This is Part 6 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 Box 3: Cooperation and Coordination on Wikipedia Wikipedia, the largest and most popular free-content online encyclopedia, hosts an ecology of bots that generate articles, fix errors on pages, link to other sites and databases, tag articles in categories, identify vandals, notify users, and so on[205,206,207]. These bots are open-source, documented, approved, registered, and tagged[208,205. They are not sophisticated: most use basic regular expressions or straightforward heuristics, and only some incorporate machine learning techniques. They are significantly less numerous than human editors but complete a disproportionately large volume of all edits [209,36,207]. Compared to human-human interactions, bot-bot interactions are more reciprocal and balanced but do not exhibit status effects [36]. While bots are more likely to be involved in back-and-forth reverts with each other over long periods, these accidental encounters rarely indicate direct opinion conflict but constitute routine productive maintenance work or reflect conflicts existing between their human owners [210]. Human editors interact mainly with policing bots, primarily by criticizing the legitimacy of the norms they enforce, rather than the sanctions themselves, suggesting that editors perceive bots as extensions of their human owners rather than independent agents[211]. Bots have been invaluable to the maintenance and operation of Wikipedia. The diversity of the bot ecology guarantees the system’s robustness and resilience. For instance, during the random outage of the anti-vandalism ClueBot NG, the website eventually caught up, albeit more slowly than usual, thanks to the heterogeneity of the quality control network, comprising instantaneous fully automated robots, rapid tool-assisted humans (cyborgs), humans editing via web browsers, and idiosyncratic batch scripts [212]. Wikipedia demonstrates that successful bot governance and regulation does not have to sacrifice distributed development and diversity. The algorithmic simplicity, independence, and heterogeneity of the machines facilitate the system’s success and resilience overall but may also introduce unexpected complexities and uncertainties in communication at smaller scales[213]. 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 6 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 6 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 Box 3: Cooperation and Coordination on Wikipedia Wikipedia, the largest and most popular free-content online encyclopedia, hosts an ecology of bots that generate articles, fix errors on pages, link to other sites and databases, tag articles in categories, identify vandals, notify users, and so on[205,206,207]. These bots are open-source, documented, approved, registered, and tagged[208,205. They are not sophisticated: most use basic regular expressions or straightforward heuristics, and only some incorporate machine learning techniques. They are significantly less numerous than human editors but complete a disproportionately large volume of all edits [209,36,207]. Compared to human-human interactions, bot-bot interactions are more reciprocal and balanced but do not exhibit status effects [36]. While bots are more likely to be involved in back-and-forth reverts with each other over long periods, these accidental encounters rarely indicate direct opinion conflict but constitute routine productive maintenance work or reflect conflicts existing between their human owners [210]. Human editors interact mainly with policing bots, primarily by criticizing the legitimacy of the norms they enforce, rather than the sanctions themselves, suggesting that editors perceive bots as extensions of their human owners rather than independent agents[211]. Bots have been invaluable to the maintenance and operation of Wikipedia. The diversity of the bot ecology guarantees the system’s robustness and resilience. For instance, during the random outage of the anti-vandalism ClueBot NG, the website eventually caught up, albeit more slowly than usual, thanks to the heterogeneity of the quality control network, comprising instantaneous fully automated robots, rapid tool-assisted humans (cyborgs), humans editing via web browsers, and idiosyncratic batch scripts [212]. Wikipedia demonstrates that successful bot governance and regulation does not have to sacrifice distributed development and diversity. The algorithmic simplicity, independence, and heterogeneity of the machines facilitate the system’s success and resilience overall but may also introduce unexpected complexities and uncertainties in communication at smaller scales[213]. 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

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

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

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Machine Learning Reveals the Faces Behind Viral QAnon Posts

The New Anatomy of Customer Experience - Part 2

Prompt Engineering Will Always Matter (Just Not How You Think)

AI's Paradoxical Path to New Math: To Find Better Answers, It Needs Less Data and a "Dumber" Brain

AI's Trillion-Dollar Infrastructure Bet: What Leaders Need to Know

A Guide on How to Make Your AI Fool-Proof

Machine Learning Reveals the Faces Behind Viral QAnon Posts

The New Anatomy of Customer Experience - Part 2

Prompt Engineering Will Always Matter (Just Not How You Think)

AI's Paradoxical Path to New Math: To Find Better Answers, It Needs Less Data and a "Dumber" Brain

AI's Trillion-Dollar Infrastructure Bet: What Leaders Need to Know

A Guide on How to Make Your AI Fool-Proof

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps