For the record I now know a professor at one of the premier institutions in the world, who is a total fraud. Their research was fraudulent in grad school. Their lab mates tried to raise concerns and nothing happened. That person graduated, with everyone on the committee knowing the issues. Then they got a premier post-doc position. People in their lab (who I caught up with at a conference) mentioned their work was terrible. Now they’re a professor at a top tier university.

Along the way, everyone knew and when people tried to bring up concerns higher ups in the institution suppressed the knowledge. Mostly because their fraudulent work was already cited tens of times.

This wasn’t directly in my field, but I saw it go down and followed it.

In my day job, I just throw out papers that don’t publish datasets and code. Most cs work is equally useless. It’s all a farce.

EDIT: I recommend the book for some insights - “Rigor Mortis: How Sloppy Science Creates Worthless Cures, Crushes Hope, and Wastes Billions by Richard Harris”

There’s also a decent podcast by Bret and Eric Weinstein that dives into a similar story https://youtu.be/dJNjH4SP6vw?list=PLq9jO8fmlPee9ezOraOHAJ3g9...

There are many examples, at the end of the day... the people in the institutions will protect themselves.

There was an interesting research paper that claimed you could up your IQ with a computer game called dual n back and a lot of paper tried to replicate it, but my mind was blown when I realized none of them used the same code. None of them actually shared the code they used for their research and then they all claimed they were testing the same thing when they obviously weren't.

To me, refusing to share the source code to a program that could be used to replicate your research seems like a big middle finger to science itself. It shows a total disregard to study replication.

Some well intentioned papers share code which is hard to run years afterwards. It's sometimes much simpler to reimplement things that to get the code to run if they are described accurately.

Besides, some articles hide ugly things, nasty tricks and lies in the code, which make their results a lot less believable and valid. Being super upfront about models in terms of equations and pseudocode is important.

Of course, we should also have standards to make code reproducible. Perhaps depositing a VM with everything ready to run.

The danger then is that the VM has so much undocumented complexity that if anything goes wrong, or goes "well" when it shouldn't, no one can explain why. Which also reintroduces a vector to hide nasty tricks.

The worst aspect of this is the lack of will or funding to replicate, replicate, and replicate again all significant results that get published. Post-processed data can be altered, but a TB of raw data is meaningless as well if it hasn't been produced properly, has been obfuscated, or is weirdly formatted.

Data availability is a red herring for the vast majority of the science being made right now (almost everything that does not depend on a multi-millions dollars experiment). If data availability is an end in itself, we would just have moved the goalposts and have a data quality problem instead of a reproducibility problem.

Yes, this is hugely important. We need clearer requirements for what constitutes properly 'published' data, code and methods. It should include all raw data (both used and discarded) as well as complete snapshots of each preliminary and intermediate processing step in a time-stamped chronology.

This is an area where the expertise and best practices of software development, documentation and source control could help inform standards and tooling which would dramatically improve the quality of scientific publishing and replication.

Umm, not really? How many years are we talking about here? Because even Cobol is runnable. Sure some OS related quirks may need changing but getting the raw source is much more likely to expose a subtle flaw in the researcher's method than just the equations. For example, an incorrect negative check (to string vs < 0) or an off by one error.

Also, the Python script crashes on loading the data files unless there are two (but not more than two) spaces in its full file path. This is not documented anywhere.

But properly running code is not the solution either. I can count on the fingers of one had the downloads of some of the codes we've published (easily compilable modern Fortran), and AFAIK nobody ever published anything using them. Having a multitude of codes available does not mean much if nobody runs them, assuming they can be compiled. And I would guarantee that none of the scientists who download these codes would be able to understand what they do in any detail.

Independent replication is inherently expensive, but also critical to the field at large. Some sort of code vault that releases the code and data after a period could be a solid compromise.

Reproducing the code base from scratch just isn't going to be tractable for large scientific pieces of software (e.g. CERN).

Better to have the source code and dig through it carefully as part of the review and replication than insist everyone write their own cleanroom copy just to avoid systematic bias.

That said I am all for peer reviewing code, but that’s not where this ends up. People are going to reuse code they don’t bother to dig into because people are people. The obvious next step would be to reject any paper that’s reusing code from a different team, but that’s excessive.

That said reusing code or data is much safer than reusing code and data.

I can’t begin to understand where you are coming from.

If existing code is buggy or doesn't do what its authors think it does, this can be a big problem. Even if the idea is correct, whole swathes of downstream literature can get contaminated if authors didn't even attempt to implement an algorithm independently, but just blindly reused an existing docker image that they didn't understand. I consider that poor practice. If you claim that your work is based on Paper X, but what you really mean is that you just ran some Docker image associated with Paper X and trusted that it does what it says it does, instead of independently reimplementing the algorithm, that is not replication.

In older areas of science the norm is to try to replicate a paper from the published literature without reuse of "apparatus". For example, in chemistry you would try to replicate a paper using your own lab's glassware, different suppliers for chemicals, different technicians, etc. Analogies here are imperfect, but I would consider downloading and re-running an existing docker image to be dissimilar from that. It's more like re-running the experiment in the first lab rather than replicating the published result in a new lab. As a result it can, like re-running a chemistry experiment in the same lab, miss many possible confounds.

Of course, you do have to stop somewhere. Chemistry labs don't normally blow their own glass, and it's possible that the dominant way of making glass in your era turns out to be a confound (this kind of thing really does happen sometimes!). But imo, on the code side of things, "download and rerun a docker image" is too far towards the side of not even trying to replicate things independently.

The original comment was with respect to some n-back training program. There's so many other potential places of bias in an experiment like that, that you'd be foolish not to start with the exact same program. If an independent team uses a different software stack, and can't replicate, was it the different procedure, software, subjects, or noise?

The first step in scientific replication is almost always, "can the experiment be replicated, or was it a fluke?" In this stage, you want to minimize any free variables.

It's a matter of (leaky) abstractions. If I'm running a chemistry replication, I don't need the exact same round bottom flask as the original experiment; RBFs are fungible. In fact I could probably scale to a different size RBF. However, depending on the chemistry involved, I probably don't want to run the reaction in a cylindrical reactor, at least not without running in the RBF first. That has a different heating profile, which could generate different impurities.

Likewise, I probably don't need the exact same make/model of chromatograph. However, I do want to use the same procedure for running the chromatograph.

Essentially, this would be like trying to reproduce a paper and starting by checking if their mathematical apparatus is correct. It's not useless, and it can help detect fraud or just plain bad peer review of course, but I wouldn't call that an attempt to reproduce their results per se.

Also doing an independent implementation doesn't mean you should start there - what if it doesn't replicate with the same code?

The paper is entered into the scientific record, not the code, which will inevitably become obsolete (old language, old frameworks, implementation details tied to old hardware or operating systems, maybe some source code will be lost, github will go out of business). If the code is necessary, then crucial details have been left out of the paper, so it is not reproducible (although there are some journals that let you submit code as an artifact).

Pseudocode is better, IMO.

It is done on purpose.

The sad thing is that it isn't. It's just how the things are done.

src: friends in HEP

If it's hard or tedious... well so what? So is life. That's most of science is exactly that rigor.

You might as well say no one else can grow a potato because of all the work your farm had to do all year to arrive at your crop. Yes, any other farmer will have to do all the same stuff. It's an utterly unremarkable observation.

Peer review isn't what it's chalked up to be. Basically it's two other people in the same field saying "There's no blatantly obvious issues with this paper" and even that isn't always guaranteed. Reviewers don't make any efforts to actually replicate the research, crunch the data, etc.

src: I used to be in HEP

If the results are sufficiently different, then look at the source?

In medical studies, one level is to reevaluate the data from the same set of patients to see if any bias or errors wasn't included, perhaps with a different or newer statistical idealogy. The best is for the study to be repeated with a fresh set of patient data to see if the underlying conclusions were valid.

> In the good old days physicists repeated each other's experiments, just to be sure. Today they stick to FORTRAN, so that they can share each other's programs, bugs included.

[1] https://www.cs.utexas.edu/users/EWD/transcriptions/EWD04xx/E...

Yes, but that's useful too.

COVID-19 lockdowns largely kicked off in the west due to an epidemiological model from Imperial College London, written over a period of many years by the now notorious Professor Neil Ferguson.

When his team uploaded a pre-print of his paper and started sending it to government ministers, the code for his model wasn't available. They spent months fighting FOIA requests by claiming they were about to release it, but just had to tidy things up a bit first. When the code was finally uploaded to GitHub the world discovered the reason for delay: the model was a 15,000 lines of C trash fire of race conditions, floating point accumulation errors and memory corruptions in which basically every variable was in global scope and with a single letter name. It was a textbook case of how not to write software. In fact it was a textbook case of why variable names matter because one of the memory corruptions was caused by the author apparently losing track of what a variable named 'k' was meant to contain at that point in the code.

Not surprisingly, the model didn't work. Although it had command line flags to set the PRNG seeds these flags were useless: the model generated totally different output every time you ran it, even with fixed seeds. In fact hospital bed demand prediction changed from run to run by more than the size of the entire NHS Nightingale crash hospital building programme, purely due to bugs.

And as we now know the model was entirely wrong and the results were catastrophic. Lockdowns had no impact on mortality. There are many people who looked at the data and saw this but here's just the latest meta-analysis of published studies showing that to be true [1]. They destroyed the NHS which now has a cancer treatment backlog and pool of 'missing' patients so large that it cannot possibly catch up, meaning people will die waiting for treatment from the system they supported with their taxes for their entire lives. They destroyed the tax base, leaving the government with an unpayable debt that can be eliminated only via inflation meaning they will have soon destroyed people's savings too. It's just a catastrophe of incompetence and hubris.

The incorrectness of the model wasn't due only to programming bugs. The underlying biological assumptions were roughly GCSE level or a bit lower (GCSE is the exams you take at 15/16 in the UK), and it's quite evident that high school germ theory is woefully incomplete. In particular it has nothing to say on the topic of aerosol vs droplet transmission, which appears to be a critical error in the way these models are constructed.

Nonetheless, even if the assumptions were correct such a model should never have been used. Anyone outside the team who had access to the original code would have seen this problem immediately and could have sounded the alarm, but:

1. Nobody did have access.

2. ICL lied to the press by claiming the code had been published and peer reviewed years earlier (so where was it?)

3. Then when it was revealed the model wasn't reproducible, they teamed up with another academic at Cambridge and lied again by publishing a report+press release claiming it actually was reproducible and claims otherwise were misinformation.

4. And then the journal Nature and the BBC repeated these false claims of reproducibility.

All whilst anyone who looked at the GitHub issues list could see it filling up with determinism bugs. If you want citations for all these claims look here, at a summary report I wrote for a friendly MP [2].

So. It's good that the Royal Society is telling people not to engage in censorship, but their justifications for taking that stance reveal they're still living in lala land. By far the deadliest and most dangerous scientific misinformation throughout COVID has come from the formal institutions of science themselves. You could sum all the Substacks together and they would amount to 1% of the misinformation that has been published by government-backed "scientists", zero of whom have been banned from anything or received any penalty whatsoever. For as long as the scientific institutions are in denial about how utterly dishonest their culture has become we will continue to see a weird inversion in which random outsiders point out basic errors in their work and they respond by yelling "disinformation".

[1] https://sites.krieger.jhu.edu/iae/files/2022/01/A-Literature...

[2] https://plan99.net/~mike/epidemiology.pdf

Overall it seems that they would have been better served by adding lockdown compliance to their models, which would likely explain much of the difference. It's absurd to claim that lockdowns don't work when a country like Vietnam (100 million people, first detected cases of COVID-19 community spread outside China) had <100 total COVID-19 deaths in 2020. Strict targeted lockdowns, strict isolation requirements after every identified case, for contacts up to the third degree ( contact of someone who was a contact of someone who came in contact with a patient) - these all must have played a role, and any study that fails to explain such extreme success is simply flawed.

"If this interpretation is true, what Björk et al. (2021) find is that information and signaling is far more important than the strictness of the lockdown. There may be other interpretations, but the point is that studies focusing on timing cannot differentiate between these interpretations. However, if lockdowns have a notable effect, we should see this effect regardless of the timing, and we should identify this effect more correctly by excluding studies that exclusively analyze timing."

"It's absurd to claim that lockdowns don't work when a country like Vietnam (100 million people, first detected cases of COVID-19 community spread outside China) had <100 total COVID-19 deaths in 2020"

Many countries claim an improbably low level of COVID death. That doesn't mean their numbers can be taken at face value, although poorer countries do seem to be less worse hit simply because they have far fewer weak, obese and elderly people to begin with.

It's certainly not absurd to claim lockdowns don't work. You're attempting to refute a meta-analysis of studies looking at all the data with the example of a single country. That's meaningless. A single data point can falsify a theory but it cannot prove a theory. To prove lockdowns work there has to be a consistent impact of the policy.

"Lockdowns" is shorthand for "people not coming into contact with other people in a way that spreads the virus"

It's shorthand for a set of government policies that were intended to reduce contact, not eliminate it, because "not coming into contact with other people" is impossible. People still have to go to shops, hospitals, care homes, live with each other, travel around and so on even during a lockdown.

Your belief that it's "completely absurd" to say lockdowns don't affect mortality is based on the kind of abstract but false reasoning that consistently leads epidemiologists astray. Consider a simple scenario in which lockdowns have no effect that's still compatible with germ theory - you're exposed to the virus normally 10 times per week every week. With lockdowns that drops to 5 times a week. It doesn't matter. Everyone is still exposed frequently enough that there will be no difference in outcomes.

"Because the virus cannot teleport through walls, lockdowns necessarily prevent transmission"

https://www.washingtonpost.com/archive/politics/2003/04/18/i...

Viruses can in fact teleport through walls: "The SARS virus that infected hundreds of people in a 33-story Hong Kong apartment tower probably spread in part by traveling through bathroom drainpipes, officials said yesterday in what would be a disturbing new confirmation of the microbe's versatility."

In this scenario, lockdowns work fine, but are insufficient! Perhaps with stringent use of N95 masks during contactless food ration delivery it can be reduced to 0.05 times per week. Then the pandemic ends and we go back to normal.

Also, if you only go out half as often, that's not exactly a lockdown either. A lockdown is people not coming into contact with other people in a way that spreads the virus. It's not people still often coming into contact with other people in a way that spreads the virus but not as often as before.

This kind of completely irrational reasoning is exactly why lockdowns are now discredited. The fact that the response to "here's lots of data showing that lockdowns didn't work" is to demand an impossible level of lockdown that would kill far more people than COVID ever could simply through supply chain collapse alone, really does say it all.

That might be a definition issue. Here in Germany, we had several lockdowns. Many (most?) countries would say we had no lockdown at all.

Antivaxxers often bring up papers concluding that mask mandates don't work, as evidence that masks don't work.

> First, people respond to dangers outside their door. When a pandemic rages, people believe in social distancing regardless of what the government mandates. So, we believe that Allen (2021) is right, when he concludes, “The ineffectiveness [of lockdowns] stemmed from individual changes in behavior: either non-compliance or behavior that mimicked lockdowns.”

> Third, even if lockdowns are successful in initially reducing the spread of COVID-19, the behavioral response may counteract the effect completely, as people respond to the lower risk by changing behavior

Since it is an obvious consequence of the germ theory of disease that isolation stops the spread of disease, the only real question is if lockdowns are efficient at enforcing isolation (and in what conditions, with what costs etc). At best, the paper concludes that they are not, and that government propaganda about the risks of the disease works as well or better.

> You're attempting to refute a meta-analysis of studies looking at all the data with the example of a single country. That's meaningless. A single data point can falsify a theory but it cannot prove a theory. To prove lockdowns work there has to be a consistent impact of the policy.

I would say the null hypothesis would be that lockdowns do work, by relatively simple, almost mechanical, principles (lockdown forces isolation, isolation means the virus can't physically get from one person to another).

And there is basically no country that hasn't seen significant isolation that did well in the pandemic. Whether that isolation was caused by efective, if perhaps draconic lockdowns (such as in China or Vietnam) or by cultural norms and self preservation (such as in Finland or Norway), this remains true.

The real exceptions are countries that successfully isolated themselves from the outside world, and thereafter isolated only those carrying the virus, through broder closures and strict quarantine requirements plus testing - Taiwan, New Zealand are examples of such.

For this type of response, using broad statistical studies that equate very different ground-level phenomena (lockdowns varied wildly in form and in the degree of compliance, but the paper ignores all that in its quantitative data) to make it look good on a graph are mostly misleading - but what more can you expect from three economists dabbling their hand at epidemiology and sociology?

But there is a valid reason for this to happen, and it doesn't mean bugs in the code. If the code is run in a distributed way (multiple threads, processes or machines), which it was, the order of execution is never guaranteed. So even setting the seed will produce a different set of results if the outcomes of each separate instance depend on each other further in the computation.

There are ways to mitigate this, depending on the situation and the amount of slowdown that's acceptable. Since this model was collecting outcomes to create a statistical distribution, rather than a single deterministic number, it didn't need to.

The fact the model was also drawing distributions also will be why different runs produced possibly vastly different results. Those results would be at different ends of a distribution. Only distributions are sampled and used, not single numbers.

Regarding the GCSE level comment, my concern was the opposite, that the model was trying to model too much, and that inaccuracies would build up. No model is perfect (including this one) and the more assumptions made the larger the room for error. But they validated the model with some simpler models as a sanity check.

My view on criticisms of the model were that they were more politically motivated, and the code takedowns were done by people who may have been good coders, but didn't know enough about statistical modeling.

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...

Then there's literally no point to using PRNG seeding. The whole point of PRNG seeding is so you can define some model in terms of "def model(inputs, state) ->" output, and get the "same" output for the same input. I put "same" in quotes because defining sameness on FP hardware is challenging. But usually 0.001% relative tolerance is sufficiently "same" to account for FP implementation weirdness.

If you can't do that, then your model is not a pure function, in which case setting the seed is pointless at best, and biasing/false sense of security in the worst case.

As you mention, non-pure models have their place, but reproducing their results is very challenging, and requires generating distributions with error bars - you essentially "zoom out" until you are a pure function again, with respect to aggregate statistics.

It does not sound like this model was "zoomed out" enough to provide adequate confidence intervals such that you could run the simulation, and statistically guarantee you'd get a result in-bounds.

So, run the code with a seed in a non distributed way (e.g. in R turn off all parallelism), and then the results should be the same in every run.

Then once this test output is validated, depending on the nature of the model, it can be run in parallel, and guarantees of deterministic behaviour will go, but that's ok.

I didn't develop the model, so can't really say anything in depth beyond the published materials.

I just found it odd at the time how this specific detail was incorrectly used by some to claim the model was broken/irredeemably buggy.

Edit: Actually, in general, perhaps there's one other situation where the seed might be useful, assuming you have used a seed in the first place. Depending on the distributed environment, there's no guarantee that the processes or random number draws will be run in the same order. But it might be that in most cases they're in the same order. This might bias the distribution of the samples you take. So you might want to change the seed on every run to protect yourself from such nasty phantom effects.

Make no bones about it, these are programming faults. There's no reason why distributed, long-running models can't produce convergent results with a high degree of determinism given the input state. But this takes some amount of care and attention.

> So you might want to change the seed on every run to protect yourself from such nasty phantom effects.

That's a perfect example of what I mean where a seed is actually worse. If you know you can't control determinism, then you might as well go for the opposite: ensure your randomness is high quality enough such that it approximates a perfect uniform distribution. Adding a seed here means you are more likely to capture the true distribution of the output.

I saw the John Cormack review which suggested there was nothing overly troubling in the code, https://mobile.twitter.com/id_aa_carmack/status/125487236876...

The other takedown reviews focused on the fact that there was non-determinism despite a seed, without understanding that's not necessarily a problem.

Agreed on the second point about not having a seed, but I added the "assuming you have used a seed" caveat because sometimes people do use the seed for some reproducible execution modes (even multi-thread/process ones), which are fine, and it's just easier to randomly vary the seed generation rather than remove it all together when running in a non-deterministic mode.

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.

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.

https://www.nature.com/articles/d41586-020-01685-y

https://twitter.com/ID_AA_Carmack/status/1254872369556074496

This is literally impossible, because viruses cannot teleport through walls.

Unless there is confusion between the government calling for lockdowns, and people actually doing lockdowns?

https://www.sciencedirect.com/science/article/pii/S016041202...

Also food and other required resources are similarly unable to teleport through walls, so the people involved in growing, transporting, preparing and delivering them to your door can't do "lockdown" like the minority who are able to work from home.

As you are doing here. I don't see a name or any specifics anywhere in your comment. Of course not including any names or specifics also means you could be making it up for a good story. We have no way of knowing.

As an aside, I've worked with plenty of academics and while I sometimes thought their research area was stupidly low stakes, the only researchers that I thought were truly wasting time were the ones that had to do research for a medical degree. Basically forced research.

