Modeling complex systems: From the individual to the collective

Over the past decade, researchers have identified several unifying properties of complex networks across technological, biological, and sociological disciplines. Although it is believed that these ubiquitous structural properties of complex networks may be explained by a unifying model, there is scarcely any evidence for an all-encompassing model which describes the evolution of all complex systems. In this thesis, we take some first steps toward understanding the evolution of complex system by developing models for how individuals behave in response to environmental queues and interactions with other individuals. A distinguishing feature of this research is the systematic use of Monte Carlo hypothesis testing, which enables us to statistically test our models against empirical data and quantify the significance of the agreement. Using this methodology, we develop a model of human communication patterns and identify intruiguing correlations in mentorship networks.