通过集体表现的多学科学习有利于去中心化
John Meluso1, Laurent Hébert-Dufresne1,2
1Vermont Complex Systems Center, College of Engineering & Mathematical Sciences, University of Vermont, Burlington, VT 05405.
概括
多学科团队可以使用集体评估从网络邻居那里学习. 密集的网络阻碍了勘探,但有助于开发,而去中心化通常会提高团队的表现.
科学领域:
- 组织行为 组织行为
- 团队动力学 团队动力学
- 学习科学 学习科学
背景情况:
- 传统的团队学习模式假定并发学习或解决方案共享,这在多学科环境中往往是不可行的.
- 在多学科团队中,不同的角色掩盖了个人的贡献,使绩效归因和从合作者身上学习复杂化.
- 调解文物对于在复杂的团队结构中实现有效学习至关重要.
研究的目的:
- 研究团队网络结构如何影响多学科团队的学习和表现.
- 确定个人可以通过这种机制来归因相互依赖的任务中的表现的机制.
- 为优化在专业环境中的团队学习提供设计原则.
主要方法:
- 模拟的团队环境,具有不同的网络结构和任务类型.
- 分析不同网络条件下的个人和集体绩效指标.
- 检查受探索和开发搜索策略影响的学习动态.
主要成果:
- 团队网络结构影响业绩,特别是当贡献按网络属性加权时.
- 密集的网络对勘探 (创新) 期间的业绩有负面影响,但对开发 (精炼) 有积极影响.
- 分散的团队结构在各种任务中始终提高了绩效.
结论:
- 调解文物,如集体绩效评估,使多学科团队中的网络邻居能够有效地学习.
- 最佳的团队网络设计取决于任务对探索与利用的重视.
- 分权化是改善团队在复杂,相互依存的工作环境中的表现的强有力的策略.
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