Models and Idealizations in Science: Artifactual and Fictional Approaches. Cassini, A. & Redmond, J., editors Springer International Publishing, Cham, 2021. ![Models and Idealizations in Science: Artifactual and Fictional Approaches [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Mauricio Suárez and Agnes Bolinska apply the tools of communication theory to scientific modeling 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 modeling 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 conclude that it becomes possible in principle to quantify idealization and abstraction in informative models, although precise absolute quantification will be difficult to achieve in practice.
@book{cassini_models_2021,
address = {Cham},
series = {Logic, {Epistemology}, and the {Unity} of {Science}},
title = {Models and {Idealizations} in {Science}: {Artifactual} and {Fictional} {Approaches}},
isbn = {978-3-030-65802-1},
shorttitle = {Informative {Models}},
url = {https://doi.org/10.1007/978-3-030-65802-1_3},
abstract = {Mauricio Suárez and Agnes Bolinska apply the tools of communication theory to scientific modeling 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 modeling 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 conclude that it becomes possible in principle to quantify idealization and abstraction in informative models, although precise absolute quantification will be difficult to achieve in practice.},
language = {en},
urldate = {2021-05-29},
publisher = {Springer International Publishing},
editor = {Cassini, Alejandro and Redmond, Juan},
year = {2021},
doi = {10.1007/978-3-030-65802-1_3},
keywords = {Abstraction, Content, Idealization, Information},
}
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