Many models of learning in teams assume that team members can share solutions or learn concurrently. However, these assumptions break down in multidisciplinary teams where team members often complete distinct, interrelated pieces of larger tasks. Such contexts make it difficult for individuals to separate the performance effects of their own actions from the actions of interacting neighbors. In this work, we show that individuals can overcome this challenge by learning from network neighbors through mediating artifacts (like collective performance assessments). When neighbors' actions influence collective outcomes, teams with different networks perform relatively similarly to one another. However, varying a team's network can affect performance on tasks that weight individuals' contributions by network properties. Consequently, when individuals innovate (through ``exploring'' searches), dense networks hurt performance slightly by increasing uncertainty. In contrast, dense networks moderately help performance when individuals refine their work (through ``exploiting'' searches) by efficiently finding local optima. We also find that decentralization improves team performance across a battery of 34 tasks. Our results offer design principles for multidisciplinary teams within which other forms of learning prove more difficult.

翻译：许多团队学习模型假设团队成员能够共享解决方案或并行学习。然而，这些假设在多学科团队中不成立，因为团队成员通常需要完成大型任务中各自不同但相互关联的部分。这种情境使个体难以区分自身行为与交互邻居行为对绩效的影响。在本研究中，我们证明个体可以通过中介物（如集体绩效评估）从网络邻居中学习来克服这一挑战。当邻居行为影响集体成果时，不同网络结构的团队相对表现相似。然而，改变团队网络结构会影响那些按网络属性加权个体贡献的任务绩效。因此，当个体通过"探索性"搜索进行创新时，密集网络会因增加不确定性而略微损害绩效；相反，当个体通过"利用性"搜索完善工作时，密集网络能通过高效寻找局部最优值而适度提升绩效。我们还发现，在34项任务中，去中心化能够提升团队整体绩效。我们的研究结果为多学科团队提供了设计原则，而在这些团队中其他学习形式往往更为困难。