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

38 Pages Posted: 7 Dec 2018 Last revised: 15 Jun 2020

See all articles by Matthew O. Jackson

Matthew O. Jackson

Stanford University - Department of Economics; Santa Fe Institute

Suraj Malladi

Stanford University, Graduate School of Business

David McAdams

Duke University - Fuqua School of Business

Date Written: March 1, 2019

Abstract

We examine how well people learn when information is noisily relayed from person to person; and we study how communication platforms can improve learning without censoring or fact-checking messages. We analyze learning as a function of social network depth (how many times information is relayed) and breadth (the number of relay chains accessed). Noise builds up as depth increases, so learning requires greater breadth. In the presence of mutations (deliberate or random) and transmission failures of messages, we characterize sharp thresholds for breadths above which receivers learn fully and below which they learn nothing. When there is uncertainty about mutation rates, optimizing learning requires either capping depth, or if that is not possible, limiting breadth by capping the number of people to whom someone can forward a message. Limiting breadth cuts the number of messages received but also decreases the fraction originating further from the receiver, and so can increase the signal to noise ratio. 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 of messages received or from the content of received messages, but the learner does not need to pay attention to both.

Keywords: Social Learning, Communication, Noise, Mutation, Bias, Fake News

JEL Classification: D83, D85, L14, O12, Z13

Suggested Citation

Jackson, Matthew O. and Malladi, Suraj and McAdams, David, Learning through the Grapevine: The Impact of Noise and the Breadth and Depth of Social Networks (March 1, 2019). Available at SSRN: https://ssrn.com/abstract=3269543 or http://dx.doi.org/10.2139/ssrn.3269543

Matthew O. Jackson (Contact Author)

Stanford University - Department of Economics ( email )

Landau Economics Building
579 Serra Mall
Stanford, CA 94305-6072
United States
1-650-723-3544 (Phone)

HOME PAGE: http://www.stanford.edu/~jacksonm

Santa Fe Institute

1399 Hyde Park Road
Santa Fe, NM 87501
United States

Suraj Malladi

Stanford University, Graduate School of Business ( email )

Stanford, CA
United States

David McAdams

Duke University - Fuqua School of Business ( email )

Box 90120
Durham, NC 27708-0120
United States

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