Lie, cheat, and steal: modelling advice from Stephen Ellner and John Guckenheimer:

From Chapter 9 of Ellner and Guckenheimer’s fantastic book:

Three Commandments for Modelers

The principles of model development can be summarized as three important rules: 1. Lie 2. Cheat 3. Steal.

These require some elaboration:

Lie. A good model includes incorrect assumptions. Practical models have to be simple enough that the number of parameters does not outstrip the available data. Theoretical models have to be simple enough that you can figure out what they’re doing and why. The real world, unfortunately, lacks these properties. So in order to be useful, a model must ignore some known biological details, and replace these with simpler assumptions that are literally false.

Cheat. More precisely, do things with data that would make a statistician nervous, such as using univariate data to fit a multivariate rate equation by multipli – cation of limiting factors or Liebig’s law of the minimum, and choosing between those options based on your biological knowledge or intuition. Statisticians like to let data “speak for themselves.” Modelers should do that when it is possible, but more often the data are only one input into decisions about model structure, the rest coming from the experience and subject-area knowledge of the scientists and modelers.

Steal. Take ideas from other modelers and models, regardless of discipline. Cutting-edge original science is often done with conventional kinds of models using conventional functional forms for rate equations—for example, compartment models abound in the study of HIV/AIDS. If somebody else has developed a sensible-looking model for a process that appears in your model, try it. If some – body else invested time and effort to estimate a parameter in a reasonable way, use it. Of course you need to be critical, and don’t hesitate to throw out what you’ve stolen if it doesn’t fit what you know about your system.

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