Now I went to a premier university and I'm friends with some smart cookies, but I don't buy for a second the overall theme of this comment chain. There is a reason the West is incredibly wealthy and it isn't because our best and brightest are faking it.

That said, I have also witnessed plenty of low-level fraud: changing of dates to match documentation, discarding "outlier" samples without justification or even documentation, etc. Definitely enough to totally invalidate a result in some cases.

The reasons for that can be myriad. Actual or false scientific success of the last 30 years is not necessarily it.

Also, don't you feel the ship has kind of been slowly going down during the last 10 years, wealth-wise ?

Not for the rich, so the reasons are not that much to do with value-creation (it's value distribution that's rotten).

IMHO, there is also a tremendous amount of value dilution going on, and not just due to the action of the US FED or Treasury, or other central banks.

The problems are systemic and global, and conjoined with the problem of value distribution.

Your second comment was effectively "Well I don't know they are a fraud and I'm not going to be the one who upsets people by trying to put a stop to it".

I don't say this as a criticism of you. I say this a defense of the people you are criticizing. Stopping people like this takes a lot of work and often some personal risk. Most of us aren't willing to do it despite us pretending otherwise.

I don't recall where I first heard it, but the principle that it takes 10x the amount of energy to refute bullshit as it does to generate it certainly seems to apply in this case.

https://www.urbandictionary.com/define.php?term=bullshit%20a...

Reality is almost irrelevant in the face of ideology, dogma, and all the other forces that keep counterintuitive or uncomfortable reality out.

That is an awfully authoritative statement to make based off what OP shared.

But either way, this has been proven time and time again whether we are talking about simple corruption like OP mentioned or more serious forms of injustice. People will judge themselves based off motivations and others based off actions. We can excuse ourselves because of the personal inconvenience that acting will cause us. But other people don't get that luxury. They get criticized purely based off that lack of action because if we were in their situation surely we would do the right thing. Being honest about this is important step to actually fixing the system because we need to identify the motivations which lead to the lack of action in order to remove them and encourage action. Simply vilifying people for not acting accomplishes nothing.

Which part do you disagree with? Maybe the 'much' on much less risk?

'given evidence' is true unless OP made op the story. (And even if OP made up the story then we're judging fictional people and the judgements are still valid.)

I think 'less risk' goes part and parcel with being administration rather than someone lower rank reporting a problem.

And it seems clear to me that it's their job.

So, criticism. Which is not a particularly harsh outcome. And doesn't necessarily mean they made the wrong decision, but them providing a justification would be a good start.

Inaction is often excusable when it's not your job. It's always important to look at motivations, but vilification can also be appropriate when there's dereliction. Sometimes there are no significant motivations leading to lack of action, there's just apathy and people that shouldn't have been hired to the position.

OP never mentioned evidence. People just "knew" this person was a fraud but there was no mention of the actual evidence of fraud. The closest thing to evidence is that "their work was terrible" but that isn't evidence of fraud.

>I think 'less risk' goes part and parcel with being administration rather than someone lower rank reporting a problem.

We have no idea the risks involved. It would be highly embarrassing for a prestigious school and/or instructor to admit that they admitted a fraud into their program. Maybe this isn't even the first time this has happened to these people. Would you want to be the person know for repeatedly being duped by frauds? Maybe that would that ruin your reputation more than looking the other way and letting this person fail somewhere else where you would not be directly tied to their downfall. It is also incredibly risky to punish this person without hard evidence as that can lead to a lawsuit.

These are not meant to be definitive statements. They are just hypothetical that show how we can't judge people's motivations without knowing a lot more about the situation.

While I agree that there are frauds out there in academia and elsewhere, I have no reason to believe that you’re not yourself some sort of fraud. You’ve essentially posted “I have direct knowledge of [unspecified academic fraud in which somebody claimed to have direct knowledge of [unspecified academic fraudulent conclusion] but can’t back it up] but won’t back it up”

Your overall point is… what? Fraud of perpetuated by cowardice? Self interest? An overall sense of apathy towards the truth? Your comment could be construed as any of those.

There are people that love to spread fear, uncertainty and doubt without having to rely on being truthful. People that are intentionally misleading with the sole intent of leveraging people’s biases and emotions to confirm folks notions and whip people up into an artificially-created frenzy make statements yours.

Serious questions: 1. Do you actually give a shit about this big fraud you’ve brought up but not revealed? And 2. Why did you post?

To expose it directly here would likely have little effect generally, but would have an outsized effect personally (damaging trust). If it did have an impact it would likely expose those involved (many careers). I am not in the command chain, I have seen the evidence and it's overwhelming. But I'm not in a position to enact the requisite change.

That said, I don't actually give a shit about this particular case. It's widespread, insanely wide spread. Most studies / work cannot be replicated.

> 2. Why did you post?

As an anecdote to highlight something that I've seen. I also linked to several other informational pieces with public accounts (so don't trust mine, fine -- trust theirs).

Sorry, I don’t mean to pick at you but… what?

If you were to anonymously post the name of an academic fraud, you personally would necessarily be found out, and it would ruin multiple careers?

The powers that be know who you are, know of the fraud and its nature and those involved? You’ve been privy to this big juicy secret that’s shared by many, but if its content were to be revealed, you would certainly be the one pointed out?

Are you the only person that could reasonably know about and publicly object to this fraud? If so, is it a necessary function of your social or professional life to cover up this fraud? If so, I’ll go back to “why did you post?” (“Sharing anecdotes” isn’t really an answer to “Why did you share this anecdote?”)

I think its more "how dare you hint scientists are corrupt!" that kind of drives the outrage.

Correct me if I am wrong, but this website, and the owners are in U.S. of A. If so, an appropriate court order would force the disclosure of logs, IP addresses, accounts, etc. There are plenty of examples where sites had to disclose sufficient details to track an 'anonymous' writer down. With multi-million (billion?) dollar endowments on the line, I would be worried to presume just a throw away account would work.

Feel free to dismiss the claim if you wish.

Sure, sometimes people lie on the internet but this is not an outlandish story.

“Best case” scenario everyone loses their funding, including the colleagues who did report this stuff. The institution, the lab, etc would lose their funding. Those wanted to get PhDs in those labs will lose theirs. It’ll have a serious negative impact on everyone, even those who did the correct thing.

This is why academia is as corrupt as it is. They all evaluate each other’s paper, give each other grants and anyone who exposes anything loses their entire careers.

That's an odd way of reading a post about "a professor" that contrasts them against the rest of their lab.

Academic misconduct.

> Against whom?

I don't know, which puts me exactly where I was ten minutes ago. They have no control over the situation, and if they hadn't posted I would know even less, so it's not suppression.

They have complete control over how complicit they are in preserving the coverup of acts they allege to know are definitely true.

> and if they hadn't posted I would know even less

The reality is that right now you know exactly as much as before they posted because what they posted was unsubstantiated. They may as well have said that their name is Princess Peach and that they're pregnant with the mustached child of a certain Italian plumber for all the good it does you.

One of three things must be true:

1) They have real firsthand knowledge that the claims are true and they're actively deciding to protect the identity(ies) of a conspiracy of rampant fraudsters whose actions are so egregious that they tarnish the very essence of the scientific academy itself.

