Don’t get me wrong. Without theory, we would not know what to look for, or why. We would not make predictions about patterns in nature. I would not have known to put unique marks on wasps, or to look at genetic relatedness. I would not have known where to expect sequence variation in genomes. I would not have known to predict that in social amoebae the altruism caste is environmentally not genetically determined.Theory gives us the delight of expectations confirmed. It tells us just which stone to turn over. It allows us to advance, to generalize, to take ideas from one area and apply them to another.
Theories do not always prove to be true. Theories can be shown to be false in a variety of ways. They may be logically inconsistent. They may not fit with other theories that have been extensively shown to be true. They may not fit with the data.
The data. There’s the rub. The point of theory is to tell us how the real world works. Theories may tell you what observations to make, but once you have observed, theory will predict what patterns you will find. If the data points do not fall in the directions predicted by the theory, the theory must be rejected. Of course sample sizes need to be adequate and statistics need to be unbiased and properly applied. One study failing to support a grand theory will not torpedo it, but many such studies will.
If you tell me that your theory must be true, so you modify it slightly every time data-based studies challenge the theory, then it is not a theory. It is a religion. A theory must predict pattern in the natural world. A theory must point to kinds of data that will test it. Theory has a close relationship to data. The best theories make clear, specific predictions. Data can be collected that test the theories, either supporting or falsifying them.
This is where we get to the trouble with theory. In a way, it is really the trouble with models. Theoreticians unfettered with any knowledge of reality see only the models. The math of one may be like the math of another, and preferred for some reason or another. They may substitute purple for red because they like it better, untroubled that in making the substitution they have made their model untestable. Often these theoreticians invade from physics or math. They are amazed at the mathematical simplicity of many of our models. They do not understand why we do not soar on their mathematical balloons. They rewrite and change with facility, often ignoring that they have buried key features of biology in their models. They may write a sib-based model, but hide or ignore this feature because it is no more important to them than any other mathematical fillip. They think only in the world of models, disdaining the hard-working empiricists.
You may think I’m being too harsh, but my own field of social evolution has had more than its fair share of such people recently. How many of us have had to waste time that could be spent advancing the field on this silliness? This is not to say there are not pilgrims from math or physics that come into the field and do real good, advancing important ideas. The difference is that they pay attention to the data side of things. They think about how models might be tested. They care about testing. They care about the real, natural world. They do not have a model with nowhere to take it.
In my book, the best theoreticians are grounded in data, using it as a springboard, and as a place they can return to for truth-testing their ideas. Charles Darwin is an obvious example of such a theoretician. There are lots of others and their ideas last. They are tested. They matter.