https://doi.org/10.1140/epjb/s10051-024-00838-6
Regular Article - Statistical and Nonlinear Physics
Opinion dynamics based on social learning theory
School of Science, Beijing University of Posts and Telecommunications, 100876, Beijing, China
a
qldai@bupt.edu.cn
b
haihongli@bupt.edu.cn
Received:
8
June
2024
Accepted:
29
November
2024
Published online:
9
December
2024
In opinion dynamics, how individuals update their opinions has a profound impact on the final opinion distribution. Though extensive efforts have been made to explore opinion evolution rules, it still remains a challenging issue since opinions of individuals are usually shaped by complicated factors in the real world. In this paper, we introduce social learning theory (SLT) into opinion dynamics and study how the opinion evolution rule derived from SLT affects opinion evolution. Based on SLT, three factors are considered when individuals update their opinions, peer influence, role model influence and personal experience, and three parameters are introduced to regulate their weights of them. Numerical simulations on scale-free networks reveal that the opinion dynamics based on SLT could effectively promote consensus in a population. Especially, the role model influence from surroundings plays a significant role in the consensus of opinions. Whereas, consensus could not be realized through only the role model influence, and an appropriate combination with peer influence can facilitate consensus best. Meanwhile, we find that, holding personal experience to a certain extent is in favor of the final consensus, although it may extend the relaxation time. Besides, when the weight of personal experience is fixed, there exists an optimal weight combination of peer influence and role model influence that leads to the minimum relaxation time. These results may offer a new perspective on understanding the evolution of public opinions and the emergence of consensus.
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© The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature 2024
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
