Training Collaborators for Effective Division of Labor

Abstract

By dividing labor, collaboration brings together the complementary strengths of individuals. Yet, division of labor benefits collaboration not only by assigning people to tasks that match their competences, but by allowing them to improve “on-the-job” through training and practice. Thus, optimal division of labor requires anticipating how each individual will develop and what roles they will eventually be best suited for. Existing research on collaboration, however, rarely considers this prospective dimension. To address this gap, we studied how humans make training decisions, while manipulating the long-term consequences of those decisions. Across three experiments ($N=$ 600), participants trained two military defense teams to counter two types of attacks (land and air), where the long-term goals and the teams' relative competences varied. Participants made a sequence of training decisions before assigning teams to roles in the final battle. Overall, participants divided labor and trained collaborators by considering how each team's competence would develop, and whether they would eventually meet task demands. These patterns were best captured by a Planning model that trained collaborators based on the expected outcome at the time of deployment. The Planning model outperformed several heuristic alternatives that optimized training based on current competence, learning potential, fairness, or versatility. Together, these findings provide a first step toward understanding how training unlocks one of the central benefits of dividing labor—complementary competences. People do not simply match existing competences to tasks; rather, they actively cultivate individual competences to support future specialization.