2) They don't have any real knowledge that the claims are true, and the story is rumormongering.

3) I swear I had a third one, but now that I've written those two I can't think of what it was. I'll leave this placeholder here in case it comes to me.

But in my experience (math, science, & engineering), it is actually far less prevalent than in other places.

Forget the overzealous #sciencetwitter people. I have found that academia is one of the rare places where people the absolute top of their field who are often actually modest & aware of their ignorance about most things.

This view doesn't really align with designing public policy - while those people exist - they aren't the problem (or are insofar they aren't voicing their positions loud enough).

Coming from a place of modesty and acknowledging limitations is not the "believe science" movement.

https://quillette.com/2021/07/06/looking-for-covid-19-miracl...

https://www.wweek.com/news/2021/09/15/a-progressive-biologis...

> “I am unvaccinated, but I am on prophylactic ivermectin,” Weinstein said on his podcast in June. “And the data—shocking as this will be to some people—suggest that prophylactic ivermectin is something like 100% effective at preventing people from contracting COVID when taken properly.”

He wasn't just claiming that ivermectin might have some efficacy against SARS-CoV-2 (possible, though I doubt it), or that the risks of the vaccine were understated to the public (basically true; but it's a great tradeoff for adults, and probably still the right bet for children). Bret was clearly implying that for many people--including himself, and he's not young--the risk/benefit for prophylactic ivermectin was more favorable than for the vaccine. There was no reasonable basis for such a belief, and the harm to those who declined vaccination based on such beliefs has become obvious in the relative death rates.

The first article I've linked above is by Yuri Deigin, who had appeared earlier on Bret's show to discuss the possibility that SARS-CoV-2 arose unnaturally, from an accident in virological research. This was back when that was a conspiracy theory that could get you banned from Facebook, long before mainstream scientists and reporters discussed that as a reasonable (but unproven) hypothesis like now. So I don't think Bret's services as a contrarian are entirely bad, but they're pretty far from entirely good.

For an expert's take on Eric's claims specifically, see https://www.cantorsparadise.com/eric-weinstein-how-not-to-fo...

And the ones that are masquerading as having Nobel worthy research chops to get the audience to believe their gripes about the scientific establishment is on topic enough.

I recommend this podcast for a complete breakdown of the Weinsteins. https://decoding-the-gurus.captivate.fm/episode/eric-and-bre...

I have not seen or heard of Eric Weinstein so I can't comment.

I'm not sure what kind of bar you're using to compare your chosen media, but it seems extremely high, and I'd like to know what you consider to be suitably informative.

They really haven't. They continued to double-down on ivermectin and other COVID era flim flam as it came to light more studies were dodgy or outright fraudulent. It's pretend science theatre from a former small university lecturer who managed a couple research papers in 20 years. For example, telling the audience with a straight face that it doesn't matter if the studies going into a metaanalysis are biased because the errors will cancel out.

This extends well beyond the science itself, for what it's worth. It's an open secret in every department that professors X, Y and Z have the same level of sexual restraint as Harvey Weinstein. It doesn't stop the "woke" institutions doing everything they can to protect these professors' reputations, though.

I understand after some time it would be an embarrassment for the department because they hired, vetted them, etc. But why did that get tolerated initially, was it a case of nepotism? It seems they must have had some kind of a special privilege or status to skate by so easily despite the accusations.

https://youtu.be/0TI0jtr6APw

Hey careful there, it must depend on the area because all the type theory and functional programming research comes with and always came with proofs and working code. It couldn't be more rigorous and useful.

Now, yes, impurities — even minor ones — can have significant effects. But that tends to be in rare circumstances and chemists are quite aware of the need to check for it where it's most needed such as catalysis research.

No one is going to scream "I have a new drug" for something for which the composition is unclear.

I don't know what world you live in, but it isn't one of a typical North American nor European university research lab.

When I hear people disillusioned with how science works, more often than not I see someone who is forced to see that the mental model they had of how it works doesn't line up with how it actually works. It's messy, and has always been messy. Just read some history books, and you'll see "scientific" ideas in the 19th and early 20th century that persisted for a long time simply because some people disliked specific other people with opposing ideas. You'll see horrible egos, non-scientific arguments to reject ideas, people scheming and trying to undermine each other, and so on. Basically, humans being humans to each other. There never was a time when science was performed in the idealized model that we sometimes wish it was done: it always involves people being people, flaws and all.

Plus, I don't know any working scientist who doesn't assume most scientific discoveries are complete bullshit (or, more likely, they aren't bullshit as much as they ultimately contribute no new knowledge or understanding). We all know how the game works and why people put ideas out there (especially with the increased volume of publications people are expected to have now). What keeps people like me going is that I know most ideas are dumb or flat out garbage, but good ones emerge from that churn too and push things along. I like to think that some of my dumb ideas, when I throw them into that churn, will also occasionally push things along.

Don't get me started in the "believe in the science" line. The fact that we've turned that phrase into a political cudgel is not helping the situation.

I feel the HN community might resonate with how startups are seen currently. Most of them fail, some are outright fraud, but a few completely change the game.

The model to assess the scientific enterprise should be similar to a VC. Most will fail but the few big successes will more than compensate for the losses.

The anti-pro-sciencers are claiming there is not just noise, but a sea of deliberately fabricated conspiracy theories concocted over many decades with an ultimate end goal of putting tracking chips in them.

It's obviously not a completely worthless institution - there have clearly been some impressive discoveries despite all of the bullshit.

I think the main source of distrust comes from the jarring disconnect between the way that the institution is presented to the public vs. the way it actually functions in real life. "Messy" is a beautiful way to describe things from the inside.

The problem, in my opinion, is how external entities treat science. Some examples:

* Media reporting low grade papers as "Science" which constantly contradict each other. "Scientists found out $something causes cancer", then a year after that "Scientists found out $thatsamething cures cancer

* Pseudo scientific drugs being sold for a health-and-beauty-obsessed general public with little to no effect

* Political think tanks funding flawed research to push their own agenda and heavily echoing those researches in their echelons for political gain

It seems that academia has become a foundation for other entities to cling on for profit, and now the it's the acamedia's reputation that has been shattered.

There absolutely is. Scientists and universities are constantly declaring themselves to be bastions of reason and enlightenment, and their research that they themselves know is shit to be of great significance and rigor.

I'm not even talking about the outright corruption that you mentioned - just the more mundane things like researchers publishing papers with completely useless/misleading information (e.g. testing a ML algorithm on a single dataset and declaring it as a breakthrough in neurosignal decoding) just to get those updoots from your mates.

Despite all the brilliant minds and appearance of professionalism and pride and campus pageantry...

it's easily a much more insecure, drama-filled, immature, and petty social dynamic and environment than most high schools.

That seems imo to be due to a combination of academia itself and a govt (or govt style) administration apparatus, where everyone is constantly trying convince everyone else of the importance of what it is they do.

I get the feeling that you claim that it is all fraud and lies, and that those who disagree can see no wrong with it at all.

Then someone responds with a nuanced reply, something that I, as someone who lives in a university town in Sweden, have the impression that almost everyone in academia actually agrees with.

