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

Chih-Sheng Hsieh, Lachlan Deer, Michael Koenig and Fernando Vega-Redondo

No 18093, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: 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.

JEL-codes: C72 D71 D72 D74 D83 (search for similar items in EconPapers)
Date: 2023-04
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Related works:
Working Paper: Social Networks and Collective Action in Large Populations: An application to the Egyptian Arab Spring (2024)
Working Paper: Social Networks and Collective Action in Large Populations: An application to the Egyptian Arab Spring (2024)
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