Deriving models that are simple, but not too simple

This picture of Albert Einstein is from Wikipedia and is in the US public domain

In the first few pages of A Biologist’s Guide to Mathematical Modeling in Ecology and Evolution by Sally Otto and Troy Day they paraphrase Albert Einstein (pg 7) who said:

“Everything should be made as simple as possible, but no simpler”


After I give a talk, I am often asked questions such as: “you assumed that space is homogeneous, but isn’t there a mountain range to the west?” or “could you expand your model to consider the influence of hunting on adult wolf survivorship?” And for a split second this thought races through my head: They’re right. I am wrong. My work is wrong! This is terrible, I must add in hunting to fix it.

It is tempting to think that a more complex model is better. Will other scientists assume that I aren’t skilled enough to include hunting in the model? Will they not understand that this was my deliberate choice – the choice not to include it?

As a final comment, if some asks “could you expand your model to consider the effect of climate change?” at the end of one of my talks, I will return the question by asking what they think would change if I had explicitly included this. The question above, without further elaboration, could imply that I didn’t include climate change because I overlooked it. Returning the question helps to draw attention to the challenges that modellers face and to highlight the types of careful considerations that go into model construction.

This entry was posted in Just simple enough by Amy Hurford. Bookmark the permalink.

About Amy Hurford

I am a theoretical biologist. I became aware of mathematical biology as an undergraduate when I conducted an internet search to learn about the topic. Now, twelve years later, I want to know, what is it that makes great models great? This blog is the chronology of my thoughts as I explore this topic.

3 thoughts on “Deriving models that are simple, but not too simple

  1. A related question is how you arrive at the simple model. Do you start *really* simple and then add stuff? Or start really complex and then try to boil things down? What are the advantages and drawbacks of each approach?

  2. I’ve had smart people argue for both approaches to me. Earl Werner, for instance, once told me that he prefers to start complex and then simplify because he’s worried that otherwise he’ll add complexities in an unrealistic way. He wants to start with all the known biology he can in order to be sure that, when he simplifies, he’s not somehow distorting any of the biology. I mention that only because I think most theoreticians prefer to go the opposite way.

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