And then it turns out that you also mostly agree. So, now I'm asking myself (and you): Why did I read that first post of yours as so hostile? As someone who was actively trying to create mistrust?

Is it just because how the debate climate is on the internet, or should you perhaps consider trying to be a bit more nuanced when communicating on the internet?

I think this happens all the time - popular opinion swings far in one direction, criticisms then go in the opposite direction as disillusionment and problems get exemplified, again too far, back and forth, rediscovering the lessons of the past and settling down.

How else do you define bullshit?

But the self-correcting nature of science is still its biggest redeeming feature. And therefore unless I can figure out things by myself, I'll trust scientific consensus. I may not trust individual papers, as people make mistakes, may have an axe to grind, but I'll believe the consensus. For sure occasionally even scientific consensus will be wrong.

One area I've studied pretty extensively is the history of cancer treatment. In the long story of the history of cancer treatment, it is absolutely scandalous how often the scientific consensus was wrong and persisted for years in spite of the evidence. For example, the radical mastectomy for the treatment of breast cancer continued to be used for many years, leaving many women disfigured, in spite of wide evidence that it did not produce better outcomes vs more restrained breast tissue removal.

In the history of science, many of these kinds of bad ideas have persisted simply due to deference/seniority - the incentives are all stacked towards paying your dues and not challenging the status quo and absolutely not towards being right/following the actual scientific method. There is a reason the saying "Science advances one funeral at a time" exists - as Max Planck noted: "a new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents die, and a new generation grows up that is familiar with it.”

Overall it just felt like she was a hot potato and nobody wanted to put their name on *anything* outside of protocol. Even blood work. She was treated at the James Cancer Center in Ohio and we got second opinions from Cleveland Clinic and MD Anderson in New York. These are all fairly well regarded institutions in cancer treatment. I was expecting strong opinions and got hand waving and reluctance to interfere with any treatment selected by her primary oncologist. In the process of all of this i read hundreds of studies and research papers, spoke with numerous PIs, trial coordinators and industry reps. I couldn’t get any traction for anything and came away feeling a bit hopeless.

After it was all over i started making public offers of $25k as a starting point to just review her case from end to end to assess the quality of her care and determine if anything could be learned from it. The only takers I got for that were lawyers who were hoping to twist it into malpractice case, which I wasn’t interested in.

The experience left me extremely bitter about the current state of healthcare. After a while i was able to develop some empathy for the providers. They’re trapped in a system that mortgages their future with student loans and directly threatens their ability to cover with litigation and insurance. They have to stay on the rails or risk financial ruin.

So kinda like the democracy then.... the worst system, apart from all the others (paraphrased)?

I remember seeing a plot of the values over time, but my google-fu is failing me.

https://alexholcombe.medium.com/confirmation-bias-in-science...

It that the one you remember?

Most doctors are more like engineers than physicists.

https://link.springer.com/chapter/10.1007/978-3-0348-9008-3_...

"Even individual fanatic scientific advocates of the Einsteinian theory seem to have finally abandoned their tactic of cutting off any discussion about it with the threat that every criticism, even the most moderate and scrupulous ones, must be discredited as an obvious effluence of stupidity and malice"

People often present science as some sort of free-floating edifice that "self corrects" through mysterious mechanisms. It doesn't. Scientists with wrong ideas have to be explicitly corrected by other people, some of whom will be random Swiss patent clerks and other outsiders. Therefore you cannot have science without free speech, because otherwise there's no way for bad ideas to be corrected. It's as simple as that. And yet, academic "scientists" are often at the forefront of demanding it be shut down.

I think COVID has demonstrated that a substantial fraction of people would still be opting for quackery instead of conventional treatment.

People have done lots of harm (burning money is doing harm) with crap science. That is totally wrong and harmful.

What may happen later is not a solution.

I don't think most people really try to argue against "science" as is, or even the scientific process in the large scale. The gripe we have is against people who first do bad science (or popularize them), refuse to admit they're wrong, then patronizingly tell the public to trust them anyways, and still have the audacity to reassure us that "it will all work out (50 years later)".

It always fights against anything new, and will try to be opposed to anything against the status quo. It is so confident it is right it actually feels like it is doing the right thing by trying to suppress ideas opposed to the establishment.

Kind of like Galileo I guess, and the religious leaders of hus day are like the intellectual elitists of our day.

Thinking about the copernican revolution it made sense the establishment was opposed to it, not because it wasn’t factually correct, but it was like knowledge people aren’t ready for. And it was too unpredictable what it would do if the idea became dominant.

Scientific consensus can be a hard thing to define sometimes. Climate change is an example where there are enough studies out there that a meta-study can prove consensus, but what about something more recent and difficult to measure, like the effectiveness of covid vaccines/measures?

In any case, you read with exasperation or amusement the multiple errors in a story, and then turn the page to national or international affairs, and read as if the rest of the newspaper was somehow more accurate about Palestine than the baloney you just read. You turn the page, and forget what you know.

– Michael Crichton

Too bad his criticism comes from fantasy, instead of facts.

And of course he's a climate change denier.

But this quote isn’t really a criticism of science. It’s a criticism of journalism.

I think most people here on HN agree with his premise regarding the quality of science journalism. I’m not so sure about his conclusion that all journalism should be treated with the same suspicion.

The quoted passage specifically says his area of expertise is show business. Do you know of misinformation that he endorses or spreads on that topic?

"State of Fear" is a polemic against climate science.

If all you read was the comment I responded to, then it's no surprise that you don't think I provided enough information to criticize his books. Read his books, then read my criticism.

How unrealistic. Scientists like Peter Daszak and the EcoHealth Alliance would never do anything evil or untoward, such as using grant money to perform dangerous gain-of-function research on coronaviruses, demonstrably lying to the NIH about the scope of this research, and then misleading the public on the COVID-19 lab-origin theory while failing to disclose their financial and professional ties to the lab in question [0][1][2].

[0] https://theintercept.com/2021/09/23/coronavirus-research-gra...

[1] https://www.washingtonpost.com/opinions/2021/10/25/one-perso...

[2] https://www.theatlantic.com/science/archive/2021/09/lab-leak...

I didn't say that.

My point is that your comment reinforces the point that is made by the much-quoted quote.

He said he knows show business. Calling his writing on climate science crap is consistent with the quote.

Can you see how your comment sounds paradoxical?

You seemed to be saying "don't pay attention to his quote because he's clueless about climate change", but the quote is about people who aren't, say, climate scientists, being gullible about the topic.

It's a self-referential quote. If you refuted it, does that mean that non climate scientists can judge climate science?

It seemed like an amusing paradox, and I guess I'm curious about your motivation.

Thanks for the upvote.

Sure, the quote in itself is great, but the post that you replied to is pointing out that the person who made that quote is himself doing the same thing as those journalists that he is criticizing.

The current climate we're in means that consensus science from 50+ years of research and medical practice around the world is being questioned. Not some BS CS paper on a performance improvement in something that will be read and forgotten about, or that some new medication/fruit/treatment has some miracle cure that others can't duplicate.

