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A Hybrid Opinion Formation and Polarization Model
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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689558/ https://www.ncbi.nlm.nih.gov/pubmed/36421547 http://dx.doi.org/10.3390/e24111692 |
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author | Yang, Baizhong Yu, Quan Fan, Yi |
author_facet | Yang, Baizhong Yu, Quan Fan, Yi |
author_sort | Yang, Baizhong |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9689558 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96895582022-11-25 A Hybrid Opinion Formation and Polarization Model Yang, Baizhong Yu, Quan Fan, Yi Entropy (Basel) Article 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. MDPI 2022-11-19 /pmc/articles/PMC9689558/ /pubmed/36421547 http://dx.doi.org/10.3390/e24111692 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yang, Baizhong Yu, Quan Fan, Yi A Hybrid Opinion Formation and Polarization Model |
title | A Hybrid Opinion Formation and Polarization Model |
title_full | A Hybrid Opinion Formation and Polarization Model |
title_fullStr | A Hybrid Opinion Formation and Polarization Model |
title_full_unstemmed | A Hybrid Opinion Formation and Polarization Model |
title_short | A Hybrid Opinion Formation and Polarization Model |
title_sort | hybrid opinion formation and polarization model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689558/ https://www.ncbi.nlm.nih.gov/pubmed/36421547 http://dx.doi.org/10.3390/e24111692 |
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