Fuck Nuance [pdf]

Yes, we need theories that are easy enough to reason about that they provide useful insight, but I don't think this is even close to being too complicated.

On Physics, beyond the obvious, the rest can be derided as noise (you won't have an exact measure of anything, sometimes this is a new phenomenon yet to be discovered, or something known but overlooked in this situation, but usually "noise")

Now when you depend on more variables, your first guess might only predict the result 60% of the time (as an example). Better than random choice, but still has a low predictability. Then you add more "obvious" variables and can result in, 80% prediction, which is good, but not as good as you can get with physics.

This can be social sciences or even something more hard as biology or medicine.

In the end it's an issue of overfitting/underfitting

Actually, I see two problems with this notion.

First, there's no "paradox". That "no amount or theory or symbolism will produce a real apple" is not paradoxical. If anything, it's common sense.

Second, this common sense might very well be wrong (and that would be a real paradox this time).

In fact, what you call "real apple" might itself be a simulation (along with all the universe we know) -- created by higher order beings.

Still, you might say, our theories, us being inhabitants of the (simulated) universe, won't produce a real apple for us.

But who's to say that? It depends on the rules of the simulated universe. And we can't say with 100% certainty we know the rules of ours (if it's one such).

E.g. the rules could very well allow the thoughts and symbolism of its inhabitants to create new objects in the universe.

In that sense, the universe would be like Lisp (where data are code), or like using "reflection".

This is assuming that the non-fitting data is not ignored, if it is known to the theorist at all. If anything, the bigger problem among social science amateurs is obliviousness to data or lack of intuition to understand how biased collected data is simply because they're unaware of historical data. Hence, they come up with theories like x = t.

> there is only one valid response: "your theory predicts X but experiment shows Y."

Sure, except that the falsifying experiment took place 890 years ago, you likely don't know about it, and if you did, you'd find excuses (possibly good ones) for dismissing it as irrelevant. The problem is, of course, that experiments are very hard to conduct, and all-but-impossible to conduct under lab conditions.

The reason intuition is so important is that we know with absolute certainty that some things that seem so natural and stable now, used to be diametrically different, and they changed because people changed them. If you know from experience that 80% of coefficients are known to vary considerably over time and as a result of human action, you tend to be very skeptical when someone claims they have found a "natural law" of human society.

> So is blaming scientists with simple theories for "atrocities".

Perhaps it's ad hominem, but it really happened and more than once (except the atrocities were real and required no quotation marks). I don't know what else to tell you.

> They make testable predictions which accurately predict the world

Maybe, and maybe they theorize that x = t when, in fact, x" = 0. Showing that x = -t may also be a valid solution requires making huge changes, among them changing the perception that the theories are correct, which in itself perpetuates the path of the system. The only thing you can do is point to historical trends, but historians, most of them ever so careful, just sigh in exasperation, while the masses believe the "science". Just today I read something wonderful about perception of the sexes[1], yet I believe many might find simple models that perfectly predict the world they see around them, not realizing it is no more than the result of people who made it this way so-and-so years ago, and they can make it different yet again.

It's like having a reverse Cassandra complex only in reverse, and it can be just as frustrating.

Do you have any doubts that Netflix's prediction engine shapes people preferences more than it passively predicts them? I am not saying that it doesn't suggest movies people enjoy, only that it could have made completely different suggestions that people may have enjoyed even more. How do you measure the effectiveness of this algorithm against all others (and there are way more possible algorithms that can be tested with the sample they have)? Or do you just declare it successful and accurate?

[1]: https://www.reddit.com/r/AskHistorians/comments/4c5l63/in_th...