通过通信模式预测团队表现，早期干预更有效
TRIBE: Predicting Team Performance via Communication Behavior Ensembles
- 基于通信行为聚类出可预测绩效的团队类型
- 任务进行10%时即可准确预测最终表现
- 适用于无特定任务知识的跨领域团队分析
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，且无需依赖特定任务知识。我们提出TRIBE——一种领域无关的方法，揭示传统绩效指标无法捕捉的团队行为特征。研究表明，通信模式可将团队划分为具有绩效预测能力的行为族群，最早在任务完成10%时即具备预测能力，实现及时干预。我们在四个不同数据集上验证了该方法：通信模式能够预测团队表现，预测强度取决于任务结构对行为自由度的允许程度。时间分析显示，AI代理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维持行为灵活性。此外，我们将TRIBE与Llama对比并优化流程，实现显著提速与性能提升。
原文摘要 · Abstract (English)
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.
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