We're in a world were people think that Bill Gates is inserting microchips into people and that Covid is a hoax by the world government and no one has died. Academia may be broken - but don't loose faith in science.

I would prefer that the general public take advice from scientific researchers with a grain of salt, with an understanding that the system is far from perfect and much of what is published either contains methodological flaws that lead to false conclusions or cannot be reproduced at all.

I also think a little bit of common sense would go a long way.

A big problem, as I see it, is the unwillngness of academia to engage in debate with the public. Whilst academics nowadays are supported and encouraged to engage in "outreach"; the nature of that is almost always in broadcast mode.

In the age of social media, science needs to learn how to communicate, and engage with non-tenured people asking good, evidence based, questions. Sure they get bombarded with abuse and crackpots, but that should not mean ignoring absolutely everybody that approaches them with reason and civility; as so often happens.

Trust is breaking down in society. Is there any wonder, when trust grows from interaction?

> Sure they get bombarded with abuse and crackpots, but that should not mean ignoring absolutely everybody that approaches them with reason and civility; as so often happens.

I think it is a really hard problem. Figuring it if someone is arguing in good faith is hard. And engaging with someone arguing from bad faith is really draining. Besides the difficulty, people worry that engaging with some people will draw even more responses, many of them being in bad faith. Certainly only responding partially often leads to "yeah you are responding to them, but what about X".

I think in the end, wading through all the abuse is just so painful that many people decide to ignore input. And doing anything that might cause more abuse to flood in is reflexively avoided, since that abuse actually hurts.

Solving this is going to require specialization. Because being able to wade through the abuse is not something everyone can do. It will also require proper incentives to actually do this, because the people who can handle the abuse aren't going to do so out of charity.

https://www.nature.com/articles/d41586-021-02483-w

Robert Malone's bio

"Dr. Robert Malone is the inventor of the nine original mRNA vaccine patents, which were originally filed in 1989 (including both the idea of mRNA vaccines and the original proof of principle experiments) and RNA transfection. Dr. Malone, has close to 100 peer-reviewed publications which have been cited over 12,000 times. Since January 2020, Dr. Malone has been leading a large team focused on clinical research design, drug development, computer modeling and mechanisms of action of repurposed drugs for the treatment of COVID-19. Dr. Malone is the Medical Director of The Unity Project, a group of 300 organizations across the US standing against mandated COVID vaccines for children. He is also the President of the Global Covid Summit, an organization of over 16,000 doctors and scientists committed to speaking truth to power about COVID pandemic research and treatment."

Do you really think it is unreasonable to listen to what someone with this kind of a background has to say about mRNA vaccines?

So no, I don't think his random commentary is particularly relevant or reasonable to listen to, any more than Jaron Lanier's haterade @ the tech industry is relevant just because he was working on shitty VR in 1990. Some people are dinosaurs and just need to be ignored, it's not unusual for people to grow to hate the things that they worked on when they were young and get increasingly unhinged arguing against them.

I don't think Rogan should be censored, for sure, 100% and without any reservations - free speech should be absolute, I'm old-school in that respect. But I absolutely think anyone paying attention to this crap should be socially scorned as a dumbass, and TBH I can't get my chuff up too far over people trying to convince Spotify to boot Rogan over this, free markets are also sacrosanct and that's what this is (even if I also would vastly prefer that Spotify holds to a hardline free-speech ethos and leaves him up).

> consensus science from 50+ years of research and medical practice [...] being questioned

This is precisely what scientists should have been doing! I'm afraid having your work done for you by unqualified amateurs is a kind of punishment for skimping on it for too long.

> a background of "Ultimate Fighting Championship color commentator, comedian, actor, and former television presenter"

This is essentially a class-ist argument: Someone who tells dirty jokes on netflix couldn't possibly, by the essence of their being, decide for themselves which scientist to talk to. What's particularly striking is that this comes after the author seemingly accepts the parent's premise that academia is borken. What I'm hearing is "Yeah, we haven't earned your trust, but at least we're not those people". This is not a winning argument, and certainly not a scientific one.

> We're in a world were people think that Bill Gates is inserting microchips into people and that Covid is a hoax by the world government and no one has died

This too seems heavily influnced by class. When people believe "Bill Gates is inserting microchips" they are factually wrong but tentatively right. They are powerless in politics, while oligarchs, including foreign ones, have outsized influence. Wild conspiracies are how these feelings find expression in those that have no other way of doing so. By taking everything literal ("no, Bill Gates is not literally inserting microchips"), their enemies can avoid dealing with the issues a more charitable interpretation would be getting at.

> don't loose faith in science

To the extent that science is used as the underwriter of various political projects, I think people are perfectly fine losing faith in it. 5 decades of "belief" in science hasn't done much good, so what's the point? On the other hand, I'm not worried that people will stop using material science... By and large mistrust is very well placed.

This is so on the mark - too often we forget to focus on what someone is _really_ trying to communicate, and take their words at "face value" - which is not communication at all.

How else do you think scientists reached consensus, exactly? Maybe the problem lies with the unqualified amateurs, who are, well unqualified to do that?

Scientific consensus is reached gradually over the course of decades, after a theory stood the test of many many challenges. No scientific theory is absolute and final and challenges are welcome, but they should be scientific, i.e. based on real data and statistically gounded analysis. Yes, it's hard to change consensus, not only in science but in any kind of community. This does not mean everybody in that community is corrupt. "Extraordinary claims require extraordinary evidence". If changing a consensus was easy, it would not be a real consensus.

>> a background of "Ultimate Fighting Championship color commentator, comedian, actor, and former television presenter" > >This is essentially a class-ist argument: Someone who tells dirty jokes on netflix couldn't possibly, by the essence of their being, decide for themselves which scientist to talk to.

No it's not. Getting all background knowledge and staying up to date with all pubblications in a given scientific field is very difficult and time-consuming. Expertise does exist, and takes year to build. Somebody who tells dirty jokes on netflix likely does not have the time or energy do to that. Maybe, but quite unlikely.

> To the extent that science is used as the underwriter of various political projects, I think people are perfectly fine losing faith in it.

What is the alternative? If no policy should be based on science, then what? Tea leaves? Crystal balls? Shamans? Prayers? Goat sacrifices?

> 5 decades of "belief" in science hasn't done much good.

You really need to elaborate here.

Please, please, please. Do not confuse science (the process of discovery) with scientific institutions (the groups of humans with all their defects).

I'm afraid you're lacking imagination. Also, the irony of these being historical examples rather than made up seems to be lost on you. Humans are strange.

On a more serious note, if you were interested in what a modern society looks like that is no longer guided by science, you could study Russia. It seems a cynical blend of religion, nationalism, and imperialism is what it is. I'm not advocating for this, I'm describing what it looks like.

>> 5 decades of "belief" in science hasn't done much good.

> You really need to elaborate here.

I specifically prefaced it with "as underwriter of political projects". Over that time, the #1 influence on policy has been economics, and besides covid all of the most pressing issues today are economic: Stagnation, deindustrialization, inequaliy, and inflation. People are asking, if policy over that period was built on such solid scientific foundation, how come we've ended up here?

