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Opinion Dynamics with Bayesian Learning

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  • Aili Fang
  • Kehua Yuan
  • Jinhua Geng
  • Xinjiang Wei

Abstract

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.

Suggested Citation

  • Aili Fang & Kehua Yuan & Jinhua Geng & Xinjiang Wei, 2020. "Opinion Dynamics with Bayesian Learning," Complexity, Hindawi, vol. 2020, pages 1-5, February.
    • Handle: RePEc:hin:complx:8261392
    DOI: 10.1155/2020/8261392
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    Cited by:

    1. Low, Nicholas Kah Yean & Melatos, Andrew, 2022. "Discerning media bias within a network of political allies and opponents: The idealized example of a biased coin," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    2. Fang, Aili, 2021. "The influence of communication structure on opinion dynamics in social networks with multiple true states," Applied Mathematics and Computation, Elsevier, vol. 406(C).

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