QUANTA: Quantitative Network-Based Models of Adaptive Team Behavior
Abstract
Major Goals: 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. Our research will: -Enable leaders to identify a broader set of strategies for creating successful teams, based on the characteristics of individual members, network structures, and emergent groups; -Identify processes that enable the emergence of optimal interaction patterns for a given set of individuals or teams, and improve team performance over task sequences; -Reveal scalable team structures to develop theories and tools for the decomposition of complex tasks; -Apply machine learning and optimization to identify new approaches that teams and organizations can use to adapt to new and unfamiliar environments.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 14, 2022
- Accession Number
- AD1229257
Entities
People
- Ambuj Singh
Organizations
- University of California, Santa Barbara