thumbnail

A model of strength

Science

By:
and
DOI: 10.1126/science.342.6155.192

Links

Abstract

In her AAAS News & Notes piece "Can the Southwest manage its thirst?" (26 July, p. 362), K. Wren quotes Ajay Kalra, who advocates a particular method for predicting Colorado River streamflow "because it eschews complex physical climate models for a statistical data-driven modeling approach." A preference for data-driven models may be appropriate in this individual situation, but it is not so generally, Data-driven models often come with a warning against extrapolating beyond the range of the data used to develop the models. When the future is like the past, data-driven models can work well for prediction, but it is easy to over-model local or transient phenomena, often leading to predictive inaccuracy (1). Mechanistic models are built on established knowledge of the process that connects the response variables with the predictors, using information obtained outside of an extant data set. One may shy away from a mechanistic approach when the underlying process is judged to be too complicated, but good predictive models can be constructed with statistical components that account for ingredients missing in the mechanistic analysis. Models with sound mechanistic components are more generally applicable and robust than data-driven models.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
A model of strength
Series title:
Science
DOI:
10.1126/science.342.6155.192
Volume
342
Year Published:
2013
Language:
English
Publisher:
Science
Contributing office(s):
Northern Prairie Wildlife Research Center
Description:
2 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Science
First page:
192
Last page:
193
Number of Pages:
2