Oxford Events, the new replacement for OxTalks, will launch on 16th March. From now until the launch of Oxford Events, new events cannot be published or edited on OxTalks while all existing records are migrated to the new platform. The existing OxTalks site will remain available to view during this period.
From 16th, Oxford Events will launch on a new website: events.ox.ac.uk, and event submissions will resume. You will need a Halo login to submit events. Full details are available on the Staff Gateway.
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.
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