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Explanatory Models Versus Predictive Models: Reduced Complexity Modeling in Geomorphology

  • Alisa Bokulich
Conference paper
Part of the The European Philosophy of Science Association Proceedings book series (EPSP, volume 2)

Abstract

Although predictive power and explanatory insight are both desiderata of scientific models, these features are often in tension with each other and cannot be simultaneously maximized. In such situations, scientists may adopt what I term a ‘division of cognitive labor’ among models, using different models for the purposes of explanation and prediction, respectively, even for the exact same phenomenon being investigated. Adopting this strategy raises a number of issues, however, which have received inadequate philosophical attention. More specifically, while one implication may be that it is inappropriate to judge explanatory models by the same standards of quantitative accuracy as predictive models, there still needs to be some way of either confirming or rejecting these model explanations. Here I argue that robustness analyses have a central role to play in testing highly idealized explanatory models. I illustrate these points with two examples of explanatory models from the field of geomorphology.

Keywords

Sediment Transport Explanatory Model Robustness Analysis Modeling Goal Quantitative Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

I would like to express my deep gratitude to Brad Murray for stimulating discussions about these issues and for generously sharing his expertise with me.

I am also grateful to the National Science Foundation, grant 1230676, for their support of this research.

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Department of PhilosophyBoston UniversityBostonUSA

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