演講者簡介 : Professor Chih-Sheng Hsieh received his Ph.D. in Economics from the Ohio State University. He is currently a Professor at National Taiwan University. His research interests are Applied Econometrics, Labor Economics, Health Economics, and Social Networks.
演講摘要 : We study a dynamic model of collective action – for concreteness, we speak of a riot – in which agents interact through, and learn from, a co-evolving social network. We consider two different scenarios on how agents form their expectations when changing their behavior. In one of them, conceived as a “benchmark”, they are assumed to be completely informed of the prevailing state (action profile and network). Instead, in the alternative scenario, agents are assumed to shape their expectations about the state from a combination of local observation and social learning (modeled à la DeGroot). In both cases we provide a complete characterization of the long-run behavior of the system. While the first assumption of complete information is common, the second one is arguably more realistic. Furthermore, we show that only the latter assumption yields the following twin conclusion: a significant long-run probability of successful collective action and a meaningful time scale of convergence to this state of affairs. This, we argue, suggests a plausible route to understanding what otherwise seems a puzzle, i.e. how do very large populations attain (“coordinate on”) collective action. Finally, we illustrate the empirical potential of the model by showing that it can be efficiently estimated for the so-called Egyptian Arab Spring by relying on large-scale cross sectional data on agents’ choices and their network of interactions. The estimation results are fully in line with the predictions of the theory.
