This is Part 8 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 Discussion The algorithms in current human-machine social systems are relatively simple. Few use sophisticated machine learning or AI, and typically, these guide narrow and specific behaviors [218,219]. Except for malicious social bots and customer-service chatbots, most machines do not mimic human qualities. Most machines are superhuman — processing vast data amounts, acting swiftly, and handling tedious tasks— or candidly non-human— resisting peer influence, not reciprocating, and acting randomly. There is a clear distinction between covert and overt bots: covert bots are more problematic than bots declaring their identity and following norms and regulations. The effects of machines on human-machine social systems vary by their number, algorithms, network position, interaction situation, institutional regulations, technological affordances, organizational context, and emerging norms and culture (see Boxes 1-4). Machines alter outcomes through their unique behavior because humans interact differently with them, and because of their indirect effects – machines’ presence changes how humans interact amongst themselves. Machines can be beneficial when they act or steer humans to counteract human weaknesses. For instance, noisy bots can disrupt sub-optimal outcomes and improve coordination, persistently cooperative bots can curb retaliation and maintain cooperation, machines in central roles as arbitrageurs improve price discovery and market quality, and network-engineering bots boost collective welfare via cooperator assortment and defector exclusion. With global information, higher processing power, and instantaneous execution, machines can quickly address external events like vandalism or political and natural crises, ensuring system robustness, resilience, and efficiency. Depending on the situation, machines offer superhuman persistence or randomness, norm-setting rationality, or solution diversity, enhancing human behavior towards better outcomes. However, what helps in one context can hinder in another. Machines’ unintuitive solutions may confuse humans, hindering innovation and technological progress. Humans might not act fast enough to correct machines’ errors, resulting in instabilities and flash failures. Machines are less adaptive than humans to changes, impeding system evolution. Machines can be designed to exploit human weaknesses, triggering cascades that exacerbate polarization, emotional contagion, ideological segregation, and conflict. Machines’ non-human optimization logic, execution speed, and behavioral rigidity can clash with human behavior, pushing interactions toward undesirable outcomes. 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 8 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 8 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 Discussion The algorithms in current human-machine social systems are relatively simple. Few use sophisticated machine learning or AI, and typically, these guide narrow and specific behaviors [218,219]. Except for malicious social bots and customer-service chatbots, most machines do not mimic human qualities. Most machines are superhuman — processing vast data amounts, acting swiftly, and handling tedious tasks— or candidly non-human— resisting peer influence, not reciprocating, and acting randomly. There is a clear distinction between covert and overt bots: covert bots are more problematic than bots declaring their identity and following norms and regulations. The effects of machines on human-machine social systems vary by their number, algorithms, network position, interaction situation, institutional regulations, technological affordances, organizational context, and emerging norms and culture (see Boxes 1-4). Machines alter outcomes through their unique behavior because humans interact differently with them, and because of their indirect effects – machines’ presence changes how humans interact amongst themselves. Machines can be beneficial when they act or steer humans to counteract human weaknesses. For instance, noisy bots can disrupt sub-optimal outcomes and improve coordination, persistently cooperative bots can curb retaliation and maintain cooperation, machines in central roles as arbitrageurs improve price discovery and market quality, and network-engineering bots boost collective welfare via cooperator assortment and defector exclusion. With global information, higher processing power, and instantaneous execution, machines can quickly address external events like vandalism or political and natural crises, ensuring system robustness, resilience, and efficiency. Depending on the situation, machines offer superhuman persistence or randomness, norm-setting rationality, or solution diversity, enhancing human behavior towards better outcomes. However, what helps in one context can hinder in another. Machines’ unintuitive solutions may confuse humans, hindering innovation and technological progress. Humans might not act fast enough to correct machines’ errors, resulting in instabilities and flash failures. Machines are less adaptive than humans to changes, impeding system evolution. Machines can be designed to exploit human weaknesses, triggering cascades that exacerbate polarization, emotional contagion, ideological segregation, and conflict. Machines’ non-human optimization logic, execution speed, and behavioral rigidity can clash with human behavior, pushing interactions toward undesirable outcomes. 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

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