Self-Organizing Networks, Social Complexity, and Disease Dynamics
Nina H. Fefferman
March 4th, 2010
JEC 3117, 4:00 p.m. to 5:00 p.m.
Refreshments at 3:30 p.m.
Computational simulation can provide powerful insights into biological questions. We'll discuss a model system of social behavior and build from this very simple model a framework for the study of evolution
of social complexity. We'll extend these results to show how continued social dynamics within an otherwise constant population can affect things
like information flows and disease burden. Lastly, we'll talk about some methodological best practices for the study of stochastic systems.
Prof. Fefferman is a mathematical biologist interested in the efficiency and
robustness of self-organizing systems, especially in the fields of
evolutionary sociobiology, epidemiology, and biosecurity. She is a member
of the Center for Discrete Math and Theoretical Computer Science (DIMACS),
a faculty member of Rutgers University Department of Ecology and Evolution,
and the co-Director of the Tufts Medical School Initiative for the
Forecasting and Modeling of Infectious Diseases.
Last updated: March 1, 2010