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A free dialogical logic for surrogate reasoning: generation of hypothesis without ontological commitments

Redmond, Juan (2021) A free dialogical logic for surrogate reasoning: generation of hypothesis without ontological commitments. THEORIA. An International Journal for Theory, History and Foundations of Science, 36 (3). pp. 297-320. ISSN 2171-679X

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Abstract

This article aims to present a Free Dialogic Logic [FDL] as a general framework for hypothesis generation in the practice of modelling in science. Our proposal is based on the idea that the inferential function that models fulfil during the modelling process (surrogate reasoning) should be carried out without ontological commitments. The starting point to achieve our objective is that the scientific consideration of models without a target is a symptom that, on the one hand, the Applicability of Logic should be considered among the conditions of adequacy that should take into account all modeling process and, on the other, that the inferential apparatus at the base of the surrogate reasoning process must be rid of realistic assumptions that lead to erroneous conclusions. In this sense, we propose as an alternative an ontologically neutral inferential system in the perspective of dialogical pragmatism.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Redmond, Juanjuan.redmond@uv.cl
Additional Information: ISSN: 0495-4548 (print)
Keywords: targetless models, dialogic, surrogate reasoning, free logic
Subjects: Specific Sciences > Mathematics > Logic
General Issues > Formal Learning Theory
General Issues > Models and Idealization
Depositing User: Unnamed user with email theoria@ehu.es
Date Deposited: 08 Dec 2021 17:53
Last Modified: 08 Dec 2021 17:53
Item ID: 19995
Journal or Publication Title: THEORIA. An International Journal for Theory, History and Foundations of Science
Publisher: Euskal Herriko Unibertsitatea / Universidad del País Vasco
Official URL: https://ojs.ehu.eus/index.php/THEORIA/article/view...
DOI or Unique Handle: 10.1387/theoria.21902
Subjects: Specific Sciences > Mathematics > Logic
General Issues > Formal Learning Theory
General Issues > Models and Idealization
Date: September 2021
Page Range: pp. 297-320
Volume: 36
Number: 3
ISSN: 2171-679X
URI: http://philsci-archive.pitt.edu/id/eprint/19995

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