Descriptive multiscale modeling in data-driven neuroscience

Haueis P (2022)
Synthese: An International Journal for Epistemology, Methodology and Philosophy of Science 200(2): 129.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Abstract / Bemerkung
Multiscale modeling techniques have attracted increasing attention by philosophers of science, but the resulting discussions have almost exclusively focused on issues surrounding explanation (e.g., reduction and emergence). In this paper, I argue that besides explanation, multiscale techniques can serve important exploratory functions when scientists model systems whose organization at different scales is ill-understood. My account distinguishes explanatory and descriptive multiscale modeling based on which epistemic goal scientists aim to achieve when using multiscale techniques. In explanatory multiscale modeling, scientists use multiscale techniques to select information that is relevant to explain a particular type of behavior of the target system. In descriptive multiscale modeling scientists use multiscale techniques to explore lower-scale features which could be explanatorily relevant to many different types of behavior, and to determine which features of a target system an upper-scale data pattern could refer to. Using multiscale models from data-driven neuroscience as a case study, I argue that descriptive multiscale models have an exploratory function because they are a sources of potential explanations and serve as tools to reassess our conception of the target system.
Stichworte
Multiscale modeling; Explanation; Descriptive modeling; Neuroscience; Exploratory models
Erscheinungsjahr
2022
Zeitschriftentitel
Synthese: An International Journal for Epistemology, Methodology and Philosophy of Science
Band
200
Ausgabe
2
Art.-Nr.
129
ISSN
0039-7857
eISSN
1573-0964
Page URI
https://pub.uni-bielefeld.de/record/2962692

Zitieren

Haueis P. Descriptive multiscale modeling in data-driven neuroscience. Synthese: An International Journal for Epistemology, Methodology and Philosophy of Science . 2022;200(2): 129.
Haueis, P. (2022). Descriptive multiscale modeling in data-driven neuroscience. Synthese: An International Journal for Epistemology, Methodology and Philosophy of Science , 200(2), 129. https://doi.org/10.1007/s11229-022-03551-y
Haueis, P. (2022). Descriptive multiscale modeling in data-driven neuroscience. Synthese: An International Journal for Epistemology, Methodology and Philosophy of Science 200:129.
Haueis, P., 2022. Descriptive multiscale modeling in data-driven neuroscience. Synthese: An International Journal for Epistemology, Methodology and Philosophy of Science , 200(2): 129.
P. Haueis, “Descriptive multiscale modeling in data-driven neuroscience”, Synthese: An International Journal for Epistemology, Methodology and Philosophy of Science , vol. 200, 2022, : 129.
Haueis, P.: Descriptive multiscale modeling in data-driven neuroscience. Synthese: An International Journal for Epistemology, Methodology and Philosophy of Science . 200, : 129 (2022).
Haueis, Philipp. “Descriptive multiscale modeling in data-driven neuroscience”. Synthese: An International Journal for Epistemology, Methodology and Philosophy of Science 200.2 (2022): 129.

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