Author(s): Anonymous
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Average-based updating is one of the most common and simplest approaches to model spread of information in social networks. In this approach, each agent forms their belief as the average of reported beliefs of its contacts in the previous period. Golub and Jackson (2012) show that if subjects form symmetric (undirected) connections then under some conditions the speed of convergence can be linked to a measure of network homophily. Additionally, knowing the probabilities of connections within each type and across types happens to be sufficient to accurately predict the speed of convergence. However, these results provide little guidance for more realistic cases in which the influence is asymmetric (John listens to Donald, but Donald doesn't listen to John). The question on the speed of convergence of beliefs with non-symmetric (undirected) connections still remains open. Theoretical derivations can potentially rely on the recent result by Chatterjee (2023) linking Markov chain convergence times to singular values of the chain's generator matrix. (inspired by Ben Golub).
Published: 2023-10-24 17:32:49 PT
Stage: Research Idea
Fields: Microeconomics, Mathematical Methods
Research Group(s): Playground
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Versions: v1 (10/24/2023)