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Laplace’s Demon and the Adventures of His Apprentices

Published online by Cambridge University Press:  01 January 2022

Abstract

The sensitive dependence on initial conditions (SDIC) associated with nonlinear models imposes limitations on the models’ predictive power. We draw attention to an additional limitation than has been underappreciated, namely, structural model error (SME). A model has SME if the model dynamics differ from the dynamics in the target system. If a nonlinear model has only the slightest SME, then its ability to generate decision-relevant predictions is compromised. Given a perfect model, we can take the effects of SDIC into account by substituting probabilistic predictions for point predictions. This route is foreclosed in the case of SME, which puts us in a worse epistemic situation than SDIC.

Type
Research Article
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

Work for this article has been supported by the London School of Economics’s Grantham Research Institute on Climate Change and the Environment and the Centre for Climate Change Economics and Policy funded by the Economics and Social Science Research Council and Munich Re. Frigg acknowledges financial support from the Arts and Humanities Research Council–funded Managing Severe Uncertainty project and grant FFI2012-37354 of the Spanish Ministry of Science and Innovation (MICINN). Bradley’s research was supported by the Alexander von Humboldt Foundation. Smith would also like to acknowledge continuing support from Pembroke College, Oxford. We would like to thank Wendy Parker, David Stainforth, Erica Thompson, and Charlotte Werndl for comments on earlier drafts and helpful discussions.

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