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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/11926
Ver los metadatos del registro completo
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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 |
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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 |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.13397 |