Balancing Humans and Machines: A Study on Integration Scale and Its Impact on Collaborative Performance

In the evolving artificial intelligence domain, hybrid human-machine systems have emerged as a transformative research area. While many studies have concentrated on individual human-machine interactions, there is a lack of focus on multi-human and multi-machine dynamics. This paper delves into these nuances by introducing a novel statistical framework that discerns integration accuracy in terms of precision and diversity. Empirical studies reveal that performance surges consistently with scale, either in human or machine settings. However, hybrid systems present complexities. Their performance is intricately tied to the human-to-machine ratio. Interestingly, as the scale expands, integration performance growth isn't limitless. It reaches a threshold influenced by model diversity. This introduces a pivotal `knee point', signifying the optimal balance between performance and scale. This knowledge is vital for resource allocation in practical applications. Grounded in rigorous evaluations using public datasets, our findings emphasize the framework's robustness in refining integrated systems.

Authors

• Rui Zou Central China Normal University
• Sannyuya Liu Central China Normal University
• Yawei Luo Zhejiang University
• Yaqi Liu Beijing Electronic Science and Technology Institute
• Jintian Feng Central China Normal University
• Mengqi Wei Central China Normal University
• Jianwen Sun Central China Normal University

Keywords

• HAI: Teamwork, Team formation
• MAS: Coordination and Collaboration
• MAS: Multiagent Learning
• MAS: Multiagent Systems under Uncertainty
• MAS: Teamwork