Artículo
Effective Complexity: In Which Sense is It Informative?
Fecha de publicación:
09/2020
Editorial:
Springer
Revista:
Journal for General Philosophy of Science
ISSN:
0925-4560
e-ISSN:
1572-8587
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
This work responds to a criticism of effective complexity made by James McAllister, according to which such a notion is not an appropriate measure for information content. Roughly, effective complexity is focused on the regularities of the data rather than on the whole data, as opposed to algorithmic complexity. McAllister’s argument shows that, because the set of relevant regularities for a given object is not unique, one cannot assign unique values of effective complexity to considered expressions and, therefore, that algorithmic complexity better serves as a measure of information than effective complexity. We accept that problem regarding uniqueness as McAllister presents it, but would not deny that if contexts could be defined appropriately, one could in principle find unique values of effective complexity. Considering this, effective complexity is informative not only regarding the entity being investigated but also regarding the context of investigation itself. Furthermore, we argue that effective complexity is an interesting epistemological concept that may be applied to better understand crucial issues related to context dependence such as theory choice and emergence. These applications are not available merely on the basis of algorithmic complexity.
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Articulos(IIF)
Articulos de INSTITUTO DE INVESTIGACIONES FILOSOFICAS
Articulos de INSTITUTO DE INVESTIGACIONES FILOSOFICAS
Citación
Céspedes, Esteban; Fuentes, Miguel Angel; Effective Complexity: In Which Sense is It Informative?; Springer; Journal for General Philosophy of Science; 51; 3; 9-2020; 359-374
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