context, as well as peculiarities and unique problems that concrete techno-organizational and sociocultural environments generate. Our utmost ambition is to stimulate cumulative empirically driven and mechanism-focused sociological research in the emerging, fast-evolving field of human-AI science. At stake are new and urgent social challenges such as online misinformation, market flash crashes, cybersecurity, labor market resilience, and road safety. With increasing social connectivity and accelerating developments in AI, understanding the complex interactions between humans and autonomous machines is a challenging undertaking, but one that is crucially important for a better human future. Acknowledgments MT acknowledges the generous support of the Santa Fe Institute during the period when the research was conducted. 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Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This paper is available on arxiv under CC BY 4.0 DEED license. This is Part 12 of a 12-part series based on the research paper “Human-Machine Social Systems.” This is Part 12 of a 12-part series based on the research paper “Human-Machine Social Systems.” “ Human-Machine Social Systems .” Human-Machine Social Systems Table of Links Abstract and Introduction Abstract and Introduction Human-machine interactions Human-machine interactions Collective outcomes Collective outcomes Box 1: Competition in high-frequency trading markets Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Box 4: Cooperation and contagion on Reddit Discussion Discussion Implications for research Implications for research Implications for design Implications for design Implications for policy Implications for policy Conclusion, Acknowledgments, References, and Competing interests Conclusion, Acknowledgments, References, and Competing interests Conclusion This survey synthesizes relatively disparate literature based on agent-based models, controlled experiments, online field interventions, and observational analyses from human-computer interaction, robotics, web science, financial economics, and computational social science under a common theoretical framework: human-machine social systems. We identify common dynamics and patterns that emerge from the interactions of humans and autonomous machines regardless of the specific context, as well as peculiarities and unique problems that concrete techno-organizational and sociocultural environments generate. Our utmost ambition is to stimulate cumulative empirically driven and mechanism-focused sociological research in the emerging, fast-evolving field of human-AI science. At stake are new and urgent social challenges such as online misinformation, market flash crashes, cybersecurity, labor market resilience, and road safety. With increasing social connectivity and accelerating developments in AI, understanding the complex interactions between humans and autonomous machines is a challenging undertaking, but one that is crucially important for a better human future. Acknowledgments MT acknowledges the generous support of the Santa Fe Institute during the period when the research was conducted. 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Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. Authors: Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv