There are two kinds of scientific revolutions, those driven by new tools and those driven by new concepts. Thomas Kuhn in his famous book, The Structure of Scientific Revolutions, talked almost exclusively about concepts and hardly at all about tools. His idea of a scientific revolution is based on a single example, the revolution in theoretical physics that occurred in the 1920s with the advent of quantum mechanics. This was a prime example of a concept-driven revolution. Kuhn’s book was so brilliantly written that it became an instant classic. It misled a whole generation of students and historians of science into believing that all scientific revolutions are concept-driven. The concept-driven revolutions are the ones that attract the most attention and have the greatest impact on the public awareness of science, but in fact they are comparatively rare. In the last 500 years, in addition to the quantum-mechanical revolution that Kuhn took as his model, we have had six major concept-driven revolutions, associated with the names of Copernicus, Newton, Darwin, Maxwell, Freud, and Einstein. During the same period there have been about twenty tool-driven revolutions, not so impressive to the general public but of equal importance to the progress of science. Two prime examples of tool-driven revolutions are the Galilean revolution resulting from the use of the telescope in astronomy, and the Crick-Watson revolution resulting from the use of X-ray diffraction to determine the structure of big molecules in biology. The effect of a concept-driven revolution is to explain old things in new ways. The effect of a tool-driven revolution is to discover new things that have to be explained…

The above quote is from Freeman Dyson’s book Imagined Worlds (49-50) where he goes after Thomas Kuhn for having too abstract a notion of how scientific advancement occurs. The idea that tool driven revolutions “explain new things that have to be explained” might be apt in the harder sciences, but applying that thinking to the social sciences might be a bit dangerous.

Political methodologists, who are keen to tell anyone who’ll listen that they have the journal with the highest impact factor, are the people in political science who develop new tools to analyze politics. They’ve created a bevy of new types of standard error, new types of estimators for data types, and new types of questions which might better tap some sort of latent dimension (for instance, how should we measure racial animosity?). Some of these things have been very useful. The addition of hazard models to political science has been a tremendous boon to those studying how policies diffuse across states or countries. What makes the useful methods useful is that they were created with an eye towards how to better answer a question with a question already in mind.

At the same time, there are many tools created by political methodologists who put the cart before the horse a little bit. That is, they create methods with the hope that other political scientists will use them to answer questions or they will create a method and incorporate a real “question” to prove the value of their method but the “question” isn’t very substantively important. In other words, some political methodologists create a new tool without fully having a “new thing that has to be explained.” In my view, this needlessly complicated political sciences, creates redundant projects which all examine the same question but with newer methods, and distracts from discovering “new things” to explain.

I think richer political science can be had if people study politics first and then seek to find the approximate best tool second to help understand the politics around them. I bet, for most questions, the tools already in existence will suffice.