Modeling for understanding v. modeling for numbers
Citable URI
https://hdl.handle.net/1912/8921As published
https://doi.org/10.1007/s10021-016-0067-yAbstract
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.
Description
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.
Collections
Suggested Citation
Preprint: 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/1912/8921Related items
Showing items related by title, author, creator and subject.
-
Application of an inverse model in the community modeling effort results
Zhang, Huai-Min (Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 1995-02)Inverse modeling activities in oceanography have recently been intensified, aided by the oncoming observational data stream of WOCE and the advance of computer power. However, interpretations of inverse model results ... -
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 ... -
Short-term dispersal of Fukushima-derived radionuclides off Japan : modeling efforts and model-data intercomparison
Rypina, Irina I.; Jayne, Steven R.; Yoshida, Sachiko; Macdonald, Alison M.; Douglass, Elizabeth M.; Buesseler, Ken O. (Copernicus Publications on behalf of the European Geosciences Union, 2013-07-24)The Great East Japan Earthquake and tsunami that caused a loss of power at the Fukushima nuclear power plants (FNPP) resulted in emission of radioactive isotopes into the atmosphere and the ocean. In June of 2011, an ...