Facilitating cooperation in human-agent hybrid populations through autonomous agents

Guo, Hao; Shen, Chen; Hu, Shuyue; Xing, Junliang; Tao, Pin; Shi, Yuanchun; Wang, Zhen

doi:10.1016/j.isci.2023.108179

Cited by 7 publications

(1 citation statement)

References 52 publications

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“…Recent research has sparked interest in using AI-driven agents or bots to study cooperation issues ( 38 , 39 ). They have revealed bots’ ability to address coordination dilemmas ( 21 ) and scaffold cooperation ( 23 ) by integrating bots into network engineering and game interactions ( 22 , 24 ).…”

Section: Introductionmentioning

confidence: 99%

Enhancing social cohesion with cooperative bots in societies of greedy, mobile individuals

Shi,

He,

Shen

et al. 2024

PNAS Nexus

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Addressing collective issues in social development requires a high level of social cohesion, characterized by cooperation and close social connections. However, social cohesion is challenged by selfish, greedy individuals. With the advancement of artificial intelligence (AI), the dynamics of human-machine hybrid interactions introduce new complexities in fostering social cohesion. This study explores the impact of simple bots on social cohesion from the perspective of human-machine hybrid populations within network. By investigating collective self-organizing movement during migration, results indicate that cooperative bots can promote cooperation, facilitate individual aggregation, and thereby enhance social cohesion. The random exploration movement of bots can break the frozen state of greedy population, help to separate defectors in cooperative clusters, and promote the establishment of cooperative clusters. However, the presence of defective bots can weaken social cohesion, underscoring the importance of carefully designing bot behavior. Our research reveals the potential of bots in guiding social self-organization and provides insights for enhancing social cohesion in the era of human-machine interaction within social networks.

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Section: Introductionmentioning

confidence: 99%

Enhancing social cohesion with cooperative bots in societies of greedy, mobile individuals

Shi,

He,

Shen

et al. 2024

PNAS Nexus

Self Cite

View full text Add to dashboard Cite

show abstract

Prosocial dynamics in multiagent systems

Santos

2024

AI Magazine

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Meeting today's major scientific and societal challenges requires understanding dynamics of prosociality in complex adaptive systems. Artificial intelligence (AI) is intimately connected with these challenges, both as an application domain and as a source of new computational techniques: On the one hand, AI suggests new algorithmic recommendations and interaction paradigms, offering novel possibilities to engineer cooperation and alleviate conflict in multiagent (hybrid) systems; on the other hand, new learning algorithms provide improved techniques to simulate sophisticated agents and increasingly realistic environments. In various settings, prosocial actions are socially desirable yet individually costly, thereby introducing a social dilemma of cooperation. How can AI enable cooperation in such domains? How to understand long‐term dynamics in adaptive populations subject to such cooperation dilemmas? How to design cooperation incentives in multiagent learning systems? These are questions that I have been exploring and that I discussed during the New Faculty Highlights program at AAAI 2023. This paper summarizes and extends that talk.

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Engineering Optimal Cooperation Levels with Prosocial Autonomous Agents in Hybrid Human-Agent Populations: An Agent-Based Modeling Approach

Guo,

He,

Shen

et al. 2024

Comput Econ

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Facilitating cooperation in human-agent hybrid populations through autonomous agents

Cited by 7 publications

References 52 publications

Enhancing social cohesion with cooperative bots in societies of greedy, mobile individuals

Enhancing social cohesion with cooperative bots in societies of greedy, mobile individuals

Prosocial dynamics in multiagent systems

Engineering Optimal Cooperation Levels with Prosocial Autonomous Agents in Hybrid Human-Agent Populations: An Agent-Based Modeling Approach

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