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/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

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

Zendy Tools

Zendy Open

We examine how well people learn when information is noisily relayed fromperson to person; and we study how communication platforms can improve learningwithout censoring or fact-checking messages. We analyze learning as a functionof social network depth (how many times information is relayed) and breadth(the number of relay chains accessed). Noise builds up as depth increases, solearning requires greater breadth. In the presence of mutations (deliberate orrandom) and transmission failures of messages, we characterize sharp thresholdsfor breadths above which receivers learn fully and below which they learnnothing. When there is uncertainty about mutation rates, optimizing learningrequires either capping depth, or if that is not possible, limiting breadth bycapping the number of people to whom someone can forward a message. Limitingbreadth cuts the number of messages received but also decreases the fractionoriginating further from the receiver, and so can increase the signal to noiseratio. Finally, we extend our model to study learning from message survival:e.g., people are more likely to pass messages with one conclusion than another.We find that as depth grows, all learning comes from either the total number ofmessages received or from the content of received messages, but the learnerdoes not need to pay attention to both.

Learning through the Grapevine: The Impact of Noise and the Breadth and Depth of Social Networks