Social Networks and Collective Action in Large Populations: An Application to the Egyptian Arab Spring

Abstract
We study a dynamic model of collective action where agents are connected by an evolving social network that provides the channel through which they not only interact but also communicate. We consider two alternative scenarios: while in the ``benchmark'' context agents are completely informed, in the alternative one they are not, shaping their beliefs through a combination of local observation and social learning a la DeGroot. We completely characterize the long-run behavior of the system in both cases and show that only in the latter scenario (arguably the most realistic) agents eventually achieve collective action with significant probability and a meaningful time scale. We suggest that this sheds light on the puzzle of how large populations can coordinate on globally desired outcomes. Finally, we illustrate the empirical potential of the model by showing that it can be efficiently estimated for the Egyptian Arab Spring using large-scale cross-sectional data from Twitter.