The fusion process of interval opinions based on the dynamic bounded confidence

Liang, Haiming; Li, Cong-Cong; Dong, Yucheng; Jiang, Yan-Ping

doi:10.1016/j.inffus.2015.08.010

Cited by 32 publications

(10 citation statements)

References 39 publications

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“…Opinion dynamics has been explored by most scholars in recent years [51][52][53]. However, few researchers explored the opinions of affinity and social noise systematically.…”

Section: Discussionmentioning

confidence: 99%

An opinion dynamics model based on affinity and social noise

Liu

He²,

Qiu³

et al. 2022

Front. Phys.

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Most previous works have studied the evolution of opinions based on the Hegselmann–Krause model, the Deffuant–Weisbuch model, and the Sznajd model. However, the influence of social influence on opinions is discussed less. Based on the social influence theory and the Hegselmann–Krause model of opinion dynamics, we introduce the affinity and social noise in the Hegselmann–Krause model of opinion dynamics and propose an affinity and social noise Hegselmann–Krause model (ASNHK). The influence of affinity degree, affinity threshold, social noise, and personnel heterogeneity on opinion evolution is discussed in experimental analysis. Experimental results show that the affinity between people can improve opinions to form a consensus positively, but the affinity threshold has a negative role contrarily. Moreover, when the social noise increases, the opinions will form a consensus. When it increases to a certain value, the opinion will be decentralized. Furthermore, personnel heterogeneity has different effects on opinion evolution. Open-minded individuals are more likely to form a unified opinion, while closed-minded individuals have difficulty unifying their opinions. Overall, this research provides a clearer explanation of the group opinion evolution from social influence.

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“…Opinion dynamics has been explored by most scholars in recent years [51][52][53]. However, few researchers explored the opinions of affinity and social noise systematically.…”

Section: Discussionmentioning

confidence: 99%

An opinion dynamics model based on affinity and social noise

Liu

He²,

Qiu³

et al. 2022

Front. Phys.

View full text Add to dashboard Cite

show abstract

“…For each of the three parameters above, we evaluated how they influence the speed of convergence. So given a model with all parameters specified, we use

to represent the average number of iterations needed to achieve convergence (see [ 50 ] for more details). Moreover, in order to observe convergence in a convenient way, we used an additional parameter

which means the number of iterations we perform in a particular run.…”

Section: Simulations With Poe Modelmentioning

confidence: 99%

“…Group polarization is a hot topec in recent research of opinion dynamics [ 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 ]. In this section, we propose three extensions of our POE model which served for polarization.…”

Section: Three Mechanisms For Polarizationmentioning

confidence: 99%

A Hybrid Opinion Formation and Polarization Model

Yang

2022

Entropy

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The last decade has witnessed a great number of opinion formation models that depict the evolution of opinions within a social group and make predictions about the evolution process. In the traditional formulation of opinion evolution such as the DeGroot model, an agent’s opinion is represented as a real number and updated by taking a weighted average of its neighbour’s opinions. In this paper, we adopt a hybrid representation of opinions that integrate both the discrete and continuous nature of an opinion. Basically, an agent has a `Yes’, `Neutral’ or `No’ opinion on some issues of interest and associates with its Yes opinion a support degree which captures how strongly it supports the opinion. With such a rich representation, not only can we study the evolution of opinion but also that of support degree. After all, an agent’s opinion can stay the same but become more or less supportive of it. Changes in the support degree are progressive in nature and only a sufficient accumulation of such a progressive change will result in a change of opinion say from Yes to No. Hence, in our formulation, after an agent interacts with another, its support degree is either strengthened or weakened by a predefined amount and a change of opinion may occur as a consequence of such progressive changes. We carry out simulations to evaluate the impacts of key model parameters including (1) the number of agents, (2) the distribution of initial support degrees and (3) the amount of change of support degree changes in a single interaction. Last but not least, we present several extensions to the hybrid and progressive model which lead to opinion polarization.

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“…In addition, some scholars have also considered the changes in the time when studying the convergence of models [17,18]. Some scholars combine opinion leaders [19][20][21], network media [22,23], information content [24], opinion form [25,26], and network structure [27,28] with the bounded con dence model to enrich the application of the bounded con dence model. For example, Chen et al suggested that leaders' opinions are important in guiding normal individuals to access truth, and that in some cases, small changes in parameters about individual characteristics can lead to large-scale changes in social groups.…”

Section: Introductionmentioning

confidence: 99%

Opinion Dynamics Model with Bounded Confidence and the Sleeper Effect

Wei

Jia

Zhu

et al. 2022

Computational Intelligence and Neuroscience

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The evolution of opinions is a complex mechanism. The evolution of individual opinions is not only influenced by own and others but also by psychological effects. Therefore, based on the classical bounded confidence model and the sleeper effect, a new opinion evolution model is proposed in this paper. In this new opinion evolution model, we increase the opinions on every step and take into account the “discount opinions.” Since mental states for human are not easy to measure, we assumed three different initial networks and carried out simulation experiments. To verify the rationality on our model, we compared the effects with and without the sleeper effect, different thresholds, and discounting opinion ratio on opinion aggregation and convergence. Finally, we found the sleeper effect can differently affect the convergence of opinions in different opinion environments.

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The fusion process of interval opinions based on the dynamic bounded confidence

Cited by 32 publications

References 39 publications

An opinion dynamics model based on affinity and social noise

An opinion dynamics model based on affinity and social noise

A Hybrid Opinion Formation and Polarization Model

Opinion Dynamics Model with Bounded Confidence and the Sleeper Effect

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