Dynamics Analysis for the Random Homogeneous Biased Assimilation Model

This paper studies the evolution of opinions over random social networks subject to individual biases. An agent reviews the opinion of a randomly selected one and then updates its opinion under homogeneous biased assimilation. This study investigates the impact of biased assimilation on random opini...

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Main Authors: Jiangbo Zhang, Yiyi Zhao
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/11/7/1661
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author Jiangbo Zhang
Yiyi Zhao
author_facet Jiangbo Zhang
Yiyi Zhao
author_sort Jiangbo Zhang
collection DOAJ
description This paper studies the evolution of opinions over random social networks subject to individual biases. An agent reviews the opinion of a randomly selected one and then updates its opinion under homogeneous biased assimilation. This study investigates the impact of biased assimilation on random opinion networks, which is different from the previous studies on fixed network structures. If the bias parameters are static, it is proven that the event in which all agents converge to extreme opinions happens almost surely. Next, the opinion polarization event is proved to be a probability one event. While if the bias parameters are dynamic, the opinion evolution is proven to depend on early finite time slots for the dynamical individual bias parameter functions independent of the biased parameter values after the time threshold. Numerical simulations further show that opinion evolution depends on early finite time slots for some nonlinear dynamical individual bias parameter functions.
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spelling doaj.art-cf0025c296f04dbbb468755bdf0fe6982023-11-17T17:08:51ZengMDPI AGMathematics2227-73902023-03-01117166110.3390/math11071661Dynamics Analysis for the Random Homogeneous Biased Assimilation ModelJiangbo Zhang0Yiyi Zhao1School of Science, Southwest Petroleum University, Chengdu 610500, ChinaSchool of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, ChinaThis paper studies the evolution of opinions over random social networks subject to individual biases. An agent reviews the opinion of a randomly selected one and then updates its opinion under homogeneous biased assimilation. This study investigates the impact of biased assimilation on random opinion networks, which is different from the previous studies on fixed network structures. If the bias parameters are static, it is proven that the event in which all agents converge to extreme opinions happens almost surely. Next, the opinion polarization event is proved to be a probability one event. While if the bias parameters are dynamic, the opinion evolution is proven to depend on early finite time slots for the dynamical individual bias parameter functions independent of the biased parameter values after the time threshold. Numerical simulations further show that opinion evolution depends on early finite time slots for some nonlinear dynamical individual bias parameter functions.https://www.mdpi.com/2227-7390/11/7/1661opinion dynamicsbias parameterhomogeneouspolarizationconsensus
spellingShingle Jiangbo Zhang
Yiyi Zhao
Dynamics Analysis for the Random Homogeneous Biased Assimilation Model
Mathematics
opinion dynamics
bias parameter
homogeneous
polarization
consensus
title Dynamics Analysis for the Random Homogeneous Biased Assimilation Model
title_full Dynamics Analysis for the Random Homogeneous Biased Assimilation Model
title_fullStr Dynamics Analysis for the Random Homogeneous Biased Assimilation Model
title_full_unstemmed Dynamics Analysis for the Random Homogeneous Biased Assimilation Model
title_short Dynamics Analysis for the Random Homogeneous Biased Assimilation Model
title_sort dynamics analysis for the random homogeneous biased assimilation model
topic opinion dynamics
bias parameter
homogeneous
polarization
consensus
url https://www.mdpi.com/2227-7390/11/7/1661
work_keys_str_mv AT jiangbozhang dynamicsanalysisfortherandomhomogeneousbiasedassimilationmodel
AT yiyizhao dynamicsanalysisfortherandomhomogeneousbiasedassimilationmodel