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 is Part 1 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. 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 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. Introduction Robotic trains and cars drive us around, auction bots outbid us for purchases, ChatGPT answers our questions, while social media bots feed us with dubious facts and news. Modern society is a complex human-machine social system in which machines are becoming more numerous, human interactions with machines – more frequent, and machine-machine interactions – more consequential. With recent advances in generative AI models, the existential threat of unexplainable and uncontrollable general AI is looming large again[1,2,3]. However, when they are numerous and interdependent, even simple unintelligent artificial agents can produce unintended and potentially undesirable outcomes. If we want to prevent financial crashes, improve road safety, preserve market competition, increase auction market efficiency, and reduce misinformation, it is no longer sufficient to understand humans – we need to consider machines, understand how humans and machines interact, and how the collective behavior of systems of humans and machines can be predicted. We require a “new sociology of humans and machines.” This narrative review synthesizes research and ideas related to social systems composed of multiple autonomous yet interacting and interdependent humans and machines such as algorithms, bots, and robots (Fig. 1). Similar to the conceptualizations of socio-technical systems [4], actor-network theory 5,6, cyber-physical social systems [7,8] social machines [9,10,11] and human-machine networks12,13, our principal assumption is that humans and machines form a single social system. In contrast, we do not approach machines as a single medium or entity – “technology” – but emphasize their multiplicity, independence, and heterogeneity and their interactions [14]. Thus, we target machines capable of social behavior, namely, a behavior that influences and is influenced by the behavior of other actors (human or machine) in the system. Further, we approach the aggregates as complex social systems where network effects and nonlinear dynamic processes produce collective outcomes that cannot be necessarily deduced from individual preferences and behavior alone [15]. Our conceptualization extends and complements the ecological approach to studying machine behavior[16], the “hybrid collective intelligence perspective” [17], and the budding field of Social AI [18]. We aim to offer a conceptual overview of the topic. Hence, we do not utilize a systematic literature search strategy and instead present selected examples to showcase the new conceptualization[19,20,21]. This paper is available on arxiv under CC BY 4.0 DEED license. 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 is Part 1 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. This is Part 1 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. “ 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 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. Introduction Robotic trains and cars drive us around, auction bots outbid us for purchases, ChatGPT answers our questions, while social media bots feed us with dubious facts and news. Modern society is a complex human-machine social system in which machines are becoming more numerous, human interactions with machines – more frequent, and machine-machine interactions – more consequential. With recent advances in generative AI models, the existential threat of unexplainable and uncontrollable general AI is looming large again[1,2,3]. However, when they are numerous and interdependent, even simple unintelligent artificial agents can produce unintended and potentially undesirable outcomes. If we want to prevent financial crashes, improve road safety, preserve market competition, increase auction market efficiency, and reduce misinformation, it is no longer sufficient to understand humans – we need to consider machines, understand how humans and machines interact, and how the collective behavior of systems of humans and machines can be predicted. We require a “new sociology of humans and machines.” This narrative review synthesizes research and ideas related to social systems composed of multiple autonomous yet interacting and interdependent humans and machines such as algorithms, bots, and robots (Fig. 1). Similar to the conceptualizations of socio-technical systems [4], actor-network theory 5,6, cyber-physical social systems [7,8] social machines [9,10,11] and human-machine networks12,13, our principal assumption is that humans and machines form a single social system. In contrast, we do not approach machines as a single medium or entity – “technology” – but emphasize their multiplicity, independence, and heterogeneity and their interactions [14]. Thus, we target machines capable of social behavior, namely, a behavior that influences and is influenced by the behavior of other actors (human or machine) in the system. Further, we approach the aggregates as complex social systems where network effects and nonlinear dynamic processes produce collective outcomes that cannot be necessarily deduced from individual preferences and behavior alone [15]. Our conceptualization extends and complements the ecological approach to studying machine behavior[16], the “hybrid collective intelligence perspective” [17], and the budding field of Social AI [18]. We aim to offer a conceptual overview of the topic. Hence, we do not utilize a systematic literature search strategy and instead present selected examples to showcase the new conceptualization[19,20,21]. 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

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Reddit Bots Improve Moderation, Encourage Cooperation, and Spread Emotions

The Role of Bots in Enhancing Wikipedia's Cooperation and Stability

Homepage: Ethnology.tech

Too Long; Didn't Read

Get Free Unlimited IP Geo Data With IPinfo Lite

How Human-Machine Networks Shape Collective Behavior in a Hybrid Society

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

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

Machines Drive Efficiency While Exposing Human Weaknesses

AI Design Must Prioritize Adaptation to Human Culture, Trust, and Ethics

AI's Complexity Demands Urgent Study of Trust, Bias, and Behaviors in Diverse Contexts

High-Frequency Trading Algorithms Improve Efficiency but Risk Market Stability

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

Machines Drive Efficiency While Exposing Human Weaknesses

AI Design Must Prioritize Adaptation to Human Culture, Trust, and Ethics

AI's Complexity Demands Urgent Study of Trust, Bias, and Behaviors in Diverse Contexts

High-Frequency Trading Algorithms Improve Efficiency but Risk Market Stability

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps