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All models are wrong, but some are completely wrong (design.blog)
305 points by magoghm 4 months ago | hide | past | favorite | 207 comments



What I've noticed about models, or at least when people are talking about them or trying to prove a point about them, is that people forget models are a simplified version of a specific part of reality, much the same as models of airplanes or something.

No matter how many variables you include, you can never capture the utterly massive and unpredictable amount of variables that exist in reality. But they're not supposed to. They're tools that are supposed to be used to help get an idea about how something might happen, that's marginally better than just guessing, due again to models always being incomplete, despite any amount of best efforts.

Like any tools, using models incorrectly, fucking around with them until you just get what you want to see or using the wrong one for the wrong task can be potentially dangerous, especially when those models are used to guide large decisions with serious consequences and impacts.

Reasoning and common sense need to be used alongside models and if the models don't seem to reflect reality, the problem is with the model and not reality and the model needs to be adjusted or thrown out otherwise continuing to use it will just lead to lousier and lousier decisions.


Humans have the tendency to think they understand something if they have a label on it. It helps them take decisions faster, whether it's good or bad.

If you tell people you don't know what causes their respiratory illness, they may think you are incompetent, and feel helpless.

If you tell them they have acute bronchitis, suddenly they feel empowered. It just means they have an inflammation of the bronchi. It says nothing of the direct cause, nor the indirect cause: a virus, tobacco, dust, air pollution or asthma. It says nothing about how bad it is. They don't even know really what it implies for their body or their treatment.

But now they think they know.

Call something a democracy, and nobody will check if it is.

Call something a privilege, and people will start to want it, working hard for it.

Models can be used to help your interact with the world. But they are also used to avoid interacting with the world: if you use them as labels, instead of comparing the model with reality, you get a shortcut to take decisions. It becomes a mere name to justify a decision process instead of a tool for the decision process.


I'm not sure I agree.

While technically bronchitis could be any inflammation of the brochial tubes, a diagnosis of bronchitis usually means that that it's the patient's major problem. It'd be odd to describe a cancer as "bronchitis" even if it involved brochi. So you do learn something, in a Griceian sense, from the relatively uninformative diagnosis. There's also the implication that there are some tools for managing it, the doctor's seen it before, etc all of which are reassuring.


"Mind is the great destroyer of reality" - a quote from one of those ancient dusty books. Our mind is a machine that creates models and labels for everything, to avoid dealing with the illigible reality.


>If you tell people you don't know what causes their respiratory illness, they may think you are incompetent, and feel helpless.

Agreed, on the medical front this also leads to a focus on sophisticated diagnostic tools and numbers versus a 'true' cure.

>Call something a democracy, and nobody will check if it is. >Call something a privilege, and people will start to want it, working hard for it.

Let me add one more to the list. Call something a 'right' and people feel denied if they are not given it. Eg. voting rights. Even where one can vote, consent is generally manufactured but people feel empowered when they vote.


"Gun rights" are totally bogus.

Let's enumerate rights: "Life." Okay. "Liberty." Gotcha. "Pursuit of happiness." Yep. "Private property." Might need some asterisks on that one, but sure. "Carry a weapon around in case I might need to murder another human being very quickly." ???

It's been this incredible PR campaign based around the fact that people are illiterate and don't know what "bear arms" means (jargon for "serve in a militia"[1].)

[1] https://languagelog.ldc.upenn.edu/nll/?p=43559


The actual right is to self-defense - itself a derivative of other fundamental rights. Access to effective means of self-defense follows from there, just as abstract freedom of speech has many derivative rights like freedom of the press.


An abstract right to freedom of thought allows for extensive time and place restrictions plus libel laws, etc. The right to self-defense (which I will concede) allows for restrictions on weapon ownership and usage.


Of course it does. But it doesn't allow for arbitrary restrictions - a restriction has to be justified, and not just on the basis that it seems like it might help.

In legal terms, the standard for restrictions on free speech in US is "strict scrutiny" for this exact reason. So while we have time and place regulations, there are also many safeguards in place to ensure that those aren't abused. To give one example, permits can only be required once disruption reaches a certain level (e.g. a solo demonstration on a sidewalk shouldn't need one), and for cases where fees are associated with said permit, it cannot be imposed on somebody unable to pay, or for whom it would be a considerable burden.

I can assure you that the vast majority of proposed gun control legislation would not pass strict scrutiny. This is evident even from the degree to which policies vary in other countries - for example, when it comes to "assault weapons", the definitions vary significantly even in US state laws. Yet there's no obvious correlation with any practical effects if you compare, say, UK (which bans all semi-autos) and Germany (where you can own an AR-15).

The laws that do seem to matter are mostly those regulating the owners directly, and even then it depends a lot on the policy. E.g. universal background checks for each purchase sound rational in theory, but evidence on their effectiveness is lackluster. OTOH a license to possess, that has to be periodically renewed, has a much more profound effect.

Czechia is a particularly interesting case, being very permissive wrt "what" - comparable to US, even with shall-issue concealed carry! - but much more stringent on "who", and this seems to work just as well as UK's tight-fisted approach: https://en.wikipedia.org/wiki/Gun_law_in_the_Czech_Republic#...


Not sure if you were in agreement with me, being sarcastic, or nitpicking.

But the history of comments is certainly not dull. Look at my profile, and see if the link mentioned in it catch your fancy, if it does, shoot me an email.


Medicine is super-bad about not being able to tell a symptom from a disease. A lot of "diseases" are just symptoms with Latin names and a set of empirical remedies.

Psychology in particular seems to have come to the conclusion that if you can name an assortment of 4 out of 10 symptoms, that counts as having identified single disease.

I think part of the problem is that we're so good at applying various techniques that no one bothers to think anymore.


Well historically the symptom /was/ the disease effectively without imaging to meaningfully separate "appeasing the spirits by ritual boiling" vs "killing microbes". To go with a niche mixed metaphor part of the issue is epistemological low resolution. If the extended "senses" and knowledge are lacking everything is imprecise.

One path of medicine closest to that sort of symptom vs disease separation being practical is phage therapy. It could be called true alternative medicine as an actual alternative as opposed to a euphemism for "false or unproven" but is generally less practical in spite of its other virtues. Because a proper phage needs to be selected per target pathogen. As opposed to not caring what strain of virus it is and just treating the symptoms so the patient can recover and not die.


'Medicine' being a stand-in for 'diagnosis'? Because medical research is all about underlying causes these days. Maybe 50 years ago it was just a corpus of traditional cures. But those days are gone.


>If you tell them they have acute bronchitis, suddenly they feel empowered.

In the The Imaginary Invalid (17th century) Molliere makes fun of it. Doctor explains how opium works through its "dormitive virtue."


It’s funny, but when you point out that a bad analogy is actually pretty accurate if you actually know anything about the other concept, people don’t want to talk about it any more.

We all know that hill climbing algorithms are often naive and sometimes hilariously wrong. Nobody will disagree with you about this, until you start talking about prioritizing work, and then everyone vigorously defends their favorite hill climbing algorithm, from task management to performance tuning.

My preferred strategy for optimization more closely resembles how a fruit grower would pick fruit, and the term, “low-hanging fruit” galls me because you would go bankrupt using that strategy. And probably lose most of your trees to disease. It could be a quite good analogy, if the lessons taken from it weren’t just so childishly naive.


I doubt many people think of it from the perspective of a fruit grower. I think of it as picking wild fruit, in which case the analogy mostly works.


It is a common attribute of 'civilian' fruit trees that the ground beneath them is littered with rotting fruit (and that fruit is a pathogen vector).

We miss a lot of opportunities, and not taking them has lingering consequences.


The low hanging fruit metaphor meaning do the simplest or easiest work first is not really applicable to fruit growing but seems to have been coopted by the busieness world instead, then we started using it too in our contexts. Not crazy about this metaphor either, but it could be useful in some contexts and while also misunderstood in others. The same goes for most jargon.


The “low hanging fruit” makes a lot more sense in a hunter/gatherer context than in an agricultural context


Low hanging fruit is a point in time tactic, not a generalized recommendation


And when you do that for a very long time they call it short-term thinking. Myopia. Missing the forest for the trees.

Plenty of that going around.


Can you elaborate on your preferred strategy?


I'm gonna do this as two posts to split up the how and the why. Here is why and some hints at how:

Perf analysis was how I got through some of my most mundane college homework assignments instead of (or sometimes, in addition to) procrastinating.

Then during my formative years I saw the following script play out several times:

Management and sales have a problem. They say the customers are upset that the code is so slow (maybe the customer has expectations, or maybe a competitor or their old system went a lot faster). It's taking 300 units of time now and they think it should take 100. So the leads and the heavies wander off and come back with 20% easy, the next 10 harder, 6, 4, and then they declare that they've done all they can. So now we're around 180 units of time. That's all the customer gets. See this <flame chart precursor> chart? Everything is nice and flat. Our hands are tied now. Business is... not happy. They're glad they have something to show the customer, but it's really just a bone.

So between tasks I'd start poking around, because why not. A pinch here, an architectural tweak there, and maybe 25% just gruntwork, and I'd find another 40% out of the "we've done all we can" code, which is just a hair away from what the business asked for.

You can imagine that you invite a tremendous amount of well-deserved scrutiny on yourself doing stuff like this. But that's okay: the most important part of optimization is not the code speed but the bang for the buck. You can certainly interpret "Low hanging fruit" to fulfill that concern, in practice most people don't (for some, lack of imagination, for others, ego-protection). But once you consider everything, including, for instance, externalities on QA, or the misapprehensions of customers who expect every release to be faster... there are a lot of more complicated things to worry about than how to rearrange this block of code to improve branch prediction. Not touching it for 10% is just a heuristic.


Performance is just budgeting. It's opportunity costs (versus expected return on the investment). It's time, materials, risk/scheduling uncertainty, and in many cases, externalities. Interestingly enough, I don't think you need to explain any of this to game designers. They seem to know this. It even appears to be the dogmatic in some circles.

When you are way over budget, every cost needs to go on the table, but your end goal is one of the most important pieces of information. That should go on the wall, in large block letters.

Look at a real budget. If you are deep in the red, it doesn't matter that your car is only 10% of how much you spend a month. What matters is that the car is 25% of your target. That is a ridiculous amount to spend on your car, even though your house is way more. Even if you spend an ungodly amount on dry cleaning or coffee, you're going to have to trade down for a cheaper car. You don't need charts to tell you this. In fact all the charts can do is convince you that nothing needs to be done. They allow you to bargain.

Therefore, I don't give a shit if you think that this function which accounts for "only 5%" of the current run time "is fine". It's 13% of our time budget on some mundane task that doesn't really make us money. It is not fine. You cannot justify 1/8th of our budget for this thing. It's gonna be fixed, and if you have no idea how, you'd better start thinking about it now, because we're gonna come back to it soon and if you don't have a solution then you might find that code is now someone else's responsibility.

But I said something about the 'fruit tree' analogy and I'm a full page of text in without a peep.

So the thing is that when you're thinking about big budgetary changes, it's more manageable to do it one subject matter at a time. Pick a 'ripe' area and glean it for everything it's worth. So I might pitch that I think I can get 30% improvement out of the edit/update code. Can I have this much time to do it? Okay. I might get half of that goal in the first couple weeks. Half of the rest in the next, but at the end of it, the last thing I touch is probably only going to get 3%. But 3% is 10% of what I promised, so I can justify it, and if they aren't complaining about the timeline, I may try to squeeze in some 1-2% things at the end in a way I can pull the plug if it looks too risky.

The important thing to note here is that it will never, ever be as cheap as it is right now to touch that 3% code. Everyone is already thinking about it, everyone knows to keep an eye on it, and new test plans are being developed, documentation and customer training plans are being updated. If I don't touch it now, that target of opportunity might never come again. That 3% plays out everywhere in the code, and I will never be able to get budget to fix any of them (I'll have to sneak them in on a refactor or leave them forever). A dozen of these is not that hard to end up with, and our code is 40% slower than it could be (or worst of all, than a competitor). If that's just the time for a button click, fine. If it's on our slowest operation and things time out, customers with larger data can't function, or they have to buy the absolute most expensive machines instead of slightly better than average? That's not fine. But everyone feels perfectly justified in never doing anything about it.

Next release, I can pitch for 25% in some other functional area, and another, and another. Last time I did this, it took over 20 months before I ran out of functional areas to work on. Every release for 'years' was faster than the previous. At the end, I knew that code better than anyone (an important point I omitted - focusing that intently on one section teaches people a hell of a lot about the code they otherwise would never know). I probably could have swung through for another 10% in every area, but by then I was ready for a new gig.


Appreciate the long reply! It sounds like you're advocating a holistic approach, when most people are only focused on the foot in front of them. I've always thought of low hanging fruit as bang for the buck -- if I can do X in 20% of the time, versus Y... but get 50% of the value, then X is low hanging.


Which leads to the saying that this headline is a play on: "All models are wrong, but some are useful." (George E.P. Box)

We probably should be repeating that a lot to make sure people hear it.


Would we encourage an epidemiologist to apply ‘fresh thinking’ to the design of an electrical substation?

Yes, absolutely. If an epidemiologist identifies and models a trend in human disease around substations, or a trend in failures of substations, or a new way of modeling the ways electrical demand can change over time and influence demand in other times and places, etc., then their input should absolutely be considered.

Just as it's annoying when a total outsider claims to know everything about a field, it is equally problematic when insiders refuse to acknowledge anyone on the outside.


That rubbed me up the wrong way too. Applied mathematics is really quite transferable across disciplines, you just usually don't have the domain knowledge to stop you chasing dead ends or reinventing the wheel.


Not to mention that sometimes experts in their fields miss common knowledge in other areas. Like the biologist that reinvented integration, Riemann sum - http://care.diabetesjournals.org/cgi/content/abstract/17/2/1... (https://fliptomato.wordpress.com/2007/03/19/medical-research...).


The easy medium is talking to other scientists, may be getting them on as a co-author.


One feels that, irrespective of the models, the data in the covid-19 case may be unusually bad.

It may be time to add a third error category[1]

I. False positive

II. False negative

III. Deliberately skewed off the map for propaganda reasons.

[1] https://en.m.wikipedia.org/wiki/Type_I_and_type_II_errors#Ty...


This is driving me crazy ;) the poor quality of data.

You'd want health organizations around the world to be publishing every possible detail (anonymized) so that the disease can be better understood. Yet three months in, with over a million cases worldwide, we still have experts disagreeing about things like asymptomatic transmission, use of masks, droplets vs. aerosol, how much distance one should stand from another, viability on surfaces, etc. etc. Even for treatment options rather than insisting on randomized double blind trials start by using the natural experiments that are already happening.

We should have the data to answer a lot of these questions (or at least draw out some probability distributions), or at least someone has it. This stuff is going to be critical in informing exit strategies.


My experience with data is: you never get good data. Always, the closer you look at it the more problems you find in how it was collected.


> You'd want health organizations around the world to be publishing every possible detail (anonymized)

If you're publishing every possible detail, then your data isn't anonymized. Anonymization consists of the removal of almost all of the details.


Or even if the data are reasonably tidy, they might expose more when juxtaposed with another dataset.


An observation from inside academia: there are some (potentially many) researchers who use the opportunity to push obscure models and simulations of their research sub-subdomains, even if they have no knowledge about public health, epidemiology, or even strong past real-world success stories of the methods they are advertising. Sometimes, this happens with a "talk to the media first" approach (before subjection of their work to the scrutiny of actual domain experts or even any form of peer review), which is certainly dangerous.


> The FT chose to run with an inflammatory headline, assuming an extreme value of ρ that most researchers consider highly implausible.

The headline "Coronavirus may have infected half of UK population — Oxford study” is not really out of line with the preprint the article talks about. What's questionable is that it was a good idea to write about the preprint at all.

"Importantly, the results we present here suggest the ongoing epidemics in the UK and Italy started at least a month before the first reported death and have already led to the accumulation of significant levels of herd immunity in both countries."

"Our overall approach rests on the assumption that only a very small proportion of the population is at risk of hospitalisable illness. [...] Three different scenarios under which the model closely reproduces the reported death counts in the UK up to 19/03/2020 are presented in Figure 1 . [...] [In two of those scenearios] By 19/03/2020, approximately 36% (R 0 =2.25) and 40% (R 0 =2.75) of the population would have already been exposed to SARS-CoV-2. [...] [The third scenario] suggests that 68% would have been infected by 19/03/2020."

The secondary headline “New epidemiological model shows vast majority of people suffer little or no illness” was much worse as that's the assumption in the model and not the result. It was changed to "New epidemiological model shows urgent need for large-scale testing" in the amended article.

> Since its publication, hundreds of scientists have attacked the work, forcing the original authors to state publicly that they were not trying to make a forecast at all.

"Attacked" sounds as if the criticism was unwarranted.


Author here: happy to take comments or criticism


I'm finding myself in disagreement with rule #6. Using a model effectively is about a lot more than just the domain knowledge. I'd value analysis from a mathematician/statistician more highly than from an infectious disease physician. There's the stuff that informs models, i.e. the observations, the experimentation etc. and then there's the science of modelling itself which isn't really in the same domain.


Don’t discount domain experts. Models are meant to predict the real world. In order for them to be accurate, the model itself needs to capture how the real world works, and the math underlying the model has to be correct. Domain experts are the most likely to have experience in both areas. Drawing an example, a physicist models the universe and knows the math and model behind electromagnetism. A mathematician probably knows the math but maybe not the model.


I'm at the point where it shouldn't matter who did it, what should matter is its integrity. I'd prefer everything was anonymously posted at some level so author background didn't go into consideration regarding how it was received.

I agree with you completely, but the flip side of the coin is that experts can be blinded by assumptions that the field has. Sometimes outsiders aren't aware of these basic assumptions and so are less biased.

There's also the simple issue that sometimes expertise comes from places you least expect for reasons you might not anticipate.

For me there's as many problems in this pandemic related to appeals to authority (at least in the US) -- problems with testing related to FDA regulation and the CDC, problems with lack of healthcare providers due to long-term rent-seeking monopolies in licensing and practice scope, problems related to academic fraudulence and incentives (see: Didier Raoult) -- that I think it's dangerous to raise appeal to authority as anything but a bias.

For me there's multiple levels of problems to this, the first of which is the conspiracy and anti-science culture surrounding the pandemic. Above that is an appeal to medical and scientific expertise and authority that has sometimes been helpful but sometimes harmful. Above that still is an appeal to rigorous thinking and risk management, which transcends expertise boundaries.


I agree with this - but I do think it should be clear that the model is from outside the mainstream. Not to dismiss it but to clarify its status. Check out the New Yorker piece I link to in the article - it's quite shocking the misinformation that's out there.


Well, we could have benefited greatly from the mainstream media and politicians taking the outside predictions seriously at the beginning of this crisis. Instead we had to wait a month for the Imperial College London to say the same exact thing before certain leaders got their heads out of the sand.

Likewise now with hydroxychloroquine--if you listen to the epidemiologists all you'd hear is how it's an UNPROVEN drug. What we need instead is coverage of sample sizes, p values, bayesian predictions of effectiveness (in the absence of controlled studies) and serious modeling of the number of ICU beds and ventilators required with and without various levels of treatment, from emergency care to prophylactic use.

The epidemiologists have their head in the sand and think we can just wait 6 months for a proper set of randomized trials. It's the less attached data modelers you need to turn to get predictions that are useful for effective policy choices.


That's not really a fair representation. (Harvard epidemiologist) Marc Lipsitch raised the alarm back in February: "it's likely we'll see a global pandemic" of coronavirus, with 40 to 70 percent of the world's population likely to be infected this year."

https://thehill.com/changing-america/well-being/prevention-c...


I do think it should be clear that the model is from outside the mainstream

Which model? Your article mostly dwells on the Oxford model in the FT which is as mainstream as it gets. The Daily Mail article is about a prediction by an Imperial academic.

There doesn't appear to be any "mainstream" in epidemiology. This kind of false-consensus argument is one I've seen before and frankly it undermines people's respect in both academic and journalism. How exactly is the "mainstream" established, in your mind? And don't talk about vague, easily manipulated non-metrics like reputation. Mainstream models should come from a single thing: success. Your insistence on rigid academic segmentation is the very thing that makes academia brittle and riven with non-replicable papers.

You know which kind of modellers I respect the most? Actuaries. They have skin in the game. They don't try to predict pandemics or sell insurance products against them, implying they think pandemics can't be modelled. So far based on what I've read about the history of epidemiology, I can't disagree with them.


Domain knowledge is very, very important in modeling because it helps you figure out what to put in the models. Math is important but certain odds and ends that are of great interest to mathematicians are not helpful for the usual simulation, and the simulations that are most helpful in informing policy.


I very much enjoyed this article you linked with the Times reporter and the law professor

https://www.newyorker.com/news/q-and-a/the-contrarian-corona...


This is a hard problem, mainly because of the problem of motivated reasoning as mentioned by another comment.

You're requiring cooperation from multiple different parties here (scientists, journalists, policy makers, readers, etc.) and any of these parties can warp the results in any number of ways regardless of the cooperation of other parties.

Climate science still hasn't solved this problem despite trying to implement what you're talking about.

It's an age-old problem, if a priori you're looking for something hard enough you're bound to find it.


I think, at the least, if journalists get some quotes from other scientists before publishing a piece on a new model it would be a major win.


I don’t think there’s value in doing this, unless the second opinion disagrees with the original model. It’s not hard to find a second “expert” to agree with just about anything, if you look hard enough.

The number of people who agree with something tells you, as a rational person, practically nothing - it reminds me of the absurd compilation argument “One Hundred Authors Against Einstein”: https://archive.org/details/HundertAutorenGegenEinstein/mode...

Einstein’s famous reply: “If I were wrong, then one would have been enough!”


An article with just opposing positions would just confuse the reader. It's also like how the media has climate skeptics debate climate scientists one-on-one as if there is an actual debate to be had, magnifying the level of climate skepticism in the field.


I think you missed the point of the story.


Journalists and their publications don't give a crap about scientific truth. They care about maximizing clicks and traffic. If a group of scientists give them the truth and another group gives them a more sensational story, they'll print the latter. Scientists will never win this battle.


That's fair. I mean asking folks to be more circumspect in general is probably a good thing.

I prefer though to look at problems through the lens of incentive structures (keeping in mind humans generally heavily time discount incentives and what incentivizes people is not always obvious! Death isn't always much of a disincentive beyond a rather short time horizon). And here I'm having a hard time seeing easy ways to tweak the incentive structure.


Often they do, but that doesn't affect the piece they write. It's not at all difficult to find people complaining "I was interviewed for this article, most of what I said was left out, and to the extent I am quoted, it's to give the impression that my beliefs are the exact opposite of what I explained to the journalist at length".


That would just mean nobody would publish articles about models, as there are no models that don't have some scientists disagreeing with them.

Which is fine. It'd be far better if people weren't trying to model COVID-19 at all. It's clearly far beyond our societies capabilities today. We can't even get useful data let alone feed it into a simulation of society.


Asking more of journalists at this juncture may be a tall order. The bar for them has been trending downward, due largely to a lack of funding and an abundance of competition, many of whom have little regard for journalistic standards.


This is a great start on how scientists (and journalists) should communicate to the public! Thanks for writing this, and the world would be a better place if everyone remembered to follow these principles.

I think in fact it would be better to go even further: not speak from a position of superiority (even if one knows more), but try to acknowledge the audience and persuade effectively. Here's a recent article on the topic: https://undark.org/2020/03/19/coronavirus-myths/ and here's one of my favourites (in a different field of science) from three years ago: https://deansforimpact.org/why-mythbusting-fails-a-guide-to-...


I think starting with the global warming example was a poor choice because there are a lot of vested interests who would find something to attack and distort even in a flawless paper.


If any of the scenarios from the famous Imperial College model turn out to have been based on just-as-bad assumptions, would you be willing to write a follow up about that?


I'm not taking a position on the Imperial College model. I'm explicitly advocating that all models should have their assumptions examined. And that policy makers should use a range of model and not depend on just one.


You will have nothing to add if the “2.2 million deaths in the US” scenario, which was blasted across every newspaper front page a few weeks ago, turns out to have been impossible all along?

If that scenario was “completely wrong” too, it seems like it would serve as a perfect example of the consequences of this kind of (still hypothetical) misinformation.


Wasn't ot explicitely worst case? No measures taken and so on? The imperial articles are free to download and all I have read had assumptions stated very clearly.


We can't rerun the experiment with a control version of the US in which nothing was shut down and we continued to have crowded sports events and night life. So it won't be possible to determine that the 2.2 million deaths scenario is impossible, especially if it's interpreted as 2.2 million extra deaths from either COVID-19 or other causes that could have been treatable by a medical system that wasn't completely overwhelmed.


I’m extremely pissed off at these folks who built an unfalsifiable model, panicked the whole world, and will now pretend that it is “science” as they ignore and downplay the death and destruction that follows in the wake of their foolishness. This will not stand.


Why do you think the Oxford model compares poorly to the Imperial one? Both are created by eminent people in their field. Neither has been peer-reviewed.


The final point seems like a bunk attack. A lot of epidemiologists were upset at Silicon Valley data scientists and hedge fund quants who were putting together prediction models that disputed what the government was saying. Well, so far these models have been more accurate than the official ones.

As we should expect, because data scientists and quants are EXACTLY the people with the set of skills necessary to make such predictive models. At best, it's an ancillary skill for epidemiologist, and we've seen many cases in this pandemic where they have wielded these tools incorrectly.


Your entire post seems to be about #6. And therefore it's entirely wrong.

Given that "people with background in infectious diseases" have largely failed, as a group, to warn us in January-February about this pandemic (see this Twitter thread for a slew of concrete examples https://twitter.com/RokoMijicUK/status/1246509433145917443), my conclusion is that unless someone has a background in hard quantitative field (regardless of what that field is), that person should not be let anywhere near quantitative models.


"the best material model of a cat is another, or preferably the same, cat” - Norbert Weiner


Wiener


I am working on trying to make an accurate model for predicting COVID-19 growth based on the per-county figures we have with COVID-19 (courtesy The New York Times). To say the data is noisy would be an understatement.

What I have found, so far, is that if we look at current daily growth (averaged over seven days) and use exponentiation to predict future growth based on the previous week’s figures, the numbers are too high (usually by a factor of two, but the error amount is all over the place).

Point being, we’re seeing a more complicated growth model than simple exponential growth; the actual growth is lower.

My work so far is on GitHub: https://github.com/samboy/covid-19-html

This is a work in progress and I’m nowhere near being able to make a simple easy to read graph showing a reasonable projection of COVID-19 growth in the United States.


A pretty good model is the one I've found in this random Google Sheet:

https://docs.google.com/spreadsheets/d/1PqMVPU0VYcDWrUnDh1Cs...

Basically, it uses the fact that mean time from contagion to death is 17,3 days. For France, it gives reasonably accurate predictions. Best than almost any other model I've seen, in fact.


It could only be exponential if the population was infinite. It has to be more of a bell curve, because as infection grows, there are fewer hosts to infect, and therefore growth would start to decline.


Even in places where 0.05% of the population has confirmed cases of the virus, I’m seeing the curve flatten. Whether that’s from quarantine of from the virus hitting its limit, I can not say. But the curve is flattening.


Yes that's good news. However when you say the curve is flattening, I believe you mean we have reached peak daily new cases, meaning the number of new cases we see is going to start declining from here.

Collectively many people seem to be referring to that as "curve flattening", but my understanding is that flattening the curve means slowing the growth rate overall, so that it takes us longer to reach peak daily new cases. It is not intended to indicate a particular point along the x axis. In fact if we are actually flattening the curve, it will take us LONGER to reach our peak. Also, its difficult to measure whether we have been successful or not, because the only thing we have to measure against would be hypothetical worse case scenarios.


I'd like to point out that we _still_ don't know if that "high R0" model was right or not. And we won't know that until we randomly test a sufficiently large random sample of the UK population for antibodies. They imposed containment _very_ late, and they go to pubs _all the time_. It is not implausible that the majority of their population already had COVID19 without even knowing what it was.

What we do know is that "doomsday style" models from IHME that just last week were predicting 50K beds needed in NY are off by a factor of 3-4, and hospitalization are starting to flatten out already. You can guess the direction they were wrong in. And before you start an uninformed argument, yes, these models assume the current isolation measures.

In the meanwhile NY hoarded the ventilators and medical supplies because it anticipated this prediction to be true. To be clear, I don't blame NY - they used the best information they had, which turned out to be bullshit. Better be safe than sorry.

These are not harmless errors. When this is over, someone should study these fiascos and estimate the death toll just from bad models alone.

https://twitter.com/AlexBerenson/status/1246465515704463360


Reading the examples, where people jumped on a single mistake to discredit an entire report points to some sad conclusions:

1. Scientific literacy is super low in the general population.

2. Motivated reasoning is rampant. People will believe anything that enables them to do what they wanted to do anyway.


> 2. Motivated reasoning is rampant.

This is an important point. Scientific scrutiny is extremely important, but there is still a difference between a judge that is stern but fair - and one that actively wants you to fail.

Motivated reasoners have no problems holding opposing parties to impossibly high standards while accepting claims without any evidence as valid arguments for their side.

Today, climate scientists have learned the lessons and improved communication and modeling considerably, even to the point where we now how "attribution science" we we can discuss climate change in the context of particular weather events. We also start seeing changes in weather patterns that are hard to ignore even for laymen.

Nevertheless we are still having the same discussions as before.


> We also start seeing changes in weather patterns that are hard to ignore even for laymen.

Hard to ignore by motivated reasoners! Changes in weather patterns observed by individuals using their own experience are not evidence of global climate change. If there's science showing that, then yes, but personal experience doesn't add any value to the conclusion, it just reinforces whatever the person already wants to believe.


I think the author oversimplifies the problems with climate models. They've had numerous mistakes and extremely critical ones. Not least of which is that they haven't factored for Multi-decadal changes in cloud cover albedo. The climate system is incredibly complex and our models do not have good models for all the subsystems that make it up, including the oceanic oscillations like Atlantic Multi-decadal Oscillation, PDO, etc. We can't even predict many of these subsystems with any degree of accuracy so we are hopelessly inaccurate at the higher level.


I agree, and there are the same politician leanings in climate change non-profits and think tanks as with anything else. Sometimes they may justify sightly more alarming views of data to gain more funding; justifying it with, "Well, it's going to be bad anyway"


> We can't even predict many of these subsystems with any degree of accuracy so we are hopelessly inaccurate at the higher level.

Or do we? This sounds like a Fermi problem

https://en.wikipedia.org/wiki/Fermi_problem#Explanation

I may be wrong, though.


This is a good time for schools to go back to teaching about Ignorance:

https://www.nytimes.com/2015/08/24/opinion/the-case-for-teac...


>Would we encourage an epidemiologist to apply ‘fresh thinking’ to the design of an electrical substation? Perhaps we should treat with caution the predictions of electrical engineers about pandemic disease outbreaks.

I kind of disagree with this point. The models we see are mostly statistical models. Anybody with a statistics background (mathematician, physicist, chemist, biologist, engineer, computer scientist, epidemiologist) can have enough of an understanding to make valid points.

Discarding opinions based on somebody's background is an argumentative fallacy in and of itself. You should of course check if somebody is trustworthy, but epidemiologists should be scrutinized under this aspect like everybody else.


wouldn't you need to be an SME to interpret the results properly and put them in their proper context? Not to mention what inputs, variable would actually make sense in the real world?


> Journalists must get quotes from other experts before publishing

No, this isn't enough. This whole way of thinking isn't enough. It's a big part of the reason for the current situation.

Journalists should report what's true, not what Tom, Dick or Harry said. If a journalist isn't qualified to make object-level claims on a given topic, don't write on that topic. For example, if Bob says there's a forest fire, then instead of publishing "Bob says there's a forest fire", you must do enough legwork to tell your readers "There's a forest fire" or "There isn't".

I allow myself to ignore all journalism that don't follow that guideline, and it makes me happier.


That's a bad example. Take the claim:

- 3 million people lost their jobs <citation link>.

- 3 million people lost their jobs, according to <expert>

- 3 million people lost their jobs, according to <expert>, while <another expert> estimates as many as 5 million during the same time frame.

Which one of these is the best framing of "the truth?" Because rarely is something worth reporting on some axiomatic statement of fact. Not only because boolean states don't normally exist - they're not compelling.

A journalist's job isn't just to tell you something happened. But to give you understanding and context, and make it compelling. What you're asking for is Wikipedia, not the news.


Um, so if you want to publish an article on how many people will die of the virus, how do you ensure it's true? Obviously you either don't write the article, or you quote the most reliable projections you can find. But those are still projections.


You can't honestly write "an article on how many people will die of the virus" which gives a number. No one knows.

What you can do is write an article about projections of how many people will die. In it you talk about the data sources being used in the model(s), the assumptions being made in the model(s), and the result they give including likely major sources of error..

You can put more or less detail depending on your audience. But it should always include enough detail that your audience understands that it is an estimate based on assumptions and likely flawed data, and you should always understand the model you are writing about even if you don't explain it.


Uhmm, no? Reality is way too complex for anyone to make a statement about what is "true" - even for domain experts and especially for journalists, whose, at best, entire specialization is "public health". We may be able to state what is "true" for very obvious matters where there are clear dividing lines for a matter to qualify (e.g. forest fire, election results). But when it comes to the sciences, it is incredibly difficult to make any such statement, especially when it comes to emerging research and predictive modeling.

Instead of journalists demonstrating the effect of Dunning-Kruger in a manner similar to what many computer scientists and engineers love to do about unrelated fields, they should rather listen to the experts and try to gather multiple opinions in order to triangulate what is probably correct.


Given a choice between an engineer who did some calculations on an unfamiliar topic, and a journalist who triangulated expert opinions but didn't do any calculations, I'll listen to the engineer.


1) The comment regarding engineers related to the tendency to assume, because one has specialized knowledge in one field, one also is qualified to comment on other topics, such as the ability to discern the "truth" in a highly volatile and complex social situation (e.g. COVID-19)

2) What you are describing is exactly what I mean: There are dozens of experts ("engineers") who have done their calculations but have come to different conclusions. To presume, as a journalist or expert, one's own calculations will provide the "truth" in such a situation is not only extremely arrogant but also, when it comes to a pandemic, extremely dangerous.

It reminds me of that electrical engineer at Imperial College who thought epidemiology is a cake-walk and wrote a paper predicting 5000 deaths in the UK, which stood in stark contrast to that modeling effort by a large group of actual, renowned experts in the field (epidemiologists, virologists, public health scholars) also at the Imperial College, whose estimates have at least estimated 20,000 deaths. The electrical engineer had to quickly backtrack on his claims after hundreds of scientists wrote in. Now imagine, every journalist would do that and directly publish it. That would be far worse than an article that brings up that 5,000 deaths study but also mentions other estimates.


> Journalists should report what is true, not what Tom, Dick or Harry said.

Especially they should not give 33% plausibility to Tom, 33% to Dick and 33% to Harry. Which is what they typically do, and call that "professional journalism."

If Tom represents 95% of scientists and Dick and Harry the fringe 5% also financed by (let say) tobacco industry or oil corporations, or Boeing, or those paid by the CIA, they should not even mention Dick and Harry in the same article (or TV a show), especially not in anything worth a major headline. They should appear with the smallest possible note in some smallest possible corner and with the title like "oil corporations paid these persons to support them again."

Sadly, but that sounds like a dream. The world would be very different then.


What you describe here is a deeply problematic fallacy and in fact what journalists already do. They are very, very far from your hypothetical "33% to Tom, 33% to Dick, 33% to Harry" scenario and it's one of the factors that undermines trust in journalism.

What actually happens is this:

1. Journalists have a story they want to write. They probably already decided what the message is going to be, but let's be generous and assume they didn't.

2. They consult a rolodex of, almost exclusively, government funded academics. This is true even if the story they're writing is about activity or science that takes place only in the private sector and for which the academics in question have no actual experience. They may consult two or three grant funded academics if they want their story to seem especially robust. If they give the private sector a chance to reply at all (often not) they will cite one or two sentences of a brief phone call in which the person being talked to doesn't know what they're trying to defend themselves against and is probably just confused. It's not a real interview with an actual in-house expert.

3. This story is then published as "Experts say, ..." even if what the chosen experts say flatly contradicts common sense or things that can be checked with 10 minutes and a search engine.

4. Readers comment below the line, pointing out the flaws in the story. If a specific company is involved, they may do a blog post explaining their side of the story which the journalists will either completely ignore, or if they think they can get away with it, selectively quote one or two sentences in a misleading way.

Industrial scientists/engineers are sometimes assumed to be inherently evil and untrustworthy. But the whole idea that journalists are untrusted because they very rarely quote people in industry is a saw of the left; it's not true, which is why the examples given always seem so curiously weird and out of date. Tobacco industry? when was the last time you even read a newspaper article about them? Oil corporations? Those firms that have spent the last 15 years rebranding themselves as energy companies because they now make solar panels too?

There's a lot of really good analysis of the trouble journalism has got itself in for, and a significant part of the blame is laid at the feet of journalists uncritically reporting anything academics say as the Whole Truth and Nothing But The Truth, when in fact they routinely contradict each other, make up statistics, report obvious common sense as "findings", are hugely over-confident in their own predictive abilities, mis-use statistics and so on.

A good book to read on the topic is "Wrong" by David Freedman:

https://www.goodreads.com/book/show/8134625-wrong

He's a former journalist and so has direct experience of this problem. He cites many examples where expert testimony caused misleading or wrong stories to be published, but IIRC none of them involved corporate scientists. Mostly academics like nutritionists, psychologists and so on.


> Mostly academics like nutritionists, psychologists and so on.

So it's completely unrelated to the topics I refer to. What I refer to is:

https://www.amazon.com/Merchants-Doubt-Handful-Scientists-Ob...

Claiming that the misinterpretation of science happened only in the distant past is intentional attempt to obscure the real problem.

There is objective truth and it is far from what some people with a lot of money peddle as the truth and what gets replicated across the media. And the media definitely don't cover what effectively advertising campaigns are as such -- paid disinformation for the benefits of some specific corporations or interest groups.


It's funny, we're not even really disagreeing with each other. The difference is I don't see government (academia) as sources of funding any different to a corporation: both sources create an incentive to huge bias, spreading of disinformation and other problems. But too many people and especially journalists like to pretend that getting your money from government grants magically makes those problems disappear. They're willing to criticise work by corporate scientists who may have an incentive to find a certain outcome, but not academics who have just as strong or even stronger incentives to find certain outcomes.

I suspect it comes from academic propaganda about 'free thought', being able to pursue any line of questioning they desire, etc. It's obviously not true. Academics find it impossible to reach a simple conclusion that's reached all the time in the corporate world: "we don't know the answer and cannot know anytime soon".


Regarding rule 2: I stumbled across https://www.sciencemediacentre.org/working-with-us/for-journ... which does a decent job of aggregating expert reactions to questions that pop up in the media.

I have many more issues with current journalism than the author of the blog post, rooted in their "fire & forget" nature of publications (no visible revisions, no corrections, almost all currently accessible articles are too old to be useful or even correct).


Regarding the "all models are wrong" maxim...

Is that statement 100% true for the low-level models that physicists use and develop? In particular, I'm curious if quantum-physics models are 100% right, just not 100% precise.


At the level that you're talking about, you'll start running into the unresolved questions of modern physics.

One of the best ways to look at this is through the Standard Model of particle physics, which essentially defines how the fundamental particles of the universe are related. Between the number of observations and large-scale experiments dealing with high-energy collision products, astrophysics, neutrino detectors, etc., some people consider the Standard Model to be the most thoroughly-tested and verified framework in all of science. That's a pretty grand claim, but hey.

But it still falls short in some ways--for one, it starts breaking down past a certain scale. Classical field theory as defined by general relativity (another model that has had enormous success under test both theoretically and experimentally) and particle physics don't get along. Neither one fully explains reality, and the interface between those two models of reality hasn't been found. That's why people research things like string theory--they're attempting to find a mathematical framework that can resolve those two frameworks, among other things.

So while each of them describes the universe extremely accurately in their own domain (check the sigma values and number of observations of experiments run on the LHC), they're not 100% right, since they can't be correctly extended to cover all scales and frames. The models remain just that--models which provide a useful framework to interpret reality, but don't fully describe the physical reality itself.


Yes, it is completely true, and not in a probabilistic way, either. A model is a mathematical construction which relates observations to predictions. However, there is no epistemic basis by which predictions can be turned into observations; we can never ultimately draw conclusions just based on inferences.

Perhaps you have heard of the idea that "the map is not the territory" [0]. Models can never be exactly descriptions of reality, not without some sort of special rationale and argument from the outside of reality.

In particular, QM models aren't 100% right. Gravity is missing almost entirely from the model (!) and there are some glaring experimental discrepancies, particularly around the vacuum catastrophe [1]. We know that the combination of QM and relativity gives a hybridized model that cannot work at all scales but explains things like the color of gold [2], so we know that there ought to be a single unified model which does work at all scales and has the same explanatory power.

[0] https://en.wikipedia.org/wiki/Map%E2%80%93territory_relation

[1] https://en.wikipedia.org/wiki/Cosmological_constant_problem

[2] https://en.wikipedia.org/wiki/Relativistic_quantum_chemistry


Some predictions of Quantum Electrodynamics have been verified to within 1 part in 100,000,000.

https://en.wikipedia.org/wiki/Precision_tests_of_QED

However, we know it cannot be completely accurate, as it has no way of explaining gravity.


I keep this distinction in mind:

- Models are deliberate simplifications of reality, in order to guide thinking and otherwise pull in only important information

- Formulations (formalizations) are encapsulation of principles into a mathematical framework

While there is significant overlap, the two categories do not overlap 100%. I see formalizations of physics as the latter, and we use the former to help keep our understanding of the latter clear.


The Standard Model has no known contradictions in the real world. https://en.wikipedia.org/wiki/Standard_Model

In "QED" Feynman states that the predictions are as precise as being able to measure the distance between (points in) New York and Los Angeles accurately to within the width of a human hair.

https://en.wikipedia.org/wiki/QED:_The_Strange_Theory_of_Lig...


Neutrino oscillation[1] is a contradiction of the Standard model as-is, no?

[1]: https://en.wikipedia.org/wiki/Neutrino_oscillation


Dunno. (IANAPhysicist) But that's hella cool. Cheers!


Maybe the best thing about models is not the numbers they produce but the insight into how different variables affect each other, how steeply.

But this information is already contained in the model itself. Therefore people should be reading the models, not their results.

The models should of course be verified by comparing their results to empirical data. But that does not often exist with global things like pandemics and climtae change.


All I can think about with these models in general is the book: Jurassic Park. A lot of talk about chaos theory in the book(kind of skimmed over in the movie). I don't know how accurate all of it is, but it seems trying to model how a dinosaur park would work in the modern day could be a lot like modeling what a virus will do(although probably easier to model a Dinosaur park)


Absolutely true. The amount of bogus science being shared is astonishing. Yet, almost no one seems to notice, care or demand better. All that’s said in the article should be, but personally I’ve lost all hope. We might be living through a generation that values TikTok And click bait more than truth, and it won’t end well, but that’s where we are heading.


I think making medical claims is quite punishable if one doesn't have a license. This could easily be extended to include journalists and politicians as well as other areas of science (if the stakes are high enough)

It would work like this:

For each field there has to be a committee similar to the ones medical practitioners already have. If the behavior is not up to standards they do an inquiry and if need be the credentials are stripped.

Someone with credentials may sign-off on articles written by journalists and must do so for laws made by law makers.

Such articles and laws stay active for as long as the credentials are valid. If the person dies a new one has to sign off on it. If that doesn't happen the law is abolished automatically and the archived news article will have to clearly state at the top that such validation is missing as-of [say] march 2043. It may also solicit such review.

We can make a convenient api that allows people with credentials to stick our their neck to approve a publication. The list of professionals endorsing the perspective must be made available from the article or law.


> I think making medical claims is quite punishable if one doesn't have a license.

Well, it's tricky. If you say "I don't believe covid-19 is a serious problem", that can't be punishable as there is a large spectrum of legitimate thinking as to its severity and what trade-offs we should want to make. Such a statement is not remotely like practicing medicine without a license, but some will argue that it is if it could help them shut up the speaker. While saying that "chloroquine phosphate is a good prophylactic for covid-19" to someone who would believe it certainly should be punishable (as attempted murder perhaps! and regardless of whether the speaker is a licensed physician!).

