Thrust 2: Analysis and models of dynamic team behavior

We  examine the performance of teams on a sequence of tasks. The first part of this research examines the changes in social influence networks resulting from repeated tasks. The second part explores team adaptivity using methods from computational learning and data-driven optimization.

Thrust 2 Tasks:

  • Analyzing the interpersonal influences on task sequences and evolving networks
    Lead: Friedkin
    • Group performance along a task sequence
    • Task types and group performance along a task sequence
    • Learned adaptations of influence networks along a task sequence
  • Learning and optimizing dynamic team behavior
    Lead: Bullo
    • Learning and optimization along task sequences
    • Learning for dynamic teams along task sequences

 

Related Publications:

  1. Romero, D., Uzzi, B., Kleinberg, J. 2016. Social Networks under Stress. 25th International World Wide Web Conference (WWW).
  2. Mei, W., Friedkin, N. E, Bullo, F. 2016. Dynamic Models of Appraisal Networks Explaining Collective Learning. IEEE Conf. on Decision and Control.
  3. Friedkin, N. E., Proskurnikov, A. V., Tempo, R., Parsegov, S. E. 2016. Network science on belief system dynamics under logic constraints. Science. 354(6310):6.
  4. Friedkin, N. E, Jia, P., Bullo, F. 2016. A Theory of the Evolution of Social Power: Natural Trajectories of Interpersonal Influence Systems along Issue Sequences. Sociological Science. 3:28.
  5. Jia P., Friedkin, N. E, Bullo, F. 2016. The Coevolution of Appraisal and Influence Networks leads to Structural Balance. IEEE Transactions on Network Science and Engineering.
  6. Uzzi, B., Dunlap, S. 2016. Make Your Enemies Your Allies: Three steps to reversing a rivalry at work. Harvard Business Review.
  7. Amelkin, V., Bogdanov, P., Singh, A. K. 2017. A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks. IEEE International Conference on Data Engineering (ICDE).
  8. Whalen, R., Mukherjee, S., Uzzi, B. 2017. Legal Decision Evolution: Temporal Patterns of Precedent Citation & Judicial Opinion Impact. Elon Law Journal.
  9. Mukherjee, S., Romero, D., Jones, B., Uzzi, B. 2017. The Nearly Universal Link between the Age of Past Knowledge and Tomorrow’s Breakthroughs in Science and Technology. Science Advances.
  10. Mei, W., Mohagheghi, S., Zampieri, S., Bullo, F. 2017. On the dynamics of deterministic epidemic propagation over networks. Annual Reviews in Control. 44:116-128.
  11. Jia, P., Friedkin, N. E, Bullo, F. 2017. Opinion Dynamics and Social Power Evolution over Reducible Influence Networks. SIAM Journal on Control and Optimization. 55(2):21.
  12. Awad, E., Bonnefon, J., Caminada, M., Malone, T. W., Rahwan, I. 2017. Experimental assessment of aggregation principles in argumentation-enabled collective intelligence. ACM Transactions on Internet Technology. 17(3).
  13. Amelkin, V., Bullo, F., Singh, A. K. 2017. Polar Opinion Dynamics in Social Networks. IEEE Transactions on Automatic Control (TAC). 62(11):5650-5665.
  14. Mei, W., Bullo, F. 2017. Competitive Propagation: Models, Asymptotic Behavior and Quality-Seeding Games. IEEE Transactions on Network Science and Engineering. 4(2):7.
  15. Friedkin, N. E., Bullo, F. 2017. How Truth Wins in Opinion Dynamics along Issue Sequences. Proceedings of the National Academy of Science (PNAS). 114(43):11380-11385.
  16. Parsegov, S. E., Proskurnikov, A. V., Tempo, R., Friedkin, N. E. 2017. Novel Multidimensional Models of Opinion Dynamics in Social Networks. IEEE Transactions on Automatic Control. 62(5).
  17. Uzzi, B., Jones, B. 2017. How to Find the Hotspots for Your Next Breakthrough Idea. Harvard Business Review.
  18. Bullo, F. 2018. Lectures on Network Systems.
  19. Romero, D., Uzzi, B., Kleinberg, J. 2018. Social Networks under stress: Specialized Team Roles and their Communication Structure. To appear in Transactions on the Web (TWEB).
  20. Rawlings, C., Friedkin, N. E. 2018. The Structural Balance Theory of Sentiment Relations: Elaboration and Test. To appear in the American Journal of Sociology.
  21. Mei, W., Friedkin, N.E, Lewis, K., Bullo, F. 2018. Dynamic models of appraisal networks explaining collective learning. To appear in IEEE Transactions on Automatic Control.
  22. Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D., Milojević, S., Petersen, A. M., Radicchi, F., Sinatra, R., Uzzi, B., Vespignani, A., Waltman, L., Wang, D., Barabási, A-L. 2018. Science of Science. To appear in Science.

 

Affiliated Faculty

Postdoctoral Fellow
University of Pennsylvania

Co-PI
Center for Control, Dynamical Systems and Computation
University of California, Santa Barbara
Research: Control theory, Multi-agent networks, Robotic coordination, Power systems

Department of Electrical and Computer Engineering
University of California, Santa Barbara

Co-PI
Keck School of Medicine
University of Southern California
Research: Social networks, systems science, social psychology, health promotion and disease prevention, health disparities

Co-PI
Department of Sociology
University of California, Santa Barbara
Research: Social psychology, social networks, and mathematical sociology

Co-PI
Center for Collective Intelligence
Massachusetts Institute of Technology
Research: Organizational design, information technology, and leadership.
 

Postdoctoral Researcher
ETH Zurich

Department of Electrical and Computer Engineering
University of California, Santa Barbara

Postdoctoral Fellow
Harvard Business School

Co-PI
Kellogg School of Management
Northwestern University

Research Assistant Professor
Kellogg School of Management
Northwestern University

Department of Computer Science
University of California, Santa Barbara