Learning Dynamics in Social Networks
This paper proposes a tractable model of Bayesian learning on social networks in which agents choose whether to adopt an innovation. We study the impact of network structure on learning dynamics and diffusion. In tree networks, we provide conditions under which all direct and indirect links contribute to an agent’s learning. Beyond trees, not all links are beneficial: An agent’s learning deteriorates when her neighbors are linked to each other, and when her neighbors learn from herself. These results imply that an agent’s favorite network is the directed star with herself at the center, and that learning is better in “decentralized” networks than “centralized” networks.

Please sign up for meetings below:
docs.google.com/spreadsheets/d/1ywTkCR-sjBInsVwaWA_M7D2iF7QamyiaLY0qYxR1NOM/edit#gid=0
Date: 8 March 2019, 14:15 (Friday, 8th week, Hilary 2019)
Venue: Manor Road Building, Manor Road OX1 3UQ
Venue Details: Seminar Room A
Speaker: Simon Board (UCLA)
Organising department: Department of Economics
Part of: Nuffield Economic Theory Seminar
Booking required?: Not required
Audience: Members of the University only
Editor: Melis Boya