Training collaborators for effective division of labor

By dividing labor, collaboration brings together the complementary strengths of individuals. Importantly, competence is not static; it develops with training, and hence the optimal division of labor is also dynamic. Choosing the training protocol that achieves this optimum is non-trivial, requiring prospection about the long-term trajectory of each individual's competence. 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 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 according to task demands, relative competences, and expectations about how collaborators would develop, and their training decisions supported these anticipated roles. 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.