https://www.wsws.org/en/articles/2020/03/18/covi-m18.html
https://www.nature.com/articles/d41586-020-01003-6
https://www.imperial.ac.uk/news/196496/coronavirus-pandemic-...
The model authors have since argued that the data was correct, but people responded to the pandemic by changing the way we live. That's OP point: that feedback cycles and corrections exist and they make modeling dynamic systems very difficult.
"Lots of people could die if you keep behaving as you currently are"
"Okay, lets behave differently"
And then less people die.
Trying to frame it as "they modelled it wrong" is nonsense. What even is the point of predictions like this if not to change behaviour - predicting outcomes based on everyone taking precautions and not telling people what might happen if they don't would be dangerous and irresponsible.
It's not that. It's that when the system you model responds to the existence of your model, it becomes anti-inductive. It's no longer like weather, but is now like the stock market[0]. Your model suddenly can't predict the system anymore[2], it can at best determine its operating envelope by estimating the degree to which the system can react to the existence of the model.
--
[0] - I use the term anti-inductive per LessWrong nomenclature[1], but I've also been reading "Sapiens" by Yuval Noah Harari, and there he uses terms "first order chaotic" for systems like weather, and "second order chaotic" for systems like the stock market.
[1] - Introduced in https://www.lesswrong.com/posts/h24JGbmweNpWZfBkM/markets-ar....
[2] - I think it becomes uncomputable in Turing sense, but I'm not smart enough to reduce this to the Halting Problem.
https://en.wikipedia.org/wiki/Lucas_critique
and also https://en.wikipedia.org/wiki/Campbell%27s_law
https://en.wikipedia.org/wiki/Goodhart%27s_law
Edit: I think this is not the first time the good people from lesswrong dug up some well known idea and gave it a new name. Good thing, too, giving this important concept more attention. Too often we forget how many people have dealt with the problem of modeling complex systems in the past. And while we can not read everything, it's often a good idea to have at least a glance at where they failed!
Too often I read/review some new "revolutionary" paper based on the idea that hey, we can model this process (involving people) like XYZ from physics, where this stuff works great! Surely, this is better than the plebian approaches in the literature! And then, to the shock of all involved, it doesn't work great....
Also: https://xkcd.com/793/
And even their best case scenarios overshot the mark -- and by a lot.
This isn't to criticize modeling -- it's only to point out how hard it is to get right.
> The models tended to overshoot the number of deaths by huge amounts. For example, the Imperial College of London estimated 40m deaths in 2020 instead of the 2m that occurred.
The very article you cited pointed out that the 40m figure was based on a "left unchecked" scenario. It was not an attempt to predict the actual number of deaths that would occur. Claiming that this is indicative of overshooting because the actual number of deaths is 2m is completely wrong.
Even a cursory glance at the actual data, even the data available at the time, shows they were completely and utterly wrong.
Were they actually so naïve that their model did not allow for the possibility that human beings change their behavior in fear of death by pandemic?
Of course there are many ways that things went wrong, and not everyone made the same mistakes.
This is the same reason as to why Econophysics, despite all its promise and grandeur, has yet to yield any useful results.
Interdependent human beings are a lot more complex entities than particles or flows.
Isn't it interesting how often one reads about science in the press that seems more geared towards generating press releases than useful results?
This hasn’t always been the case so it’s fair to consider what circumstances might foster a better situation, where research can be directed to areas most promising to add progress and value to society at large.
Consider palæontology as an entire field; there is no financial benefit nor practical application to be had for it, yet it seems to find ways to attract funding all the same, most likely because it does have a habit of generating spectacular news articles which sponsors would probably enjoy the publicity of.
But there is truly no practical benefit for mankind to be had in trying to answer to what extent various dinosaur species were endothermic and feathered.
Except we’re an insatiably curious species and it sates our curiosity.
The image of dinosaurs that became canonically entrenched in popular culture is almost certainly completely wrong, but the truth is of little consequence, exactly because it is not used for anything that might depend on it's veracity.
It really does not matter whether it be accurate or completely false, for this purpose.
In particular, this is a notoriously poor approach to modeling complex large-scale interactions between humans and their environments. There was a study I was involved in last year to determine why one of the epidemiological models for COVID was so badly off target. The root cause was modeling human behavior in the same way you would model weather, which is quite inappropriate but the implementors of the epidemiological model did not have the expertise to know better.
The selection of GPUs is also not appropriate for the nominal objectives of the program. When modeled correctly, i.e. not as weather, these kinds of things aren't the kind of workload GPUs are good at. They tend to look more like very high dimensionality sparse graphs -- latency-hiding is more important than computational throughput. CPUs are actually quite good at this.
This looks more like a program more designed to produce press releases than useful results.