Is rigor always strictly related to precision and accuracy? This is a fundamental question in the realm of Fuzzy Logic; the first instinct would be to answer in the positive, but the question is much more complex than it appears, as true rigor is obtained also by a careful examination of the context, and limiting to a mechanical transfer of techniques, procedures and conceptual attitudes from one domain to another, such as from the pure engineering feats or the ones of mathematical logic to the study of human reasoning, does not guarantee optimal results. Starting from this question, we discuss some implications of going back to the very concept of reasoning as it is used in natural language and in everyday life. Taking into account the presence—from the start—of uncertainty and approximation in one of its possible forms seems to indicate the need of a different approach from the simple extension of tools and concepts from mathematical logic.

Tabacchi, M., Termini, S. (2017). Back to “Reasoning”. In B.M. Ferraro, P. Giordani, B. Vantaggi, M. Gagolewski, G.M. Ángeles, P. Grzegorzewski, et al. (a cura di), Soft Methods for Data Science (pp. 471-478) [10.1007/978-3-319-42972-4_58].

Back to “Reasoning”

TABACCHI, Marco;TERMINI, Settimo
2017-01-01

Abstract

Is rigor always strictly related to precision and accuracy? This is a fundamental question in the realm of Fuzzy Logic; the first instinct would be to answer in the positive, but the question is much more complex than it appears, as true rigor is obtained also by a careful examination of the context, and limiting to a mechanical transfer of techniques, procedures and conceptual attitudes from one domain to another, such as from the pure engineering feats or the ones of mathematical logic to the study of human reasoning, does not guarantee optimal results. Starting from this question, we discuss some implications of going back to the very concept of reasoning as it is used in natural language and in everyday life. Taking into account the presence—from the start—of uncertainty and approximation in one of its possible forms seems to indicate the need of a different approach from the simple extension of tools and concepts from mathematical logic.
2017
Tabacchi, M., Termini, S. (2017). Back to “Reasoning”. In B.M. Ferraro, P. Giordani, B. Vantaggi, M. Gagolewski, G.M. Ángeles, P. Grzegorzewski, et al. (a cura di), Soft Methods for Data Science (pp. 471-478) [10.1007/978-3-319-42972-4_58].
File in questo prodotto:
File Dimensione Formato  
10.1007%2F978-3-319-42972-4_58.pdf

Solo gestori archvio

Dimensione 90.67 kB
Formato Adobe PDF
90.67 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/201855
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 2
social impact