Perspective in Opinion Dynamics on Complex Convex Domains of Time Networks for Addiction, Forgetting
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866916221460414464 |
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| author | Kawahata, Yasuko |
| author_facet | Kawahata, Yasuko |
| contents | This paper revises previous work and introduces changes in spatio-temporal scales. The paper presents a model that includes layers A and B with varying degrees of forgetting and dependence over time. We also model changes in dependence and forgetting in layers A, A', B, and B' under certain conditions. In addition, to discuss the formation of opinion clusters that have reinforcing or obstructive behaviors of forgetting and dependence and are conservative or brainwashing or detoxifying and less prone to filter bubbling, new clusters C and D that recommend, obstruct, block, or incite forgetting and dependence over time are Introduction. This introduction allows us to test hypotheses regarding the expansion of opinions in two dimensions over time and space, the state of development of opinion space, and the expansion of public opinion. Challenges in consensus building will be highlighted, emphasizing the dynamic nature of opinions and the need to consider factors such as dissent, distrust, and media influence. The paper proposes an extended framework that incorporates trust, distrust, and media influence into the consensus building model. We introduce network analysis using dimerizing as a method to gain deeper insights. In this context, we discuss network clustering, media influence, and consensus building. The location and distribution of dimers will be analyzed to gain insight into the structure and dynamics of the network. Dimertiling has been applied in various fields other than network analysis, such as physics and sociology. The paper concludes by emphasizing the importance of diverse perspectives, network analysis, and influential entities in consensus building. It also introduces torus-based visualizations that aid in understanding complex network structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_15318 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Perspective in Opinion Dynamics on Complex Convex Domains of Time Networks for Addiction, Forgetting Kawahata, Yasuko Physics and Society Artificial Intelligence This paper revises previous work and introduces changes in spatio-temporal scales. The paper presents a model that includes layers A and B with varying degrees of forgetting and dependence over time. We also model changes in dependence and forgetting in layers A, A', B, and B' under certain conditions. In addition, to discuss the formation of opinion clusters that have reinforcing or obstructive behaviors of forgetting and dependence and are conservative or brainwashing or detoxifying and less prone to filter bubbling, new clusters C and D that recommend, obstruct, block, or incite forgetting and dependence over time are Introduction. This introduction allows us to test hypotheses regarding the expansion of opinions in two dimensions over time and space, the state of development of opinion space, and the expansion of public opinion. Challenges in consensus building will be highlighted, emphasizing the dynamic nature of opinions and the need to consider factors such as dissent, distrust, and media influence. The paper proposes an extended framework that incorporates trust, distrust, and media influence into the consensus building model. We introduce network analysis using dimerizing as a method to gain deeper insights. In this context, we discuss network clustering, media influence, and consensus building. The location and distribution of dimers will be analyzed to gain insight into the structure and dynamics of the network. Dimertiling has been applied in various fields other than network analysis, such as physics and sociology. The paper concludes by emphasizing the importance of diverse perspectives, network analysis, and influential entities in consensus building. It also introduces torus-based visualizations that aid in understanding complex network structures. |
| title | Perspective in Opinion Dynamics on Complex Convex Domains of Time Networks for Addiction, Forgetting |
| topic | Physics and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2311.15318 |