Bring digital twins back to Earth
Bring digital twins back to Earth
We reflect on the development of digital twins of the Earth, which we associate with a reductionist view of nature as a machine. The projects of digital twins deviate from contemporary scientific paradigms in the treatment of complexity and uncertainty, and does not engage with critical and interpretative social sciences. We contest the utility of digital twins for addressing climate change issues and discuss societal risks associated with the concept, including the twins' potential to reinforce economicism and governance by numbers, emphasizing concerns about democratic accountability. We propose a more balanced alternative, advocating for independent institutions to develop diverse models, prioritize communication with simple heuristic-based models, collect comprehensive data from various sources, including traditional knowledge, and shift focus away from physics-centered variables to inform climate action. We argue that the advancement of digital twins should hinge on stringent controls, favoring a nuanced, interdisciplinary, and democratic approach that prioritizes societal well-being over blind pursuit of computational sophistication. This article is categorized under: Climate Models and Modeling > Earth System Models Climate Models and Modeling > Knowledge Generation with Models Climate, History, Society, Culture > Disciplinary Perspectives.
digital twins of the Earth system, ethics of quantification, machine learning, mathematical modeling, sociology of quantification
Saltelli, Andrea
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Gigerenzer, Gerd
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Hulme, Mike
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Katsikopoulos, Konstantinos V.
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Melsen, Lieke A.
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Peters, Glen P.
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Pielke, Roger
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Robertson, Simon
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Stirling, Andy
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Tavoni, Massimo
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Puy, Arnald
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Saltelli, Andrea
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Gigerenzer, Gerd
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Hulme, Mike
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Katsikopoulos, Konstantinos V.
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Melsen, Lieke A.
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Peters, Glen P.
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Pielke, Roger
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Robertson, Simon
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Stirling, Andy
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Tavoni, Massimo
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Puy, Arnald
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Saltelli, Andrea, Gigerenzer, Gerd, Hulme, Mike, Katsikopoulos, Konstantinos V., Melsen, Lieke A., Peters, Glen P., Pielke, Roger, Robertson, Simon, Stirling, Andy, Tavoni, Massimo and Puy, Arnald
(2024)
Bring digital twins back to Earth.
Wiley Interdisciplinary Reviews: Climate Change.
(doi:10.1002/wcc.915).
Abstract
We reflect on the development of digital twins of the Earth, which we associate with a reductionist view of nature as a machine. The projects of digital twins deviate from contemporary scientific paradigms in the treatment of complexity and uncertainty, and does not engage with critical and interpretative social sciences. We contest the utility of digital twins for addressing climate change issues and discuss societal risks associated with the concept, including the twins' potential to reinforce economicism and governance by numbers, emphasizing concerns about democratic accountability. We propose a more balanced alternative, advocating for independent institutions to develop diverse models, prioritize communication with simple heuristic-based models, collect comprehensive data from various sources, including traditional knowledge, and shift focus away from physics-centered variables to inform climate action. We argue that the advancement of digital twins should hinge on stringent controls, favoring a nuanced, interdisciplinary, and democratic approach that prioritizes societal well-being over blind pursuit of computational sophistication. This article is categorized under: Climate Models and Modeling > Earth System Models Climate Models and Modeling > Knowledge Generation with Models Climate, History, Society, Culture > Disciplinary Perspectives.
Text
WIREs Climate Change - 2024 - Saltelli - Bring digital twins back to Earth
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Accepted/In Press date: 24 July 2024
e-pub ahead of print date: 26 August 2024
Keywords:
digital twins of the Earth system, ethics of quantification, machine learning, mathematical modeling, sociology of quantification
Identifiers
Local EPrints ID: 495382
URI: http://eprints.soton.ac.uk/id/eprint/495382
ISSN: 1757-7780
PURE UUID: 02d2180d-0969-40b3-a22c-bbf51c96446f
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Date deposited: 12 Nov 2024 17:41
Last modified: 13 Nov 2024 02:52
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Contributors
Author:
Andrea Saltelli
Author:
Gerd Gigerenzer
Author:
Mike Hulme
Author:
Lieke A. Melsen
Author:
Glen P. Peters
Author:
Roger Pielke
Author:
Simon Robertson
Author:
Andy Stirling
Author:
Massimo Tavoni
Author:
Arnald Puy
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