Learning through the Grapevine and the Impact of the Breadth and Depth of Social Networks
summary
Model of noisy relay on directed trees with mutation rates mu01 and mu10 and dropout probability shows that message fidelity falls with distance and with asymmetry in mutation rates. Neutral interventions that do not inspect content—depth caps (limit relay distance) or feasible breadth caps (limit forwards per message)—raise expected true minus false messages by shifting mass toward nearby, less-corrupted sources. Key claims: there is a computable cutoff distance and degree threshold for a robust preponderance of true messages; tighter caps are needed when mutation asymmetry or transmission probability is higher.
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