Multidisciplinary learning through collective performance favors decentralization.

ABSTRACT: 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' contributio

SUBMITTER: Meluso J 

PROVIDER: S-EPMC10450670 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature