This study addresses the challenge of forming effective groups in
collaborative problem-solving environments. Recognizing the complexity of human
interactions and the necessity for efficient collaboration, we propose a novel
approach leveraging graph theory and reinforcement learning. Our methodology
involves constructing a graph from a dataset where nodes represent
participants, and edges signify the interactions between them. We conceptualize
each participant as an agent within a reinforcement learning framework, aiming
to learn an optimal graph structure that reflects effective group dynamics.
Clustering techniques are employed to delineate clear group structures based on
the learned graph. Our approach provides theoretical solutions based on
evaluation metrics and graph measurements, offering insights into potential
improvements in group effectiveness and reductions in conflict incidences. This
research contributes to the fields of collaborative work and educational
psychology by presenting a data-driven, analytical approach to group formation.
It has practical implications for organizational team building, classroom
settings, and any collaborative scenario where group dynamics are crucial. The
study opens new avenues for exploring the application of graph theory and
reinforcement learning in social and behavioral sciences, highlighting the
potential for empirical validation in future work.

本研究通过图论和强化学习提出一种新方法，建立一个数据集的图结构，将参与者作为代理人，学习一个优化的图结构来反映有效的群体动态，并利用聚类技术根据学习到的图划分明确的群体结构。该研究对协作工作和教育心理学领域做出贡献，为组织团队建设、课堂环境和其他需要重视群体动态的协作场景提供实际应用指导，并突显了图论和强化学习在社会和行为科学中的潜力和未来工作的经验验证。

图增强强化学习在协作问题解决中的有效小组形成

Graph Enhanced Reinforcement Learning for Effective Group Formation in  Collaborative Problem Solving

Collaborative problem-solving (CPS) is a vital skill used both in the
workplace and in educational environments. CPS is useful in tackling
increasingly complex global, economic, and political issues and is considered a
central 21st century skill. The increasingly connected global community
presents a fruitful opportunity for creative and collaborative problem-solving
interactions and solutions that involve diverse perspectives. Unfortunately,
women and underrepresented minorities (URMs) often face obstacles during
collaborative interactions that hinder their key participation in these
problem-solving conversations. Here, we explored the communication patterns of
minority and non-minority individuals working together in a CPS task. Group
Communication Analysis (GCA), a temporally-sensitive computational linguistic
tool, was used to examine how URM status impacts individuals' sociocognitive
linguistic patterns. Results show differences across racial/ethnic groups in
key sociocognitive features that indicate fruitful collaborative interactions.
We also investigated how the groups' racial/ethnic composition impacts both
individual and group communication patterns. In general, individuals in more
demographically diverse groups displayed more productive communication
behaviors than individuals who were in majority-dominated groups. We discuss
the implications of individual and group diversity on communication patterns
that emerge during CPS and how these patterns can impact collaborative
outcomes.

探究少数群体在协作问题解决任务的沟通模式和影响社会认知语言模式的因素，结果显示了不同族裔群体之间存在着关键的社会认知特征，对于产生富有成果的协作互动至关重要。此外，研究还发现了群体的种族和族裔构成对于个人和群体沟通模式的影响，在比例相对较高的多样化群体中，个人表现出更具生产力的沟通行为。讨论了个体和群体多样性对于协作问题解决中出现的沟通模式的影响，并阐述了这些模式对协作产出的影响。

成千上万的部分之和：少数族裔和多数族裔在协作解决问题的沟通中的作用

Greater than the sum of its parts: The role of minority and majority  status in collaborative problem-solving communication

This study delves into the intricacies of emotional contagion and its impact
on performance within dyadic interactions. Specifically, it focuses on the
context of stereotype-based stress (SBS) during collaborative problem-solving
tasks among female pairs. Through an exploration of emotional contagion, the
research seeks to unveil its underlying mechanisms and effects. Leveraging
EEG-based hyperscanning technology, the study introduces an innovative approach
known as functional Graph Contrastive Learning (fGCL), which extracts
subject-invariant representations of neural activity patterns. These
representations are further subjected to analysis using the Dynamic Graph
Classification (DGC) model, aimed at dissecting the process of emotional
contagion. By scrutinizing brain synchronization and connectivity, the study
reveals the intricate interplay between emotional contagion and cognitive
functioning. The results underscore the substantial role of emotional contagion
in shaping the trajectories of participants' performance during collaborative
tasks in the presence of SBS conditions. Overall, this research contributes
invaluable insights into the neural underpinnings of emotional contagion,
thereby enriching our comprehension of the complexities underlying social
interactions and emotional dynamics.

通过情感传染的研究并结合脑电图扫描技术，该论文揭示了情感传染与认知功能之间复杂的相互作用，特别关注女性对协作解决问题时的刻板印象压力条件下的影响。