MC2 Society as a Complex Network


Humans are strongly interdependent with others for most of their resources, knowledge and emotions. Cases in point are cooperation, conflict, happiness, depression, support, innovation, job-searching, voting, and health affecting behaviors. If social influences were just dyadic (from one person to another), social relations – ties – could be studied with the usual social science methods. However, social outcomes depend non-linearly on the structure of the larger network wherein people are embedded, which is more than a (weighted) sum of their ties. While data on ties can be collected through survey, ethnographic, or other traditional (and advanced) methods, analyzing the overall pattern and its effects on individuals and groups requires complex network analysis. This approach emerged around the turn of the century, when social network analysis became highly interdisciplinary, cross fertilizing theories and methods from the social sciences, behavioral economics (i.e. lab experiments), evolutionary science, biology, computer science and, most importantly, physics. The lectures will be mostly non-technical, however, and aimed at a varied audience.

To get an idea, the sheets of the course I used at the University of Amsterdam in Spring 2016 are at:


This course offers a tour along the most salient network insights into our society, and the most important network concepts and methods used in the field today.


Apicella, C.L. et al (2012) Social networks and cooperation in hunter-gatherers. Nature 481: 497-501.

Marlon Ramos et al. (2015) How does public opinion become extreme? Scientific Reports 5: 10032.

J. Couzin (2009) Friendship as a health factor. Science 323: 454-457.

Course location

Forum 1

Course requirements


Instructor information.