Learning Through the Grapevine: The Impact of Message Mutation, Transmission Failure, and Deliberate Bias

Jackson, Matthew O.; Malladi, Suraj; McAdams, David

doi:10.2139/ssrn.3269543

Cited by 5 publications

(3 citation statements)

References 16 publications

(9 reference statements)

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“…In ref. 7 , we show that in the presence of such mutations, slight uncertainty over the mutation rates severely limits what even a fully Bayesian receiver can learn from a distance, no matter how many independent chains of message relays they hear. Moreover, it appears that people are far from the ideal Bayesian social learner and tend to be swayed by the preponderance of messages (e.g., see ref.…”

Section: Some Background Observationsmentioning

confidence: 95%

“… ‡ In another paper ( 7 ), we analyze social learning when the pass rate p depends on message content and characterize conditions under which learning can be enhanced by such dependence. However, those results shed little additional light on the role of network structure, and so we focus here on the case in which all messages have the same pass rate.…”

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confidence: 99%

“… ‖ In ref. 7 , we characterize how agents learn with known mutation rates and known chain lengths. We show that learning from distant messages requires not only sufficient growth in the network but also that the learner has no uncertainty about the mutation rates.…”

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confidence: 99%

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Learning through the grapevine and the impact of the breadth and depth of social networks

Jackson

Malladi

McAdams

2022

Proc. Natl. Acad. Sci. U.S.A.

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We study how communication platforms can improve social learning without censoring or fact-checking messages, when they have members who deliberately and/or inadvertently distort information. Message fidelity depends on social network depth (how many times information can be relayed) and breadth (the number of others with whom a typical user shares information). We characterize how the expected number of true minus false messages depends on breadth and depth of the network and the noise structure. Message fidelity can be improved by capping depth or, if that is not possible, limiting breadth, e.g., by capping the number of people to whom someone can forward a given message. Although caps reduce total communication, they increase the fraction of received messages that have traveled shorter distances and have had less opportunity to be altered, thereby increasing the signal-to-noise ratio.

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Section: Some Background Observationsmentioning

confidence: 95%

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confidence: 99%