TRIBE：通过通信行为集成预测团队绩效
TRIBE: Predicting Team Performance via Communication Behavior Ensembles
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中文总结 AI 辅助
该研究提出与领域无关的TRIBE方法，通过通信模式可在任务早期预测团队绩效，经四数据集测试、与Llama对比及流程优化，实现性能提升与加速，还揭示了AI智能体对团队行为的影响。
中文摘要 AI 辅助
设计能有效辅助人类团队的自主智能体，关键在于理解团队动态，且通常无需任务特定知识。我们提出TRIBE，这是一种与领域无关的方法，可揭示传统绩效指标无法察觉的团队行为动态。研究表明，早在任务进行到10%时，通信模式就能将团队划分为可预测绩效的行为部落，从而实现及时干预。我们在四个不同数据集上测试TRIBE，结果显示通信模式可预测团队绩效，且预测强度随任务结构允许行为自由度的程度而变化。时间分析表明，AI智能体显著改变团队行为轨迹，而人类顾问则与自然动态保持一致，团队在整个协作过程中保持行为灵活性。此外，我们将TRIBE与Llama进行比较并优化流程，在性能提升的同时实现了显著加速。
英文摘要
Designing autonomous agents that effectively assist human teams hinges on understanding team dynamics, often without task specific knowledge. We present TRIBE, a domain independent approach that reveals team behavioral dynamics invisible to traditional performance metrics. We show that communication patterns can categorize teams into performance predictive behavioral tribes, as early as 10% into the task, enabling timely interventions. We test TRIBE on four diverse datasets and demonstrate that communication patterns predict team performance while the prediction strength varies by the degree a task structure allows for behavioral freedom. Our temporal analysis reveals that AI agents significantly alter team behavioral trajectories while human advisors align with natural dynamics, and that teams maintain behavioral flexibility throughout collaboration. Further, we compare TRIBE to Llama and optimize the pipeline, achieving significant speedup with performance improvement.
