Social networks and collective action in large populations: An application to the Egyptian Arab Spring

We study a dynamic model of collective action in which agents interact and learn through a co-evolving social network. Our approach highlights the importance of communication in this problem and conceives the social network – which is continuously evolving – as the structure through which agents not only interact but also communicate. We consider two alternative scenarios that differ only on how agents form their expectations: while in the “benchmark” context agents are completely informed, in the alternative one their expectations are formed through a combination of local observation and social learning à 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) there is a significant long-run probability that agents eventually achieve collective action within a meaningful time scale. This, we argue, 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 so-called Egyptian Arab Spring using large-scale cross-sectional data from Twitter.