English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Following the Bayesian communication learning paradigm, we propose a finitepopulation learning concept to capture the level of information aggregation inany given network, where agents are allowed to communicate with neighborsrepeatedly before making a single decision. This concept helps determine theoccurrence of effective information aggregation in a finite network and revealsexplicit interplays among parameters. It also enables meaningful comparativestatics regarding the effectiveness of information aggregation in networks.Moreover, it offers a solid foundation to address, with a new perfect learningconcept, long run dynamics of learning behavior and the associated learningrates as population diverges. Our conditions for the occurrence of finitepopulation learning and perfect learning in communication networks are verytractable and transparent.

Communication Learning in Social Networks: Finite Population and the Rates