> This could easily be extended to include journalists and politicians as well as other areas of science (if the stakes are high enough)

Journalists? Eh, maybe, but government officers generally have privileges and immunities -- good luck getting them to let those go.

Anyways, the rest of your comment reads like 1984. Quis custodiet ipsos custodes and all that. Regulatory capture and all that. There's no Objectivity. There's no way to set up a mechanism that yields objectively-correct results. All systems will be susceptible to collective delusions and other failures. There's no silver bullet here, and free speech should be part of the mix. Reactionary thinking is fun for the angry, but not good for society.


I didn't put much thought into the post, its a pretty raw idea that could be refined (and of course abused) but it doesn't have to be very Orwellian.

We have professional communities who get things wrong regularly but the rest of the time they do a very good job. Professionals have a tendency to get things right sometimes.

In the media we see journalists interview experts and twist their words into click bait. Journalists loves to attribute the work to professional sources. There is an abundance of professionals who could operate a thumbs up/down interface. Doing this comes with a certain risk to their career. When hiring someone you can pull the articles endorsed from the database. The choice to voice their opinion depends on how much they care about a topic.

The article can be published regardless. The banner will just say something like: "Zero security experts endorsed this article."

Then we will see how many dieticians are willing to put their name under the "Eating chocolate every day can help you lose weight" publication. The endorsement doesn't have to be permanent but the log will be.


> While saying that "chloroquine phosphate is a good prophylactic for covid-19" to someone who would believe it certainly should be punishable (as attempted murder perhaps! and regardless of whether the speaker is a licensed physician!).

I would disagree. As an example in the history, saying "The Earth is a sphere" was once punishable by death. Fake news and pseudo science has always existed, but censorship is not a solution. Only research, education and communication can help, and even then, each generation will always have his own set of questions without answers.

I don't agree with the parent' solution either, for the same reasons.


Lets take a different perspective. The drawback of paper days where one would publish a final version are over. On paper one could only enhance by publishing a retraction or a follow up.

Assuming the author has better things to do others could provide value by endorsement. We have star ratings and thumbs all over the web, it works but the rocket scientist gets as many votes as the 12 year old under an article about rocket science.

I've often had the discussion on wikipedia. The funniest one was 4 so called established editors repeatedly overruling the Nobel prize winner in his area of expertise on an issue that according to the guidelines is left up to editors. Initially he argued the text he added was common sense. When trying to add sources there was only cursory mention in top journals (they assumed it was common sense for their reader) everything else was considered not RS. It struck me how easy it would be to use [for example] university profile pages to host a public key. It doesn't even have to be visible. The WP editors argued it impossible but its easy.

Then them professors can go around and rate peoples publications the way they always do. As a reader I would much enjoy the endorsements.

It's much better than just my own opinion crudely put together without expertise. The journalist probably doesn't know anything either. I wonder, what are we even doing? (me and the journalist) If neither of us can see the false-positives or -negatives nor can do the probability calculations... what is the point of the exchange? I see much greater potential with little extra effort.


Chloroquine phosphate is fishtank cleaner, and -to humans- poison.

Granted, if you're just dumb and ignorant and choose to take it yourself, that's no crime. But telling others to take poison, without telling them it's poison, should be a crime. Of course, in the case of chloroquine phosphate, if you look at the packaging, you'll see it's poison.


Water will also kill you if you drink too much. But telling people that they need to drink water is not a crime.

Chloroquine phosphate is a fish tank cleaner. On the other hand, chloroquine phosphate is a medicine.


> Perhaps we should treat with caution the predictions of electrical engineers about pandemic disease outbreaks

Or former software company CEOs for that matter


I fail to understand how you can reasonably model an unprecedented event in modern history. We have no data on how people will act under a weeks, even months long lockdown. Will they stay indoors and follow guidelines? Maybe. Will they watch their livelihoods get affected, their mental health deteriorate, or get careless over time and break quarantine? Maybe.

We just don't know because we just don't have the data or heck, even anecdotal evidence from similar events in the past


If you look at the models, they have something like an order of magnitude uncertainty. And the reason for that is precisely what you are stating, they rely on future behavior, which we just don't know yet.

However, the utility of the models is to give us a sense of how the different parameters interact. There are parts of the model were we can have a lot of trust, for example that people with severe conditions will need hospitalization, or that people will react quite similar as they reacted yesterday. So for the short term, the models give us quite good guidance, and for the long term, they help to map out scenarios.

So if you actually look at the report in question, you will see that they are actually trying to estimate the impact of various non medical interventions, like encouraging social distancing, by comparing different countries. It is just that newspapers as usual just run with the most immediately digestible number, independent wether that number is important or useful.

The study in question:

https://www.imperial.ac.uk/media/imperial-college/medicine/m...

Some overview video from Dr. Campbell on youtube: (in general, I think his youtube channel is quite good)

https://www.youtube.com/watch?v=c1aoULlMpn0


On the contrary, epidemics are reasonably easy to model (at least at a basic level), and modelling them has a long academic tradition. As with all modelling, it all depends on what kind of detail you want to get out of your analysis/predictions.

If you‘re looking at a simple population infection model, lockdown efficacy is just a factor affecting the contact rate among uninfected actors. You run multiple scenarios for multiple levels of this factor, and see where that gets you.

Or did you mean something else?

(Source: I‘m not an epidemiologist, but as an ecological modeller I work with very similar tools.)


So many of the models I've seen focus on the efficacy of lockdowns in controlling the spread of the disease. Most treat the length of the lockdown as a mathematical variable - X week lockdown leads to Y infection rate.

My point is that we can't reasonably predict how people will behave in lockdown past a certain point. We've never had similar lockdowns in a world that was as globalized, as hyper connected as ours. You could go from 2 to 8 weeks of lockdowns if everyone was living in isolated villages a la 1918 Spanish Flu, but that's not our present world.

How do you model a situation where after 4 weeks of lockdowns, a social media post about food shortages goes viral, causing mass panic and breaking of quarantine?

We can't because we've never had a situation like this, or the tools for spreading (mis) information as we currently do.


There's very little unprecedented events in modern history, and pandemic certainly isn't one.


Complete lockdowns across multiple countries and cessation of all economic activity certainly is unprecedented.


We aren't ceasing all economic activity, fortunately.


If you're interested in this specific area, here is a useful summary of what's known: https://www.thelancet.com/journals/lancet/article/PIIS0140-6...

tl;dr not much


We have recent data from Wuhan.

We have old data from 1918.

This is a white swan, not a black one.

More importantly, the mortality, while much higher than flu, it's still relatively low.

Now imagine a virus as contagious as this one, but with 10% mortality over all age groups. That would be unprecedented and probably cause society meltdown.


> More importantly, the mortality, while much higher than flu, it's still relatively low.

Anecdote: someone was trying to convice me to panic about Coronavirus because "three hundred and [something] people died just today!"

  $ units
  8 billion / 80yr
  /day
"Actually, three hundred thousand people died today. Probably more, even."


I think a lot of people would be very surprised if you told them how many people die in their country every year.

It's nearly 3 million in the US.


How do you reckon social media will impact your models? Rumors and information - real or fake - has never been easier to spread in the world (Wuhan's data becomes less relevant here). We've already seen calls for defiance of lockdowns in right wing circles. In my country, old videos are being circulated to spread misinformation about the treatment.

Again, our data for all older epidemics is applicable to the epidemic in isolation. But there is no way to accurately model how the epidemic interacts with people simply because the way people live has changed drastically from past epidemics.


