Multidisciplinary learning through collective performance favors decentralization

TL;DR

This study demonstrates that collective performance assessments enable multidisciplinary team members to learn effectively from network neighbors, and that decentralization generally enhances team performance across diverse tasks.

Contribution

It introduces a novel learning mechanism through mediating artifacts in multidisciplinary teams and analyzes how network structure impacts performance during exploration and exploitation.

Findings

Network structure influences team performance on different tasks.

Dense networks slightly hinder exploration but aid exploitation.

Decentralization consistently improves team performance.

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' 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.