>This is precisely what scientists should have been doing!

My reference was to the years since we've virtually eliminated the deaths from polio, TB, and measles and other diseases in countries that have used vaccines against them. There have been studies that have shown that some vaccines cause side effects in some people, but none have shown to be comparable to the millions of lives that have been saved from debilitating diseases.

> 5 decades of "belief" in science hasn't done much good.

Says the person writing a comment on HN through a network of hardware and software unimaginable 50 years ago that was at least partly made possible by scientists and academic researchers making real breakthroughs. My comments were to the poster who thought scientific research was a polished turd because of his experience in one part of academia

This kind of example is common. It tells me its worth questioning research and looking deeper at what ever is released. When someone questions research to look at the merits of what they say.

He’s in the industry of going against “mainstream [ie scientific] knowledge”… which provides him and endless stream of salesmen who cherry picked scientific papers that support their schtick… and a lot of interested listeners who get an excuse for everything they don’t feel like doing (“oh, this guy on Rogan said brushing destroys your teeth, so I guess I’m better off leaving them alone”)

This seems to be equally valid for basically any media (using brush and toothpaste is not news). Would you say that all media is in BIG conflict of interest? Or how is Joe Rogan fundamentally different?

We know that traditional media has analogous biases towards celebrity scandals, and towards FUD and polarization in politics, and that bottom feeder broadcasters with no ethics thrive on those.

... He does it with lifestyle / well-being specifically, inviting people that peddle diets, supplements, etc.

These snake oil salesmen have quickly cottoned on to the fact that they can take disenfranchised and usually uneducated groups and regurgitate them their own opinions with the air of authority, thus gaining an almost fanatically loyal following.

It's the new age televangelists, but instead of preaching salvation for tithe, they preach actually dangerous drivel that gets people killed. It feels like a low point in human evolution, where our tools enable sociopaths to mislead clueless masses on an industrial scale.

And do you know what is the worst aspect of this? That we can't ban them in good faith, because banning information is a slippery slope one way street that we mustn't take a single step on. The only way out of this quagmire is education, but it's a long slog that will pay off in decades time and only if we make a great effort now. It's literally the tree we have to plant and yet our current generation won't get to enjoy it's shade.

Being less biased doesn’t make one more true. Both can be wrong

Also routinely says not to listen to him, they're his opinions and probably wrong.

Good to see that the Rogan stans in tech are classy as fuck. Jesus...

2) The scientists who are involved are unusually politically motivated. Most of the scientists I can name off the top of my head are in government (the likes of Fauci in the States or Kerry Chant of Australia. These people are in a position of arguing that their opinion should be given emergency power to decide ... most aspects of the social sphere. This is an excellent time to be looking beyond credentialism to actual arguments and trade offs. Without the "noble lies", please, lets make policy decisions based on facts.

3) I still haven't found anyone who can argue why the vaccine mandates aren't human rights abuses. We're talking the really basic stuff like right to work, right to assemble, right to religion, etc. I don't care who we listen to as long as they are arguing on the side of those basic freedoms. If Joe Rogan is arguing for those things, I'd rather people listened to him. Those concepts have a better track record than technocrats. We can worry about run-of-the-mill stupidity after we've dealt with the real risks here.

The latter is pretty much moot.

You do have to understand that when people say "I got banned because I said COVID may have leaked from a lab" they don't actually mean "I got banned because I said COVID may have leaked from a lab". It's significantly more likely that they got banned because they said COVID definitely leaked from a lab funded personally by Dr Anthony Fauci to create bio-weapons against America to aid the spread of communism with Chinese characteristics because he is secretly an illegal immigrant from Kenya.

That's a strawman. He's interviewing doctors, scientists etc. He's not putting forward his own views, apart from when he recounts his own covid experience.

It's like saying the left gets all their news from John Oliver.

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.

(A) In some aspects, 50+ years of accepted medical science was wrong. "Droplet dogma" [1,2] was wrong. Now we know that respiratory viruses spread by airborne aerosols [3].

The epistemological tradition of modern medical science seems to be almost solely based on randomized controlled trials (RCTs). They think everything should be studied and verified the same way that new drugs are studied. We use a lot of physical safety measures, like seat belts and airbags in cars, hard hats in construction sites, etc. because they make physical sense, and their protective properties have been measured in engineering laboratory studies. Like using crash test dummies when crash testing cars. Engineering and physical sciences have methods to achieve and test knowledge, that are not an RCT with real people.

Now the topical topic is face masks. Engineers know respirator masks can filter over 95% of fine particles, but a sizeable part of the medical science still doesn't believe in face masks, due to lack of decisive studies using an RCT-based setup.

(B) But then we also have a surprisingly strong anti-vaccine movement. In this case, the consensus of medical science is correct, and vaccines work.

So we have A, where medical science was wrong and a bit stubborn to correct itself. Science as whole was not wrong: Engineers and physicists worked out the details of aerosol spread of the virus and the filtering properties of face masks. And then we have B, a genuine anti-science movement.

But what to do? If we didn't question the established dogma as used by the authorities, then we would still believe in the droplet dogma, and we would still think face masks are useless. But if we allow the questioning of the established dogma when the dogma is indeed wrong, the how can the general public know when the old dogmas are questioned for good scientific reasons (A), and when not (B)?

[1] https://royalsocietypublishing.org/doi/10.1098/rsfs.2021.004...

[2] https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3904176

[3] https://www.science.org/doi/10.1126/science.abd9149

Or shutdowns of discussion that some health authorities, like Sweden, have (IMO correctly) judged that there is no clear benefit to vaccinating kids under 12 against COVID [2].

The issue I see is less on the fringe 'microchips' but more around the complete shutdown of discussion of complex topics are not binary.

[1]: https://www.nature.com/articles/s41591-021-01630-0/figures/2

[2]: https://www.reuters.com/world/europe/sweden-decides-against-...

That is to say that there is a selection bias for people who did not die, and this is a significant oversight in your interpretation.

They make the claim "vaccines that pose 50% higher excess risk of myocarditis compared to an infection", and this is a misleading claim.

The risk to an individual of taking the vaccine is more sensibly measured against what happens to somebody who isn't vaccinated, not what happens to somebody who isn't vaccinated and also didn't die.

This is like comparing injuries in people who survive jumping out of a plane with and without a parachute. How important is it if people without a parachute break their arms less often?

Your counter-claim is that this alternative question is what they should have been discussing, and if they had been, it'd be misleading to focus on the risk of myocarditis alone. Which is correct, but then they'd also have to take into account injuries and deaths from other non-myocarditis vaccine side effects, the costs of medical care not given due to the spending of resources on vaccines instead, QALYs and so on. But the sort of institutions that fund such research don't want to know about vaccine downsides, so don't fund any research into it, and moreover expend considerable effort to suppress whatever little research does get done.

If the vaccines are creating sterilizing immunity, this might be true. For example the measles vaccine does that.

But for all current SARS-CoV-2 vaccines we have we don't get sterilizing immunity.

So it can still make sense to get vaccinated even if society at large is vaccinated.

In that instance, a doctor who was part of the team who invented RNA-based vaccines. Any problem with that ?