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Opinion Dynamics with Bayesian Learning
Joint Authors
Wei, Xinjiang
Fang, Aili
Yuan, Kehua
Geng, Jinhua
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-5, 5 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-02-22
Country of Publication
Egypt
No. of Pages
5
Main Subjects
Abstract EN
Bayesian learning is a rational and effective strategy in the opinion dynamic process.
In this paper, we theoretically prove that individual Bayesian learning can realize asymptotic learning and we test it by simulations on the Zachary network.
Then, we propose a Bayesian social learning model with signal update strategy and apply the model on the Zachary network to observe opinion dynamics.
Finally, we contrast the two learning strategies and find that Bayesian social learning can lead to asymptotic learning more faster than individual Bayesian learning.
American Psychological Association (APA)
Fang, Aili& Yuan, Kehua& Geng, Jinhua& Wei, Xinjiang. 2020. Opinion Dynamics with Bayesian Learning. Complexity،Vol. 2020, no. 2020, pp.1-5.
https://search.emarefa.net/detail/BIM-1144162
Modern Language Association (MLA)
Fang, Aili…[et al.]. Opinion Dynamics with Bayesian Learning. Complexity No. 2020 (2020), pp.1-5.
https://search.emarefa.net/detail/BIM-1144162
American Medical Association (AMA)
Fang, Aili& Yuan, Kehua& Geng, Jinhua& Wei, Xinjiang. Opinion Dynamics with Bayesian Learning. Complexity. 2020. Vol. 2020, no. 2020, pp.1-5.
https://search.emarefa.net/detail/BIM-1144162
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1144162