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The epistemic benefits of generalisation in modelling II: expressive power and abstraction
This paper contributes to the philosophical accounts of generalisation in formal modelling by introducing a conceptual framework that allows for recognising generalisations that are epistemically beneficial in the sense of contributing to the truth of a model result or component. The framework is us...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer Netherlands
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8906528/ https://www.ncbi.nlm.nih.gov/pubmed/35287292 http://dx.doi.org/10.1007/s11229-022-03530-3 |
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author | Lehtinen, Aki |
author_facet | Lehtinen, Aki |
author_sort | Lehtinen, Aki |
collection | PubMed |
description | This paper contributes to the philosophical accounts of generalisation in formal modelling by introducing a conceptual framework that allows for recognising generalisations that are epistemically beneficial in the sense of contributing to the truth of a model result or component. The framework is useful for modellers themselves because it is shown how to recognise different kinds of generalisation on the basis of changes in model descriptions. Since epistemically beneficial generalisations usually de-idealise the model, the paper proposes a reformulation of the well-known distinction between abstraction and idealisation. A reformulated notion of abstraction is needed because the extant accounts yield wrong judgments when model-modifications introduce implicit assumptions. |
format | Online Article Text |
id | pubmed-8906528 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-89065282022-03-10 The epistemic benefits of generalisation in modelling II: expressive power and abstraction Lehtinen, Aki Synthese Original Research This paper contributes to the philosophical accounts of generalisation in formal modelling by introducing a conceptual framework that allows for recognising generalisations that are epistemically beneficial in the sense of contributing to the truth of a model result or component. The framework is useful for modellers themselves because it is shown how to recognise different kinds of generalisation on the basis of changes in model descriptions. Since epistemically beneficial generalisations usually de-idealise the model, the paper proposes a reformulation of the well-known distinction between abstraction and idealisation. A reformulated notion of abstraction is needed because the extant accounts yield wrong judgments when model-modifications introduce implicit assumptions. Springer Netherlands 2022-03-09 2022 /pmc/articles/PMC8906528/ /pubmed/35287292 http://dx.doi.org/10.1007/s11229-022-03530-3 Text en © The Author(s), under exclusive licence to Springer Nature B.V. 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Research Lehtinen, Aki The epistemic benefits of generalisation in modelling II: expressive power and abstraction |
title | The epistemic benefits of generalisation in modelling II: expressive power and abstraction |
title_full | The epistemic benefits of generalisation in modelling II: expressive power and abstraction |
title_fullStr | The epistemic benefits of generalisation in modelling II: expressive power and abstraction |
title_full_unstemmed | The epistemic benefits of generalisation in modelling II: expressive power and abstraction |
title_short | The epistemic benefits of generalisation in modelling II: expressive power and abstraction |
title_sort | epistemic benefits of generalisation in modelling ii: expressive power and abstraction |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8906528/ https://www.ncbi.nlm.nih.gov/pubmed/35287292 http://dx.doi.org/10.1007/s11229-022-03530-3 |
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