Building upon our recent, widely-cited work on collective intelligence, we view teams as semantically-labeled, multilevel networks with attributes on nodes, edges, and subgroups. Through experiments for validation, we develop models of team performance: both statistical models using networks and fine-grain models of information flows. We then examine the relationship between influence networks and collective intelligence on task performance, as we consider the robustness and optimization of teams.

Thrust 1 Tasks:

• Develop conceptual models for teams and tasks
  Lead: de la Haye

• Develop statistical and content-based models
  Lead: Singh
  • Discovering discriminative characteristics for team performance
  • Statistical models of performance
  • Assured prediction
  • Relating information flow to team performance

• Examine relationship of group performance and influence networks
  Lead: Friedkin
  • Combinatorial collective intelligence
  • Combinatorial influence networks
  • Group performance on task types
• Develop models for the robustness and optimization of teams
  Lead: Malone