Center for Humans and Machines, Max Planck Institute for Human Development, Lentzeallee 94, Berlin, 14195, Germany.
Sci Rep. 2024 Jan 30;14(1):2491. doi: 10.1038/s41598-024-52837-3.
It is widely believed that diversity arising from different skills enhances the performance of teams, and in particular, their ability to learn and innovate. However, diversity has also been associated with negative effects on the communication and coordination within collectives. Yet, despite the importance of diversity as a concept, we still lack a mechanistic understanding of how its impact is shaped by the underlying social network. To fill this gap, we model skill diversity within a simple model of collective learning and show that its effect on collective performance differs depending on the complexity of the task and the network density. In particular, we find that diversity consistently impairs performance in simple tasks. In contrast, in complex tasks, link density modifies the effect of diversity: while homogeneous populations outperform diverse ones in sparse networks, the opposite is true in dense networks, where diversity boosts collective performance. Our findings also provide insight on how to forge teams in an increasingly interconnected world: the more we are connected, the more we can benefit from diversity to solve complex problems.
人们普遍认为，不同技能带来的多样性可以提高团队的绩效，尤其是提高团队的学习和创新能力。然而，多样性也会对集体内部的沟通和协调产生负面影响。尽管多样性作为一个概念很重要，但我们仍然缺乏对其影响如何被底层社会网络塑造的机制理解。为了填补这一空白，我们在一个简单的集体学习模型中对技能多样性进行建模，并表明其对集体绩效的影响取决于任务的复杂性和网络密度。具体来说，我们发现多样性在简单任务中始终会损害绩效。相比之下，在复杂任务中，链接密度会改变多样性的影响：在稀疏网络中，同质群体的表现优于异质群体，而在密集网络中则相反，异质群体可以提高集体绩效。我们的研究结果还为如何在一个日益互联的世界中组建团队提供了一些见解：我们的联系越紧密，就越能从多样性中受益，从而解决复杂问题。
