Explanation vs Prediction - lydgate/mindmeld GitHub Wiki
Science as focused on EXPLANATION vs PREDICTION
- Read Clifford Geertz "Blurred Genres" (1980): "Science owes more to the steam engine than the steam engine owes to science."
- Darwin vs Calvin: The Human Error Darwin Inspired - Issue 90: Something Green - Nautilus
- Krakauer podcast (relevant text below)
- Adrian Curie:
- Per Bak: "Complexity, contingency, and criticality.":
- Many, many directions to go with this, but one jumping off point is that complexity science has as a basic assumption that science is (or should be) focused on explanation and discovery rather than prediction and strict hypothesis-testing
Krakauer:
Yeah. So there's two issues here. Right, to answer this properly we have to understand what complexity is, and complexity is this domain of reality that straddles the very regular and the random. And science has been really good at those two limits, right? And so one limit, classical mechanics, and the other limit, statistical mechanics. And both are powerful theories, one dealing with, if you like, crystals and the other one dealing with gases. The perfectly ordered and the very disordered. And in the middle is where it all gets complicated and complex, and that's where we live at SFI.
Now, what that's done, because science is not very good there, historically, is generated two possible approaches. One of them is complexity science and one of them is machine learning and AI, and they do different things. Machine learning and AI takes all that complexity in, encodes it in big models like deep neural networks, and makes predictions, but those predictions are completely opaque and don't give anyone an understanding as to how they were reached. On the other hand, you have complexity science, which tries to, in Murray’s language, take “a crude look at the whole.” It tries to find the right scale at which you can do theory of these adaptive systems, if you like, in the center, with a view to not producing perfect predictions, but generating real insight, explanation for why they exist.
And I think we're now entering in the 21st century, a new kind of scientific schism where we're going to live with two very different ways of engaging with reality. A machine-based, high-dimensional, very precise predictive framework that is a black box … and ours, which is a more familiar framework from the history of science, if you like, but that is faithful to the complexity of the systems we study, which doesn't predict so well, but does allow us to understand the basic mechanisms generating the phenomena of interest. And that's where I think complexity lives, and it's going to have to come to terms with living with machine learning and AI. It's almost as if we've returned, to use your biblical metaphors, to the Cain and Abel, and those two brothers are going to have to get on as opposed to one killing the other.