Understanding the Axioms and Assumptions of Logical Mathematical Systems through Raster Images: Application to the Construction of a Likert Scale
dc.contributor.author
dc.date.accessioned
2023-12-19T09:47:04Z
dc.date.available
2023-12-19T09:47:04Z
dc.date.issued
2023-11-21
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dc.description.abstract
This article presents different artistic raster images as a resource for correcting misconceptions about different laws and assumptions that underlie the propositional systems of binary logic, Łukasiewicz’s trivalent logic, Peirce’s trivalent logic, Post’s n-valent logic, and Black and Zadeh’s infinite-valent logic. Recognizing similarities and differences in how images are constructed allows us to deepen, through comparison, the laws of bivalence, non-contradiction, and excluded middle, as well as understanding other multivalent logic assumptions from another perspective, such as their number of truth values. Consequently, the first goal of this article is to illustrate how the use of visualization can be a powerful tool for better understanding some logic systems. To demonstrate the utility of this objective, we illustrate how a deeper understanding of logic systems helps us appreciate the necessity of employing Likert scales based on the logic of Post or Zadeh, which is the second goal of the article
dc.description.sponsorship
This research was supported by the Spanish Ministry of Science and Innovation/AEI/10.13039/501100011033 and by ERDF A way of making Europe (grant PID2021-123833OB-I00)
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application/pdf
dc.language.iso
eng
dc.publisher
MDPI (Multidisciplinary Digital Publishing Institute)
dc.relation
PID2021-123833OB-I00
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Reproducció digital del document publicat a: https://doi.org/10.3390/axioms12121064
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Axioms, 2023, vol. 12, núm. 12, p. 1064
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Articles publicats (D-DE)
dc.rights
Attribution 4.0 International
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dc.subject
dc.title
Understanding the Axioms and Assumptions of Logical Mathematical Systems through Raster Images: Application to the Construction of a Likert Scale
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.relation.projectID
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-123833OB-I00/ES/GENERATION AND TRANSFER OF COMPOSITIONAL DATA ANALYSIS KNOWLEDGE/
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.contributor.funder
dc.type.peerreviewed
peer-reviewed
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dc.relation.ProjectAcronym
dc.identifier.eissn
2075-1680