Modeling for understanding v. modeling for numbers
Rastetter, Edward B.
MetadataShow full item record
I draw a distinction between Modeling for Numbers, which aims to address how much, when, and where questions, and Modeling for Understanding, which aims to address how and why questions. For-numbers models are often empirical, which can be more accurate than their mechanistic analogues as long as they are well calibrated and predictions are made within the domain of the calibration data. To extrapolate beyond the domain of available system-level data, for-numbers models should be mechanistic, relying on the ability to calibrate to the system components even if it is not possible to calibrate to the system itself. However, development of a mechanistic model that is reliable depends on an adequate understanding of the system. This understanding is best advanced using a for-understanding modeling approach. To address how and why questions, for-understanding models have to be mechanistic. The best of these for-understanding models are focused on specific questions, stripped of extraneous detail, and elegantly simple. Once the mechanisms are well understood, one can then decide if the benefits of incorporating the mechanism in a for-numbers model is worth the added complexity and the uncertainty associated with estimating the additional model parameters.
Author Posting. © The Author(s), 2016. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Ecosystems 20 (2017): 215-221, doi:10.1007/s10021-016-0067-y.
Suggested CitationPreprint: Rastetter, Edward B., "Modeling for understanding v. modeling for numbers", 2016-11, https://doi.org/10.1007/s10021-016-0067-y, https://hdl.handle.net/1912/8921
Showing items related by title, author, creator and subject.
Desert dust and anthropogenic aerosol interactions in the Community Climate System Model coupled-carbon-climate model Mahowald, Natalie M.; Lindsay, Keith; Rothenberg, D.; Doney, Scott C.; Moore, J. Keith; Thornton, Peter E.; Randerson, James T.; Jones, C. D. (Copernicus Publications on behalf of the European Geosciences Union, 2011-02-15)Coupled-carbon-climate simulations are an essential tool for predicting the impact of human activity onto the climate and biogeochemistry. Here we incorporate prognostic desert dust and anthropogenic aerosols into the ...
Model behavior and sensitivity in an application of the Cohesive Bed Component of the Community Sediment Transport Modeling System for the York River estuary, VA, USA Fall, Kelsey A.; Harris, Courtney K.; Friedrichs, Carl T.; Rinehimer, J. Paul; Sherwood, Christopher R. (MDPI AG, 2014-05-19)The Community Sediment Transport Modeling System (CSTMS) cohesive bed sub-model that accounts for erosion, deposition, consolidation, and swelling was implemented in a three-dimensional domain to represent the York River ...
Recent advances in Arctic ocean studies employing models from the Arctic Ocean Model Intercomparison Project Proshutinsky, Andrey; Aksenov, Yevgeny; Kinney, Jaclyn Clement; Gerdes, Rudiger; Golubeva, Elena; Holland, David; Holloway, Greg; Jahn, Alexandra; Johnson, Mark; Popova, Ekaterina E.; Steele, Michael; Watanabe, Eiji (Oceanography Society, 2011-09)Observational data show that the Arctic Ocean has significantly and rapidly changed over the last few decades, which is unprecedented in the observational record. Air and water temperatures have increased, sea ice volume ...