A Practical Approach to Short-Term Analysis and Estimation of Social Learning Models

Models of social learning feature either binary signals or abstract signal structures often deprived of micro-foundations. Both models are limited when analyzing interim results or performing empirical analysis. We present a method of generating signal structures which are richer than the binary model, yet are tractable enough to perform simulations and empirical analysis. We demonstrate the method's usability by revisiting two classical papers: (1) we discuss the economic significance of unbounded signals; (2) we use existing experimental data to perform econometric analysis. Additionally, we provide a necessary and sufficient condition for the occurrence of action cascades