Chapter 3: Machine Learning and the Science of Teams

Teams permeate every aspect of life including business, sports, military, healthcare, and music. Over the past few decades, much has been learned about how individual team characteristics (e.g., knowledge, skills) can affect team processes (e.g., coordination, shared situational awareness) and outcomes (e.g., team performance, innovation). However, researchers have highlighted the need for advances and innovations in methods and analytic tools to help better understand team-level constructs and the dynamic nature of teams. The field of data science is described, in general, and how inductive research and machine learning algorithms, in particular, can contribute to theory and practice in teams. In this chapter, supervised learning, unsupervised learning, and semi-supervised learning are described. Then, a specific algorithm in supervised learning and unsupervised learning is described along with applied examples related to research on teams. Limitations and recommendations are discussed.