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Systems Science and the Art of Interdisciplinary Integration

Green, Sara and Andersen, Hanne (2019) Systems Science and the Art of Interdisciplinary Integration. [Preprint]

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Abstract

Systems sciences address issues that cross-cut any single discipline and benefit from the synergy of combining several approaches. But interdisciplinary integration can be challenging to achieve in practice. Scientists with different disciplinary backgrounds often have different views on what count as good data, good evidence, a good model, or a good explanation. Accordingly, several scholars have reported on challenges encountered in interdisciplinary settings. This chapter outlines how some of the challenges play out in systems biology where disciplinary ideals and domain-specific practices sometime collide. We focus on tensions arising due to differences in epistemic standards between modellers with a background in physics or systems engineering, on one hand, and experimenters with a background in molecular biology on the other. We propose that part of the problem of interdisciplinary integration can be understood as the result of unfounded “disciplinary imperialism” on both sides, in which standards from one discipline are uncritically applied to new domains without recognition of other valid or complementary perspectives. Addressing and explicating the disciplinary background for the different views can help facilitate interdisciplinary collaboration in science and improve science education.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Green, Sarasaraehrenreichgreen@gmail.com0000-0001-7011-7202
Andersen, Hanne
Keywords: Interdisciplinarity; Systems science; Systems biology; Disciplinary imperialism; Epistemic standards
Subjects: Specific Sciences > Biology
General Issues > Explanation
General Issues > Models and Idealization
General Issues > Science Education
Depositing User: Dr. Sara Green
Date Deposited: 01 Nov 2019 03:08
Last Modified: 01 Nov 2019 03:08
Item ID: 16593
Subjects: Specific Sciences > Biology
General Issues > Explanation
General Issues > Models and Idealization
General Issues > Science Education
Date: 2019
URI: http://philsci-archive.pitt.edu/id/eprint/16593

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