Structural Balance of Opinions
The concept of Heider balance, usually applied to interpersonal relations, is generalized here to opinions gathered in surveys. At first, we compare four algorithms, which drive a matrix dataset to a balanced state. The criterion is that the final state obtained with an algorithm should be as close...
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MDPI AG
2021
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oai:doaj.org-article:45887e8f58524bf0bdd862178d4e0f112021-11-25T17:29:30ZStructural Balance of Opinions10.3390/e231114181099-4300https://doaj.org/article/45887e8f58524bf0bdd862178d4e0f112021-10-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1418https://doaj.org/toc/1099-4300The concept of Heider balance, usually applied to interpersonal relations, is generalized here to opinions gathered in surveys. At first, we compare four algorithms, which drive a matrix dataset to a balanced state. The criterion is that the final state obtained with an algorithm should be as close as possible to the initial state. The result is that deterministic differential equations work better than their Monte Carlo counterparts. Next, we apply the winning algorithms to the matrix of correlations between opinions gathered in American states between 1974 and 1998. The results are interpreted in terms of the classic comfort hypothesis (E. Babbie, 2007).Malgorzata J. KrawczykKrzysztof KułakowskiMDPI AGarticleHeider balancenonlinear dynamicsAmerican statescomfort hypothesisScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1418, p 1418 (2021) |
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Heider balance nonlinear dynamics American states comfort hypothesis Science Q Astrophysics QB460-466 Physics QC1-999 |
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Heider balance nonlinear dynamics American states comfort hypothesis Science Q Astrophysics QB460-466 Physics QC1-999 Malgorzata J. Krawczyk Krzysztof Kułakowski Structural Balance of Opinions |
description |
The concept of Heider balance, usually applied to interpersonal relations, is generalized here to opinions gathered in surveys. At first, we compare four algorithms, which drive a matrix dataset to a balanced state. The criterion is that the final state obtained with an algorithm should be as close as possible to the initial state. The result is that deterministic differential equations work better than their Monte Carlo counterparts. Next, we apply the winning algorithms to the matrix of correlations between opinions gathered in American states between 1974 and 1998. The results are interpreted in terms of the classic comfort hypothesis (E. Babbie, 2007). |
format |
article |
author |
Malgorzata J. Krawczyk Krzysztof Kułakowski |
author_facet |
Malgorzata J. Krawczyk Krzysztof Kułakowski |
author_sort |
Malgorzata J. Krawczyk |
title |
Structural Balance of Opinions |
title_short |
Structural Balance of Opinions |
title_full |
Structural Balance of Opinions |
title_fullStr |
Structural Balance of Opinions |
title_full_unstemmed |
Structural Balance of Opinions |
title_sort |
structural balance of opinions |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doaj.org/article/45887e8f58524bf0bdd862178d4e0f11 |
work_keys_str_mv |
AT malgorzatajkrawczyk structuralbalanceofopinions AT krzysztofkułakowski structuralbalanceofopinions |
_version_ |
1718412281603162112 |