Learning by migrating: A computational study of diversity and team-level decision-making
Editor: Anja Maier, Stanko Škec, Harrison Kim, Michael Kokkolaras, Josef Oehmen, Georges Fadel, Filippo Salustri, Mike Van der Loos
Author: Thomas, Russell; Gero, John
Institution: 1: George Mason University, United States of America; 2: UNC Charlotte, United States of America
Section: Human Behaviour in Design
How does previous experience and learning influence a team’s ability to successfully agree on a system architecture, team roles and responsibilities, and design method? Migration of team members leads to diversity in past experiences and beliefs, which might have a positive or negative affect on team decision-making. Using computational modeling of self-managed teams across multiple project life cycles, we perform controlled experiments to evaluate performance and decision-making patterns of migrating vs. non-migrating teams. We find that there is no difference in mean performance, indicating that neither approach is intrinsically better. However, statistical tests of paired trials shows a meaningful an advantage for migrating (diverse) teams. Examining patterns of decision-making over time reveal that migrating (diverse) teams explore a wider range of team-level decisions, which makes them more adaptable in specific circumstances.