alt Hacker NewsA bit of caution with this line of thinking, because I was blindsided by it after more than decade of hand waving away "academics"
When you have the mind of "the models are wrong", you tend to be brought there by select counter examples, and in that space you only feed on counter examples. "The experts are wrong (and you can be right in 15 minutes if you consume this media!)" is abundant.
The actual reality is "the models work great until they don't". Not "the models don't work."
Ironically, the experts, the ones building the "wrong" models, tend to be the ones most aware of this.
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It's because the models have different purposes in decision making, in ways which decision makers don't often appreciate.
Models are still great at helping us understand the world and are in many ways the best thing we have. The problem is that today we are overly relying on them to make real policy interventions on an ecology (e.g. what is a "healthy" amount of animals to cull or fish) based on an ecosystem, which is just a poor model.
If we could get rid of this idea of "stability" and "equilibrium" in economics and ecology, I would be a happy man.
> Ironically, the experts, the ones building the "wrong" models, tend to be the ones most aware of this
Exactly why I'm here :-)
“Models work great” - you are in an infinite loop of recursion when you shift your frame from “Don’t trust the experts” critic to “Models work until falsified”.
This doesn’t solve your problem of who decides it is? Or isn’t?
I like the former argument that you should question “experts”.
After all, why use the “Trust the experts model” at all? Which experts? How many? What is a model? What does “works” mean? What not? And who decides about the decision-making process itself?
The former poster has merits because it is a battle of models: and again, research and new findings must not be tied to obscure academic titles.
Two academics of the same level come to opposite conclusions - now what?
And a scoring system still doesn’t change the fact that nature doesn’t care about anthropomorphic models.
I studied law, math and computer science and I was especially disappointed by mathematics, after all this is an anthropological abstraction by human beings and Cantor is the best example of shunning a man’s work, which was ground breaking, just because academic consensus opted against him and didn’t accept his work.
Only many year after his death his system became a standard that appears so clear that you wonder what was wrong with the committee back then.
So mathematics is far from being this strict, stringent and logical profession, as it is branded. Instead there are human beings using a model that abides to certain rules.
Useful, but no proof at all, whether there is a better one, like with Newton and Einstein.
Cantor by the way quit mathematics due to the rejection of his work, which was accompanied by hostility.
You be the judge here.
> When you have the mind of "the models are wrong", you tend to be brought there by select counter examples, and in that space you only feed on counter examples.
Box's (possibly apocryphal) aphorism, "all models are wrong, but some are useful", is the better mindset.
Obviously we can never perfectly model nature, but we can often get close enough to form useful predictions. A minor predictive failure does not necessarily mean you throw the model, and all of its predictions, out entirely.
> "The experts are wrong (and you can be right in 15 minutes if you consume this media!)" is abundant.
Social media, sadly, preys on the feeble-minded or willingly-deceived :(
I was always a fan of what a former senior colleague ingrained in me early in my career:
- a model is primarily judged by its predictive power
Most anti-science criticism I see is completely ignorant of what it really means to model a system or phenomenon: the challenges, the limitations, the end goal, the criteria for success, etc. It's sort of a statistical / scientific ignorance that's deeply cross-discipline. To perform science in a lot of ways is to engage with what it means to build a model, and in some ways, interrogate the universe.
If you're both an idiot and overconfident, it's tempting to throw your hands up and not engage with something as complex as "modeling", and just decide almost out of whimsy what the universe should truly be. But at least some people who find modeling too intellectually taxing at least admit that there are things that they'll never understand.
Same thing said by Isaac Asimov in 1986: "The Relativity of Wrong"
https://web.williams.edu/Mathematics/sjmiller/public_html/23...
> Ironically, the experts, the ones building the "wrong" models, tend to be the ones most aware of this.
This varies substantially by field, or by subfield. There is a vast decades-long intellectual wasteland of bad economics predicated on bad models that are only appealing on normative grounds. The entire field of behavioral psychology might be a scam. Etc etc. Science advances one funeral at a time hard part because people are unwilling to let go of their models, even when they have outlived their usefulness or have been simply proven too wrong to be useful even in their original purpose.