Modeling for Understanding v. Modeling for Numbers
Summary (1 min read)
Summary
- 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 address how and why questions, for-understanding models have to be mechanistic.
- The use of these models is clearly important; they address pressing environmental issues and attract a large amount of research money and effort.
- The emphasis of modeling for understanding is to understand underlying mechanisms, often by stripping away extraneous detail and thereby sacrificing quantitative accuracy.
- Answers to how much, where, and when can frequently be found based on past experience using purely empirical or statistical models.
- Such models have been used for thousands of years, for example, to know when to sow crops (e.g., after the Nile flood; Janick 2002).
- Modern science relies on non-mechanistic models in many ways.
- To assess the medical risk of smoking, LaCroix and others (1991) followed 11,000 individuals, 65 years of age or older, for five years to quantify the relationship between mortality rates and smoking.
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"Modeling for Understanding v. Model..." refers background in this paper
...At some level, ‘‘all models are wrong but some are [nevertheless] useful’’ (Box 1979)....
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"Modeling for Understanding v. Model..." refers background or methods in this paper
...The most influential of these next-generation models is one developed by MacArthur and Levins (1964) and further developed and applied especially by Tilman (1977, 1980): dBi dt ¼ gi Bi min p j¼1 Rj kij þ Rj mi Bi ð2Þ dRj dt ¼ Sj Xn i¼1 qij gi Bi min p k¼1 Rk kik þ Rk ð3Þ where Bi is the biomass of…...
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...Generally, these models are tested in very controlled environments where extraneous factors can be minimized (for example, chemostats, pot studies; Gause 1934;Ayala and others 1973; Tilman 1977)....
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...How does one test an abstraction? Generally, these models are tested in very controlled environments where extraneous factors can be minimized (for example, chemostats, pot studies; Gause 1934;Ayala and others 1973; Tilman 1977)....
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...Upper panel—Lotka (1925) and Volterra (1926) model (equation 1) with ri = 0....
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