Royal Society cautions against censorship of scientific misinformation online

This is the justification the academics used - because we're modelling probability distributions, bugs don't matter. Sorry, but no, this is 100% wrong. Doing statistics is not a get-out-of-jail-free card for arbitrary levels of bugginess.

Firstly, the program wasn't generating a probability distribution as you claim. It produced a single set of numbers on each run. To the extent the team generated confidence intervals at all (which for Report 9 I don't think they did), it was by running the app several times and then claiming the variance in the results represented the underlying uncertainty of the data, when in reality it was representing their inability to write code properly.

Secondly, remember that this model was being used to drive policy. How many hospitals shall we build? If you run the model and say 10, and then someone makes the graph formatting more helpful, reruns it and it now it says 4, that's a massive real-world difference. Nobody outside of academia thinks it's acceptable to just shrug and say, well it's just probability so it's OK for the answers to just wildly thrash around like that.

Thirdly, such bugs make unit testing of your code impossible. You can't prove the correctness of a sub-calculation because it's incorporating kernel scheduling decisions into the output. Sure enough Ferguson's model had no functioning tests. If it did, they might have been able to detect all the non-threading related bugs.

Finally, this "justification" breeds a culture of irresponsibility and it's exactly that endemic culture that's destroying people's confidence in science. You can easily write mathematical software that's correctly reproducible. They weren't able to do it due to a lack of care and competence. Once someone gave them this wafer thin intellectual sounding argument for why scientific reproducibility doesn't matter they started blowing off all types of bugs with your argument, including bugs like out of bounds array reads. This culture is widespread - I've talked to other programmers who worked in epidemiology and they told me about things like pointers being accidentally used in place of dereferenced values in calculations. That model had been used to support hundreds of papers. When the bugs were pointed out, the researchers lied and claimed that in only 20 minutes they'd checked all the results and the bugs had no impact on any of them.

Once a team goes down the route of "our bugs are just CIs on a probability distribution" they have lost the plot and their work deserves to be classed as dangerous misinformation.

"My view on criticisms of the model were that it was more politically motivated"

Are you an academic? Because that's exactly the take they like to always use - any criticism of academia is "political" or "ideological". But expecting academics to produce work that isn't filled with bugs isn't politically motivated. It's basic stuff. For as long as people defend this obvious incompetence, people's trust in science will correctly continue to plummet.

"Here's John Cormack's take on the model, he saw and cleaned up the original version before it was released. https://mobile.twitter.com/id_aa_carmack/status/125487236876..."

If you check the commit history, you'll see that he quite obviously didn't work with the code much at all. Regardless, if he thinks the model is not worthless, he's wrong. Anyone who reviews their bug tracker can see that immediately.

And little seems said, but I can't help notice the quite communist behaviour to demand the state mandate 'choices' for the populance wholesale 'for the greater good.' Especially knowing the vaccines don't stop the virus or the spread.

Protecting information is not censorship, and your example highlights an incredible ignorance of what censorship (which is a government tool the first amendment is specifically designed around) actually is.

No, that is the strawman world constructed by the mainstream media. The microchip story is actually: https://news.rice.edu/news/2019/quantum-dot-tattoos-hold-vac...

Human bar code tattoos financed by the Gates Foundation. Opponents are discredited because they get an insignificant detail wrong.

We see the same thing in the reporting of the Canadian trucker protests. There are 10,000+ peacefully protesting people, the media find three with a swastika flag (which could even be a literal false flag operation), and reports "right wing protests".

They also report the "desecration of an unknown soldier's memorial", which was a single woman basically walking on the monument. Compare that with "mostly peaceful" BLM protests, where whole cities were burning and monuments were actually destroyed.

99% of the population recovers, maybe you are engaging in hyperbole?

Still, you're take is wildly, overly cynical.

At the end of the day science is nothing more than systematically looking for explanations that help to make predictions. There is no way that that doesn't converge on something useful, since every explanation that is of any consequence will ultimately be put to the test and it will become entirely obvious whether it is useful or not.

And so here we are, computers compute, drugs are effective, airplanes don't fall from the sky. Is there uncertainty at the margins? Sure! But no one who proclaims to know things better than "the scientists" due to some fundamental flaw in "science" as whole has anything to say that would be worth listening to.

Because that's not how the world works. Understanding things is not about picking the right team, it's about deeply engaging with the data and formulating logical hypotheses. And once you're doing that, you're now also a scientist.

Edit: To add a bit of personal experience to the somewhat theoretical argument above... I've met a lot of good people and a lot of assholes in academia. I've certainly met a lot of people who liked to make their results sound a bit more important than they probably really were. Never once have I met someone who didn't ultimately care about drawing valid conclusions from good data.