Multidisciplinary University Research Initiative (MURI) on the Network Science of Teams
(Funded by the Army)

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. This project aims 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. Besides UCSB, our team includes leading scientists from MIT, USC, UIUC, and Northwestern.

Discovering Network Processes in Multilayer Time-evolving Networks under Incomplete Information
(Funded by the Army)

The Network Science Collaborative Technology Alliance (NS CTA) is a collaborative research alliance between the US Army Research Laboratory (ARL), other government researchers, and a consortium of university researchers. Its broad objective is to perform foundational cross-cutting research on network science utilizing social/cognitive networks, information networks, and communication networks. Our specific goal is to model and predict network evolution and the evolution of dynamic processes occurring within these networks utilizing partial observations. Questions we seek to answer include: Can we uncover hidden network layers from observed dynamic processes occurring on time-varying multilayer networks? How and where should we measure such networks? Can we use this to predict the process dynamics and detect anomalies? Can we predict network dynamics and dynamic processes on time-varying networks in the presence of missing temporal and/or structural data? How to model mixture of network processes at different temporal and structural scales? How much of missing data can be tolerated? What is the effect of missing data on a multilayered network?