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- W4285091287 abstract "We study a model of opinion exchange in social networks where a state of the world is realized and every agent receives a zero-mean noisy signal of the realized state. It is known from Golub and Jackson [6] that under DeGroot [3] dynamics agents reach a consensus that is close to the state of the world when the network is large. The DeGroot dynamics, however, is highly non-robust and the presence of a single stubborn agent that does not adhere to the updating rule can sway the public consensus to any other value. We introduce a variant of DeGroot dynamics that we call 1/m-DeGroot. 1/m-DeGroot dynamics approximates standard DeGroot dynamics to the nearest rational number with m as its denominator and like the DeGroot dynamics it is Markovian and stationary. We show that in contrast to standard DeGroot dynamics, 1/m-DeGroot dynamics is highly robust both to the presence of stubborn agents and to certain types of misspecifications." @default.
- W4285091287 created "2022-07-14" @default.
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- W4285091287 date "2022-07-12" @default.
- W4285091287 modified "2023-09-27" @default.
- W4285091287 title "Granular DeGroot Dynamics -- a Model for Robust Naive Learning in Social Networks" @default.
- W4285091287 cites W1998692453 @default.
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- W4285091287 doi "https://doi.org/10.1145/3490486.3538291" @default.
- W4285091287 hasPublicationYear "2022" @default.
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