Abduction: A Categorical Characterization

Autores
Tohme, Fernando Abel; Caterina, Gianluca; Gangle, Rocco
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Scientific knowledge is gained by the informed (on the basis of theoretic ideas and criteria) examination of data. This can be easily seen in the context of quantitative data, handled with statistical methods. Here we are interested in other forms of data analysis, although with the same goal of extracting meaningful information. The idea is that data should guide the construction of suitable models, which later may lead to the development of new theories. This kind of inference is called abduction and constitutes a central procedure called Peircean qualitative induction. In this paper we will present a category-theoretic representation of abduction based on the notion of adjunction, which highlights the fundamental fact that an abduction is the most efficient way of capturing the information obtained from a large body of evidence.
Fil: Tohme, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Matemática Bahía Blanca (i); Argentina. Universidad Nacional del Sur; Argentina
Fil: Caterina, Gianluca. Center for Diagrammatic and Computacional Philosophy; Estados Unidos
Fil: Gangle, Rocco. Center for Diagrammatic and Computacional Philosophy; Estados Unidos
Materia
Abduction
Category-Theoretical Representation
Adjunction
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/11926

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spelling Abduction: A Categorical CharacterizationTohme, Fernando AbelCaterina, GianlucaGangle, RoccoAbductionCategory-Theoretical RepresentationAdjunctionhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1Scientific knowledge is gained by the informed (on the basis of theoretic ideas and criteria) examination of data. This can be easily seen in the context of quantitative data, handled with statistical methods. Here we are interested in other forms of data analysis, although with the same goal of extracting meaningful information. The idea is that data should guide the construction of suitable models, which later may lead to the development of new theories. This kind of inference is called abduction and constitutes a central procedure called Peircean qualitative induction. In this paper we will present a category-theoretic representation of abduction based on the notion of adjunction, which highlights the fundamental fact that an abduction is the most efficient way of capturing the information obtained from a large body of evidence.Fil: Tohme, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Matemática Bahía Blanca (i); Argentina. Universidad Nacional del Sur; ArgentinaFil: Caterina, Gianluca. Center for Diagrammatic and Computacional Philosophy; Estados UnidosFil: Gangle, Rocco. Center for Diagrammatic and Computacional Philosophy; Estados UnidosElsevier Science2015-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/11926Tohme, Fernando Abel; Caterina, Gianluca; Gangle, Rocco; Abduction: A Categorical Characterization; Elsevier Science; Journal Of Applied Logic; 13; 1; 3-2015; 78-901570-8683enginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S1570868314000895info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jal.2014.12.004info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:47:42Zoai:ri.conicet.gov.ar:11336/11926instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:47:43.176CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Abduction: A Categorical Characterization
title Abduction: A Categorical Characterization
spellingShingle Abduction: A Categorical Characterization
Tohme, Fernando Abel
Abduction
Category-Theoretical Representation
Adjunction
title_short Abduction: A Categorical Characterization
title_full Abduction: A Categorical Characterization
title_fullStr Abduction: A Categorical Characterization
title_full_unstemmed Abduction: A Categorical Characterization
title_sort Abduction: A Categorical Characterization
dc.creator.none.fl_str_mv Tohme, Fernando Abel
Caterina, Gianluca
Gangle, Rocco
author Tohme, Fernando Abel
author_facet Tohme, Fernando Abel
Caterina, Gianluca
Gangle, Rocco
author_role author
author2 Caterina, Gianluca
Gangle, Rocco
author2_role author
author
dc.subject.none.fl_str_mv Abduction
Category-Theoretical Representation
Adjunction
topic Abduction
Category-Theoretical Representation
Adjunction
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Scientific knowledge is gained by the informed (on the basis of theoretic ideas and criteria) examination of data. This can be easily seen in the context of quantitative data, handled with statistical methods. Here we are interested in other forms of data analysis, although with the same goal of extracting meaningful information. The idea is that data should guide the construction of suitable models, which later may lead to the development of new theories. This kind of inference is called abduction and constitutes a central procedure called Peircean qualitative induction. In this paper we will present a category-theoretic representation of abduction based on the notion of adjunction, which highlights the fundamental fact that an abduction is the most efficient way of capturing the information obtained from a large body of evidence.
Fil: Tohme, Fernando Abel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Matemática Bahía Blanca (i); Argentina. Universidad Nacional del Sur; Argentina
Fil: Caterina, Gianluca. Center for Diagrammatic and Computacional Philosophy; Estados Unidos
Fil: Gangle, Rocco. Center for Diagrammatic and Computacional Philosophy; Estados Unidos
description Scientific knowledge is gained by the informed (on the basis of theoretic ideas and criteria) examination of data. This can be easily seen in the context of quantitative data, handled with statistical methods. Here we are interested in other forms of data analysis, although with the same goal of extracting meaningful information. The idea is that data should guide the construction of suitable models, which later may lead to the development of new theories. This kind of inference is called abduction and constitutes a central procedure called Peircean qualitative induction. In this paper we will present a category-theoretic representation of abduction based on the notion of adjunction, which highlights the fundamental fact that an abduction is the most efficient way of capturing the information obtained from a large body of evidence.
publishDate 2015
dc.date.none.fl_str_mv 2015-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/11926
Tohme, Fernando Abel; Caterina, Gianluca; Gangle, Rocco; Abduction: A Categorical Characterization; Elsevier Science; Journal Of Applied Logic; 13; 1; 3-2015; 78-90
1570-8683
url http://hdl.handle.net/11336/11926
identifier_str_mv Tohme, Fernando Abel; Caterina, Gianluca; Gangle, Rocco; Abduction: A Categorical Characterization; Elsevier Science; Journal Of Applied Logic; 13; 1; 3-2015; 78-90
1570-8683
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S1570868314000895
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jal.2014.12.004
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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