  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.
