ArticleJournal of the Royal Society, Interface2024
Prosocial punishment bots breed social punishment in human players.
Article in Journal of the Royal Society, Interface, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- On the optimal integration of intelligent agents into network systems to steer cooperation.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Cooperative bots exhibit nuanced effects on cooperation across strategic frameworks.Journal of the Royal Society, Interface · 2025Article
- Collective effect of self-learning and social learning on language dynamics: a naming game approach in social networks.Journal of the Royal Society, Interface · 2024Article
- Evolutionary mechanisms that promote cooperation may not promote social welfare.Journal of the Royal Society, Interface · 2024Article
- Emergence of cooperation in the one-shot Prisoner's dilemma through Discriminatory and Samaritan AIs.Journal of the Royal Society, Interface · 2024Article
- Prosocial punishment bots breed social punishment in human players.Journal of the Royal Society, Interface · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Prosocial punishment, an important factor to stabilize cooperation in social dilemma games, often faces challenges like second-order free-riders-who cooperate but avoid punishing to save costs-and antisocial punishers, who defect and retaliate against cooperators. Addressing these challenges, our study introduces prosocial punishment bots that consistently cooperate and punish free-riders. Our findings reveal that these bots significantly promote the emergence of prosocial punishment among normal players due to their 'sticky effect'-an unwavering commitment to cooperation and punishment that magnetically attracts their opponents to emulate this strategy. Additionally, we observe that the prevalence of prosocial punishment is greatly enhanced when normal players exhibit a tendency to follow a 'copying the majority' strategy, or when bots are strategically placed in high-degree nodes within scale-free networks. Conversely, bots designed for defection or antisocial punishment diminish overall cooperation levels. This stark contrast underscores the critical role of strategic bot design in enhancing cooperative behaviours in human/AI interactions. Our findings open new avenues in evolutionary game theory, demonstrating the potential of human-machine collaboration in solving the conundrum of punishment.
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