I’ve uploaded a forthcoming book chapter to the PhilSci archive. In “Confessions of a Complexity Skeptic“, I discuss some of the methodological difficulties that must be overcome by those who argue that “complexity thinking” of some sort will become, or should become, important in science (see Sandra Mitchell for a philosophical and Melanie Mitchell for a scientific take on the issue).
The paper is not a standalone piece but a commentary on another paper which will appear in the same volume (written by Prof. Max Urchs). Nevertheless, the commentary can probably be read on its own, and I think that the difficulties I discuss have some claim to generality.
My main points are these:
- Whenever we claim that the explanation of a phenomenon requires complexity thinking, we must make sure – by looking very closely at actual science – that more traditional explanations cannot handle the phenomenon. Since one of Urchs’s examples is the ineffectiveness of monetary policy during the financial crisis, I show (at only slightly tedious length) that this phenomenon can at least potentially be explained by a standard Keynesian analysis.
- When we claim that some area of science will profit from complexity thinking, it is not enough to point to an area where we currently have major gaps in our understanding, such as neuroscience. Our lack of understanding may be due to the fact that we are not sufficiently mindful of complexity. But it is also possible that we lack the appropriate mathematical methods, or that we have not yet grasped the salient aggregate-level causal factors in the system. The argument for complexity thinking requires us to show that actual problems have been solved by the approach. A promissory note is not enough.
That being said, I will follow the continuation of this debate – which, on the whole, is a bit removed from my area of research – with great interest.