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Effective complexity: In which sense is it informative?

Céspedes, Esteban and Fuentes, Miguel (2019) Effective complexity: In which sense is it informative? [Preprint]

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Abstract

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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Item Type: Preprint
Creators:
CreatorsEmailORCID
Céspedes, Esteban
Fuentes, Miguel
Additional Information: Forthcoming in the Journal for General Philosophy of Science
Keywords: effective complexity, algorithmic complexity, epistemic context, emergence, physical theory
Subjects: General Issues > Data
Specific Sciences > Complex Systems
General Issues > Laws of Nature
General Issues > Structure of Theories
General Issues > Theory Change
Depositing User: Mr. Esteban Céspedes
Date Deposited: 15 Oct 2019 12:12
Last Modified: 15 Oct 2019 12:12
Item ID: 16520
Subjects: General Issues > Data
Specific Sciences > Complex Systems
General Issues > Laws of Nature
General Issues > Structure of Theories
General Issues > Theory Change
Date: 2019
URI: http://philsci-archive.pitt.edu/id/eprint/16520

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