The recent convergence of research in social and psychological sciences, dynamic and quantitative modeling, and network science has led to a re-examination of collective team behavior from a quantitative and systems-oriented viewpoint. Teams cannot be understood fully by studying their components (members) in isolation: team performance is not simply a sum of individual performances; and a diversity of opinions among members leads to better group outcomes. However, it is not yet understood how patterns of interactions and relationships among team members (i.e. team networks) impact performance. Understanding these patterns is critical, as the resolution of complex issues requires deliberative within-group interaction processes in which alternative courses of action are surfaced, evaluated, and acted upon. The goals of this project are to build quantifiable informative models of teams as dynamical systems interacting over multiple networks; analyze dynamic team behavior by developing rigorous models that relate interaction patterns and network evolution to task performance; and break new ground in team design by scaling teams to solve complex tasks (i.e. teams of teams), and advancing social science theories of team performance.