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Informative Models: Idealization and Abstraction

Suárez, Mauricio and Bolinska, Agnes (2021) Informative Models: Idealization and Abstraction. [Preprint]

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Abstract

Mauricio Suárez and Agnes Bolinska apply the tools of communication theory to scientific modelling in order to characterize the informational content of a scientific model. They argue that when represented as a communication channel, a model source conveys information about its target, and that such representations are therefore appropriate whenever modelling is employed for informational gain. They then extract two consequences. First, the introduction of idealizations is akin in informational terms to the
introduction of noise in a signal; for in an idealization we introduce ‘extraneous’ elements into the model that have no correlate in the target. Second, abstraction in a model is
informationally equivalent to equivocation in the signal; for in an abstraction we ‘neglect’ in the model certain features that obtain in the target. They then conclude becomes possible
in principle to quantify idealization and abstraction in informative models, although precise absolute quantification will be difficult to achieve in practice.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Suárez, Mauriciomsuarez@filos.ucm.es0000-0002-2842-3641
Bolinska, Agnesamb273@cam.ac.uk0000-0002-6133-6734
Keywords: Information – Idealization – Abstraction - Content
Subjects: General Issues > History of Philosophy of Science
General Issues > Models and Idealization
Specific Sciences > Probability/Statistics
Specific Sciences > Physics > Statistical Mechanics/Thermodynamics
Depositing User: Prof Mauricio Suárez
Date Deposited: 27 Oct 2023 01:03
Last Modified: 27 Oct 2023 01:03
Item ID: 22701
Official URL: https://link.springer.com/chapter/10.1007/978-3-03...
DOI or Unique Handle: https://doi.org/10.1007/978-3-030-65802-1_3
Subjects: General Issues > History of Philosophy of Science
General Issues > Models and Idealization
Specific Sciences > Probability/Statistics
Specific Sciences > Physics > Statistical Mechanics/Thermodynamics
Date: 28 May 2021
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/22701

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