Very nice to see.
"3. Ontology. Ontology is the philosophy of what existence means.
My ontology is basically what I think of as "pragmatist"...we believe
things because it's useful to believe them. If you disbelieve in the
existence of a wall, you're going to stub your toe and it's going to be
unpleasant. Or maybe not...try disbelieving in the wall and let me know
the result. I'll be over here with a beer, getting your experiment on
video. That's basically my philosophy of what "existence" means. One
result of this outlook is that I think of "detectability" (or
"observability") is the same thing as "existence"...if you can't in some
way, however indirectly, stub your toe on something, it might as well
not exist. I don't know if that's what other people mean when they say
"pragmatism", but see Point 1 about language."
After that he gets into epistemology. I think those thoughts are essentially pragmatist and could be improved by becoming explicitly pragmatist. As Richard Rorty noted, the label "truth" is a compliment paid to sentences that pay their way (i.e. - that are useful). The discussion of epistemology is still good, of course.
On his statement on the goals of science, I would emphasize simply knowing about the world. Science provides benefits by giving us power over reality, it's true. But you don't need science to do that. And a lot of science is just done to gain a deeper understanding of the world, and if practical outcomes come out of it at some point it certainly can't be said to be a goal of science. So I'd put those more on par. I'm not sure this has anything to do with the "pleasure of scientists". A better understanding of the world is a goal in its own right. Certainly some discoveries are going to be unpleasant, after all!
I agree on point 6 - the discussion of scientific models. In economics especially we are dealing with multiple simultaneous processes. Models illustrate these processes. Of course in isolation such a picture is never complete, but that misses the point. It helps us understand the process of interest. If your model doesn't do that it's not of much use.
I agree with a lot of his point 7 - the techniques of science. On 7b (evidence), I would emphasize science as an explanatory endeavor rather than just a predictive endeavor. Too much focus on prediction in science has lead to the overemphasis of falsification that Noah (rightly) criticizes.
In his point 9, he shares several unhelpful views of science. I want to agree with and expand on his criticism of this one: "
"Science does not always need evidence; sometimes, we can start from
judgment and proceed by logic to a conclusion, and then accept that
conclusion without checking it against evidence." This is like when the evil wizard tries to win by turning himself into a snake...it never works."
The reason why "this never works" is the frailty of the human mind. Logic is nice to keep you more or less coloring inside the lines. But we can't rely solely on logic in explaining a complex phenomenon like reality unless we know all of the fundamental, axiomatic forces dictating reality AND some initial value or state of reality. This is true of any science. Pure logic can only guide you in physics if you take into account all the fundamental forces and an initial value. No physicists knows this, of course. We have good guesses, and so we do make models like this that simulate the evolution of a universe like ours. But you can't recreate our universe or our solar system without perfect knowledge to logically build from. The same goes for economies or anything else. Our knowledge is always imperfect and so our simulations will always deviate from actual reality.
So the question is - how confident are you in your understanding of the laws of the universe and some initial value of the universe? Is it good enough to rely on logic alone? How would you know if it were? Nobody's knowledge is that good. Indeed nobody could have any sense of whether their knowledge was that good without appealing to evidence. So you always have to check how close your logic is to the actual data and then evaluate whether your logic really worked or not. This is the problem with the praxeological tendencies in the Austrian school.
I very much agree with his last point on judgement and theory in macroeconomics too.