' actions in terms of network paths and use this formula to characterize the set of networks where naive agents eventually learn …We study a sequential-learning model featuring a network of naive agents with Gaussian information structures. Agents … correctly. This characterization implies that, on all networks where later agents observe more than one neighbor, there exist …

Individuals have a strong tendency to coordinate with all their neighbors on social and economics networks … multiplex network who deal with coordination issues using social learning and payoff-driven dynamics. Agents form their …

We introduce a model of strategic experimentation on social networks where forwardlooking agents learn from their own … own experimentation, so total information decreases with network density; we determine density thresholds below which … agents asymptotically learn the state. In contrast, agent welfare is single-peaked in network density, and achieves a second …

, models of network formation, models for the statistical analysis of social networks, as well as community detection. …In this chapter, I provide an overview of research on social networks and their role in shaping behavior and economic … outcomes. I include discussion of empirical and theoretical analyses of the role of social networks in markets and exchange …

The paper analyzes a dynamic model of rational strategic learning in a network. It complements existing literature by … network. We show that the delay in information transmission caused by incomplete network structures may induce players to … increase own experimentation efforts. As a consequence a complete network can fail to be optimal even if there are no costs for …

social network - can influence our choices. These choices in turn influence the generation of new information and shape … future choices. These considerations motivate a rich research programme on how social networks shape individual and …