Forming Diverse Teams from Sequentially Arriving People
This addresses team formation in collaborative work, such as crowdsourcing and reviewer allocation, but is incremental as it builds on existing submodular optimization methods.
The paper tackles the problem of forming diverse teams from sequentially arriving people by defining a monotone submodular objective to balance diversity and quality, and shows through crowd experiments that the algorithm leads to large gains in team diversity.
Collaborative work often benefits from having teams or organizations with heterogeneous members. In this paper, we present a method to form such diverse teams from people arriving sequentially over time. We define a monotone submodular objective function that combines the diversity and quality of a team and propose an algorithm to maximize the objective while satisfying multiple constraints. This allows us to balance both how diverse the team is and how well it can perform the task at hand. Using crowd experiments, we show that, in practice, the algorithm leads to large gains in team diversity. Using simulations, we show how to quantify the additional cost of forming diverse teams and how to address the problem of simultaneously maximizing diversity for several attributes (e.g., country of origin, gender). Our method has applications in collaborative work ranging from team formation, the assignment of workers to teams in crowdsourcing, and reviewer allocation to journal papers arriving sequentially. Our code is publicly accessible for further research.