Functionally, what's the difference? The output of their model varied based on environmental factors (how the OS chose to schedule things). The lower-order bits of some of the values got corrupted, due to floating-point errors. In essence, their model had noise, bias, and lower precision than a floating point number – all things that scientists are used to.
Scientists are used to some level of unavoidable noise from experiments done on the natural world because the natural world is not fully controllable. Thus they are expected to work hard to minimize the uncertainty in their measurements, then characterize what's left and take that into account in their calculations.
They are not expected to make beginner level mistakes when solving simple mathematical equations. Avoidable errors introduced by doing their maths wrong is fundamentally different to unavoidable measurement uncertainty. The whole point of doing simulations in silico is to avoid the problems of the natural world and give you a fully controllable and precisely measurable environment, in which you can re-run the simulation whilst altering only a single variable. That's the justification for creating these sorts of models in the first place!
Perhaps you think the errors were small. The errors in their model due to their bugs were of the same order of magnitude as the predictions themselves. They knew this but presented the outputs to the government as "science" anyway, then systematically attacked the character and motives of anyone who pointed out they were making mistakes. Every single member of that team should have been fired years ago, yet instead what happened is the attitude you're displaying here: a widespread argument that scientists shouldn't be or can't be held to the quality standards we expect of a $10 video game.
How can anyone trust the output of "science" when this attitude is so widespread? We wouldn't accept this kind of argument from people in any other field.
Report 9 talks about parameters being modeled as probability distributions, i.e. its a stochastic model. I doubt they would draw conclusions from a single run, as the code is drawing a single sample from a probability distribution. And, if you look at the paper describing the original model (cited in report 9), they do test the model with multiple runs. On top of that they perform sensitivity analyses to check erroneous assumptions aren't driving the model.
I have spent time in academia, but I'm not an academic, and don't feel any obligation to fly the flag for academia.
Regarding the politics, contrast how the people who forensically examined Ferguson's papers were so ready to accept the competing (and clearly incorrect https://www.youtube.com/watch?v=DKh6kJ-RSMI) results from Sunetra Gupta's group.
Fair point about academic code being messy. It's a big issue, but the incentives are not there at the moment to write quality code. I assume you're a programmer - if you wanted to be the change you want to see, you could join an academic group, reduce your salary by 3x-4x, and be in a place where what you do is not a priority.
ICL likes to claim their model is stochastic. Unfortunately that's just one of many things they said that turned out to be untrue.
The Ferguson model isn't stochastic. They claim it is because they don't understand modelling or programming. It's actually an ordinary agent-like simulation of the type you'd find in any city builder video game, and thus each time you run it you get exactly one set of outputs, not a probability distribution. They think it's "stochastic" because you can specify different PRNG seeds on the command line.
If they ran it many times with different PRNG seeds, then this would at least quantify the effect of randomness on their simulation. But, they never did. How do we know this? Several pieces of evidence:
1. There are no confidence intervals in Report 9, only different scenarios. See for yourself: https://www.imperial.ac.uk/media/imperial-college/medicine/m...
2. The program is so slow that it takes a day to do even a single run of the scenarios in Report 9. To determine CIs for something like this you'd want hundreds of runs at least. You could try and do them all in parallel on a large compute cluster, however, ICL never did that. As far as I understand their original program only ran on a single Windows box they had in their lab - it wasn't really portable and indeed its results change even in single-threaded mode between machines, due to compiler optimizations changing the output depending on whether AVX is available.
3. The "code check" document that falsely claims the model is replicable, states explicitly that "These results are the average of NR=10 runs, rather than just one simulation as used in Report 9."
So, their own collaborators confirmed that they never ran it more than once, and each run produces exactly one line on a graph. Therefore even if you accept the entirely ridiculous argument that it's OK to produce corrupted output if you take the average of multiple runs (it isn't!), they didn't do it anyway.
Finally, as one of the people who forensically examined Ferguson's work, I never accepted Guptra's either (not that this is in any way relevant). She did at least present CIs but they were so wide they boiled down to "we don't know", which seems to be a common failure mode in epidemiology - CIs are presented without being interpreted, such that you can get values like "42% (95% CI 6%-87%)" appearing in papers.
Luckily they published their reasoning on the number of realisations in the supplementary materials of a prior paper cited in report 9 (citation 5): https://www.nature.com/articles/nature04795#MOESM28
"Numbers of realisations & computational resources: It is essential to undertake sufficient realisation to ensure ensemble behaviour of a stochastic is well characterised for any one set of parameter values. For our past work which examined extinction probabilities, this necessitates very large numbers of model realizations being generated. In the current work, only the timing of the initial introduction of virus into a country is potentially highly variable – once case incidence reaches a few hundred cases per day, dynamics are much closer to deterministic."
So looks like they did consider the issue, and the number of realisations needed is dependent on the variable of interest in the model. The code check appears to back their justification up, "Small variations (mostly under 5%) in the numbers were observed between Report 9 and our runs."
The team's claim that you only need to run it once because the variability was well characterized in the past is also nonsense. They were constantly changing the model. Even if they thought they understood the variance in the output in the past (which they didn't), it was invalidated the moment they changed the model to reflect new data and ideas.
Look, you're trying to justify this without seeming to realize that this is Hacker News. It's a site read mostly by programmers. This team demanded and got incredibly destructive policies on the back of this model, which is garbage. It's the sort of code quality that got Toyota found guilty in court of severe negligence. The fact that academics apparently struggle to understand how serious this is, is by far a faster and better creator of anti-science narratives than anything any blogger could ever write.
I have given a possible explanation for the variation, that doesn't require buggy code, in my previous comments.
An alternative hypothesis is that it's bug driven, but very competent people (including eminent programmers like John Cormack) seem to have vouched for it on that front. I'd say this puts a high burden of proof on detractors.
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.