Haueis, Philipp (2022) Descriptive multiscale modeling in data-driven neuroscience. [Preprint]

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Abstract

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 tech-niques can serve important exploratory functions when scientists model systems whose organi-zation 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 mul-tiscale 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 fea-tures which could be explanatorily relevant to many different types of behavior, and to deter-mine which features of a target system an upper-scale data pattern could refer to. Using mul-tiscale 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.

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Item Type:	Preprint
Creators:	Creators Email ORCID Haueis, Philipp philipp.haueis@uni-bielefeld.de 0000-0002-0935-9015
Keywords:	Multiscale modeling, explanation, descriptive modeling, neuroscience, exploratory models
Subjects:	Specific Sciences > Neuroscience > Cognitive Neuroscience General Issues > Explanation General Issues > Models and Idealization General Issues > Structure of Theories Specific Sciences > Neuroscience > Systems Neuroscience
Depositing User:	Dr. Philipp Haueis
Date Deposited:	27 Jan 2022 05:04
Last Modified:	27 Jan 2022 05:04
Item ID:	20155
Subjects:	Specific Sciences > Neuroscience > Cognitive Neuroscience General Issues > Explanation General Issues > Models and Idealization General Issues > Structure of Theories Specific Sciences > Neuroscience > Systems Neuroscience
Date:	January 2022
URI:	https://philsci-archive.pitt.edu/id/eprint/20155

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