Learning Dynamics in Social Networks

Moritz Meyer-Ter-Vehn (University of California - Los Angeles)

November 3, 2020, 11:00–12:30


Room Zoom

Economic Theory Seminar


This paper proposes a tractable model of Bayesian learning on social networks in which agentschoose whether to adopt an innovation. We consider both deterministic and random networks,and study the impact of the network structure on learning dynamics and diffusion. In directed“tree like” networks (e.g. stochastic block networks), all direct and indirect links contribute toan agent’s learning. In comparison, learning and welfare are lower in undirected networks andnetworks with clusters. In a broad set of networks, behavior can be captured by a small numberof differential equations, making the model appropriate for empirical work. ( joint with Simon Board)