Neural networks and political science : testing the methodological frontiers

2023
journal article
article
cris.lastimport.wos2024-04-10T00:09:28Z
dc.abstract.enIn recent years, a number of significant methodological re-evaluations have taken place in various disciplines of science due to machine learning developments. This is particularly evident in STEM disciplines, while the behavioral and social sciences seem to approach these phenomena with some reserve. A good example is the use of artificial neural networks. Yet, acknowledging their characteristics, it can be safely assumed that they are relatively well designed to solve many problems in political science. This is due to the nature of many social phenomena that are characterized by at least three features: (1) their theoretical basis is not ultimately determined, (2) they lack fully recognized functional relations, and (3) they are described by data that occur in a form that may be cumbersome for traditional modeling. Therefore, the article proceeds with some encouragement for the use of neural networks. At the same time, however, we need to proceed with caution. To mitigate possible opacity, a new political science-informed conceptualization of neural networks categorization scheme is proposed. This aims to help social scientists come to terms with one of the exponentially developing methods in the machine learning toolboxpl
dc.affiliationWydział Studiów Międzynarodowych i Politycznych : Instytut Amerykanistyki i Studiów Polonijnychpl
dc.contributor.authorWordliczek, Łukasz - 132736 pl
dc.date.accession2023-01-25pl
dc.date.accessioned2023-02-24T11:11:38Z
dc.date.available2023-02-24T11:11:38Z
dc.date.issued2023pl
dc.date.openaccess0
dc.description.accesstimew momencie opublikowania
dc.description.number57pl
dc.description.physical37-62pl
dc.description.versionostateczna wersja wydawcy
dc.identifier.doi10.5944/empiria.57.2023.36429pl
dc.identifier.eissn2174-0682pl
dc.identifier.issn1139-5737pl
dc.identifier.urihttps://ruj.uj.edu.pl/xmlui/handle/item/308306
dc.identifier.weblinkhttps://revistas.uned.es/index.php/empiria/article/view/36429/27239pl
dc.languageengpl
dc.language.containerspapl
dc.rightsUdzielam licencji. Uznanie autorstwa - Użycie niekomercyjne - Na tych samych warunkach 4.0 Międzynarodowa*
dc.rights.licenceCC-BY-NC-SA
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.pl*
dc.share.typeotwarte czasopismo
dc.subject.enartificial neural networkspl
dc.subject.enpolitical science methodologypl
dc.subject.enmachine learningpl
dc.subject.enpredictionpl
dc.subject.enclassificationpl
dc.subtypeArticlepl
dc.titleNeural networks and political science : testing the methodological frontierspl
dc.title.alternativeRedes neuronales y ciencia política : probando las fronteras metodológicaspl
dc.title.journalEmpiriapl
dc.typeJournalArticlepl
dspace.entity.typePublication
cris.lastimport.wos
2024-04-10T00:09:28Z
dc.abstract.enpl
In recent years, a number of significant methodological re-evaluations have taken place in various disciplines of science due to machine learning developments. This is particularly evident in STEM disciplines, while the behavioral and social sciences seem to approach these phenomena with some reserve. A good example is the use of artificial neural networks. Yet, acknowledging their characteristics, it can be safely assumed that they are relatively well designed to solve many problems in political science. This is due to the nature of many social phenomena that are characterized by at least three features: (1) their theoretical basis is not ultimately determined, (2) they lack fully recognized functional relations, and (3) they are described by data that occur in a form that may be cumbersome for traditional modeling. Therefore, the article proceeds with some encouragement for the use of neural networks. At the same time, however, we need to proceed with caution. To mitigate possible opacity, a new political science-informed conceptualization of neural networks categorization scheme is proposed. This aims to help social scientists come to terms with one of the exponentially developing methods in the machine learning toolbox
dc.affiliationpl
Wydział Studiów Międzynarodowych i Politycznych : Instytut Amerykanistyki i Studiów Polonijnych
dc.contributor.authorpl
Wordliczek, Łukasz - 132736
dc.date.accessionpl
2023-01-25
dc.date.accessioned
2023-02-24T11:11:38Z
dc.date.available
2023-02-24T11:11:38Z
dc.date.issuedpl
2023
dc.date.openaccess
0
dc.description.accesstime
w momencie opublikowania
dc.description.numberpl
57
dc.description.physicalpl
37-62
dc.description.version
ostateczna wersja wydawcy
dc.identifier.doipl
10.5944/empiria.57.2023.36429
dc.identifier.eissnpl
2174-0682
dc.identifier.issnpl
1139-5737
dc.identifier.uri
https://ruj.uj.edu.pl/xmlui/handle/item/308306
dc.identifier.weblinkpl
https://revistas.uned.es/index.php/empiria/article/view/36429/27239
dc.languagepl
eng
dc.language.containerpl
spa
dc.rights*
Udzielam licencji. Uznanie autorstwa - Użycie niekomercyjne - Na tych samych warunkach 4.0 Międzynarodowa
dc.rights.licence
CC-BY-NC-SA
dc.rights.uri*
http://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.pl
dc.share.type
otwarte czasopismo
dc.subject.enpl
artificial neural networks
dc.subject.enpl
political science methodology
dc.subject.enpl
machine learning
dc.subject.enpl
prediction
dc.subject.enpl
classification
dc.subtypepl
Article
dc.titlepl
Neural networks and political science : testing the methodological frontiers
dc.title.alternativepl
Redes neuronales y ciencia política : probando las fronteras metodológicas
dc.title.journalpl
Empiria
dc.typepl
JournalArticle
dspace.entity.type
Publication
Affiliations

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