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Title: Local non-Bayesian social learning with stubborn agents
We study a social learning model in which agents iteratively update their beliefs about the true state of the world using private signals and the beliefs of other agents in a non-Bayesian manner. Some agents are stubborn, meaning they attempt to convince others of an erroneous true state (modeling fake news). We show that while agents learn the true state on short timescales, they "forget" it and believe the erroneous state to be true on longer timescales. Using these results, we devise strategies for seeding stubborn agents so as to disrupt learning, which outperform intuitive heuristics and give novel insights regarding vulnerabilities in social learning.  more » « less
Award ID(s):
2008130 1608361 2038416 1955777
PAR ID:
10332927
Author(s) / Creator(s):
;
Editor(s):
Altafini, Claudio; Como, Giacomo; Hendrickx, Julien M.; Olshevsky, Alexander; Tahbez-Salehi, Alireza
Date Published:
Journal Name:
IEEE Transactions on Control of Network Systems
ISSN:
2372-2533
Page Range / eLocation ID:
1 to 1
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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