, and showed that it gives a lower bound on the time it takes for a certain natural best-reply or learning process operating …

determine the speed of social learning. To this end, we study a variant of the seminal model of DeGroot (1974), according to … learning has a simple analytical characterization in terms of the relative entropy of agents’ signal structures and their … non-trivial implications for the rate of learning. In particular, we show that when the informativeness of different …

We study learning and influence in a setting where agents communicate according to an arbitrary social network and …

concerning the role of those characteristics in determining learning, diffusion, decisions, and resulting behaviors. We also …

We study social learning in a social network setting where agents receive independent noisy signals about the truth …

This paper examines social learning when only one of the two types of decisions is observable. Because agents arrive …, as in the standard model, learning is complete if and only if signals are unbounded. If signals are bounded, cascades may …

This paper studies sequential social learning when people learn about others' actions through coarse signals. Agents … learning occurs, i.e., agents eventually learn the truth and take the correct actions. A necessary condition for adequate … learning is that the state-signals can induce unbounded private likelihoods. When private likelihoods are unbounded, adequate …

We study social learning in a social network setting where agents receive independent noisy signals about the truth …

for behavioral observational learning in social networks. We show that a seller could potentially control the information … available to future customers and induce behavioral observational learning by using an information-revealing pricing strategy … networks. It could prevent the behavioral observational learning that would increase future customers' willingness to pay …

We analyze boundedly rational learning in social networks within binary action environments. We establish how learning … induces learning (i.e., convergence to the optimal action for every agent in every connected network) only in highly … asymmetric environments. In all other environments learning fails in networks with a diameter larger than four. Finally, we …