But if “working” is a necessary condition for an explanation (some explanations work better than others), then won’t scientific explanations eventually become subject to the optimization drive of engineering?
Well, Kepler’s model does a good job predicting? Not perfectly, but astonishingly well.
I’m still not sure that the distinction between predictive model and explanatory model is so clear. Kepler wanted to explain the universe through the harmony of the spheres. Through that objective, he used the data to discover a beautiful and robust predictive model. Was he doing science?
Insofar as modelling is a 'predictive' activity in the engineering sense of useful estimates of observables -- it tends to end in pseudoscience.
Originally the idea of spheres was a good one (and not obtainable via any compression of measurements) -- it was obtained through reasoning by analogy. but when epicycles were added over-and-over, you effectively were using a universal functional approximator to match observable data.
Since the solar system doesn't change much, the epicycle approach works (by coincidence) -- but it's pseudoscience.
A model of gravity which can account for any possible solar system is an explanation, even if it's so hard to use we cannot actually do predictions with it (the status of much science).
