Innovation adoption and collective experimentation

I study learning about an innovation with costly information acquisition and knowledge sharing through a network. Agents situated in an arbitrary graph follow a myopic belief update rule. The network structure and initial beliefs jointly determine long-run adoption behavior. Networks that share information effectively converge on a consensus more quickly but are prone to errors. Consequently, dense or centralized networks have more volatile outcomes, and efforts to seed adoption should focus on individuals who are disconnected from one another. I argue that anti-seeding, preventing central individuals from experimenting early in the learning process, is an effective intervention because the population as a whole may gather more information. (C) 2020 Elsevier Inc. All rights reserved.