The headline really cuts off the points nose.


Yeah - I was trying to play off the George Box quote but maybe it's too obscure https://en.wikipedia.org/wiki/All_models_are_wrong


Interesting, I did _not_ know the reference (I'm a SWE, not in scientific community), and I thought it was a clickbait headline. Obviously this differs from the person above me ^ who clicked the link _because_ of the reference.

Just perspective, and I'm very happy to know the reference now.


The reference to the George Box quote in the title is what originally got my attention.


It’s less the obscurity, and more that to a layman, you are saying in the title that modelling doesn't work, or it really doesn’t work. Understanding your audience and all that. Someone should write an article about it so we aren’t misrepresenting our point by trying to be clever and assume the layman understands that which has not been explained to them...


All models are wrong, some are deadly.


It is so strange - when I write exactly the same assertions I am getting downvoted into oblivion lol.


So, I basically agree with everything this article says, but it seems to miss a basic point. If journalists do what this paper suggests, they make less money.

Journalists, and the newsmedia corporations and organizations that employ them, don't run with the most inflammatory headline possible as an accidental fluke of a mistake that they were too careless to catch. Even public sector newsmedia organizations use measures of how widely read their articles are, as a figure of merit to how well they are performing. Private sector newsmedia are rewarded financially in more or less direct proportion to how widely read (or at least clicked on) their articles are, not how well informed the reader is after they're done reading it (if they even do read past the headline).

If there is one less to be learned from this whole Covid-19 debacle (and I'm sure there are several), it is that our entire news ecosystem, public and private, is fundamentally structured wrong for doing what is supposed to be its purpose, which is to make people better informed. It's not bad at it by mistake, it's bad at it as an inevitable consequence of its design.


The problem is on the demand side, not solely on the supply side. People desire reinforcement not challenge. That is natural. I suspect it is true of us all. Those who are capable direct their potential to be challenged in specific ways, allowing themselves to have biases reinforced in the unimportant places. And they have access to the knowledge in their specific areas.

This is the reason for the enduring power of the filter bubble: it is a stable equilibrium because it serves both the purposes of the demand and supply side.

You can test this by making high-reliability websites that state honest priors. You'll get an audience but the audience will be pretty specific to your subject matter and not be popular. No information source has had all of the following characteristics:

* Broad-based popular support

* High information content

* Novel information, i.e. information you can't get elsewhere

* Sustained presence

This may actually be desirable. Novel reliable information is an advantage, but it may not be a present sufficient advantage, and species survival may depend on presently boosting those capable of acquiring and utilizing information advantage. i.e. a time may come when we need to be good at it - if we have more people with this characteristic then, it'll lead to better outcomes.


This is well known in the industry. The attitude among journalists was that they had a sort of professional and civic duty to “feed people their vegetables.” But that’s a bit too high on Maslow’s hierarchy to be thinking about when you’ve laid off 80% of the staff and still barely make payroll. (My dad lived through the peak and much of the decline at our city paper before he got out).


The problem is also in the change of how the demand can manifest. For example, I would never buy a clickbait newspaper. But sometimes I click on clickbait articles online. In both cases, there is a part of me that is interested, and a part of me that knows better. If it only requires a mouse click, the interested part sometimes wins. If it requires taking out my purse, the interested part loses this fight.

The horrible thing about online advertising is that it allows people to make profit from making you look at something, even if it immediately makes you disappointed. From that moment, the need to write non-disappointing articles has decreased significantly.


Definitely true. We could compare it to the food industry (from production to retail including restaurants).

Its ideal purpose is to provide people with nourishing, healthy, enjoyable food.

But its actual incentive is to give people the food they choose to buy, which often isn't nourishing or healthy.


This is true. You can do good journalism and lose money (or have a wealthy backer who funds your loss-making newspaper) or you can make a profit by getting your "journalists" to publish lots of sensationalist stories. Y'know, Clickbait. (source: I ran a newspaper).


What about 538?


Does 538 have broad-based popular support?

How does its viewership compare to that of gossip-based media like E! or anger-based media like Fox or CNN?


> If there is one less to be learned from this whole Covid-19 debacle (and I'm sure there are several), it is that our entire news ecosystem, public and private, is fundamentally structured wrong for doing what is supposed to be its purpose, which is to make people better informed. It's not bad at it by mistake, it's bad at it as an inevitable consequence of its design.

You speak of a failure in design as the root cause of the failure to achieve the purpose of “to inform”.

From a teleological perspective, viewing the media as a tool designed “to inform” would be a mistake, it would amount to nothing more than a supposition.

Having “better informed” people was never the stated purpose of mass media so it would be wrong to say it is not fulfilling its design objectives. What would be applicable here to help us better understand the actual purpose of the media is POSIWID: ”the purpose of a system is what it does”[0]

[0] https://en.wikipedia.org/wiki/The_purpose_of_a_system_is_wha...


If you're smart enough to come up with a point like this, why not use that intelligence to make a less inane point? You're not wrong, you're just correct in a way that responds to a very narrow reading of what your parent comment was saying, and doesn't make any attempt to figure out why they'd be saying it.

Clearly the person you're responding to wants a system that makes people better informed, and I think there's a lot of other people (myself included) who want the same thing. So the question is obviously, how do we change the news ecosystem from what it is, to a system that makes people better informed?


> So the question is obviously, how do we change the news ecosystem from what it is, to a system that makes people better informed?

People tend to want their worldview confirmed. If you want people to be better informed, one way would be to make them angry when they encounter things designed to manipulate them.


>So the question is obviously, how do we change the news ecosystem from what it is, to a system that makes people better informed?

I think that other than marginal gains, this is a pipedream. The reason the news media is the way it is is due to the choices of the people consuming it. A change in the industry itself is unlikely to put a dent into this, because people will just go elsewhere to get the 'news' they want to hear.

We would need some kind of a fundamental change in society. Maybe if we completely revamped the style of childhood education, it would be possible to change what people look for in the news. I think that practically such a fundamental change is next to impossible. People talk a lot about how we need changes to the education system, but they're usually either spewing hot air or only want small changes.


I disagree. The last bit of what you're saying is particularly telling. It seems to me that you think large change is impossible because you think large changes happens all at once, instead of as a series of small changes, and as a result you're undervaluing small changes.


I think the question that needs to be answered before that is "how do you get people to want to be better informed".

If that was already true, there never would have been an Enquirer or Weekly World News.


This is a typical capitalist strategy for displacing blame and therefore responsibility: the supply side says they're just putting out what there's a demand for, and blames the demand for their own actions. The truth is, marketing creates demand, and people can't consume content if you don't create it.

Ultimately, I'm not looking to blame the supply side, either: certainly there's some blame for the demand side too. In fact, I don't believe blame is a productive thing to do in this situation or most situations. But I am trying to change it from the supply side, because when you try to create change from the demand side, you run into more issues with affecting free choice which makes this and many other problems more intractable.

I also think you're wrong about why the Enquirer et al exist: some people take these as just humor and get their information elsewhere, and others want to be informed, but due to bugs in the human brain, think that sources like the Enquirer are good sources of information. Neither of these are "people not wanting to be informed".


>So the question is obviously, how do we change the news ecosystem from what it is, to a system that makes people better informed?

By bettering basic education. People could learn the relationships and interdependecies of the system and society itself, including the role of information. Why not learn everything through RPGs?

This would create a demand for better news sources in the long term.


I don't understand your reasoning for why you think better basic education would increase demand for more informative news sources. Could you explain more?


Sorry for replying late.

Because education increases environmental awareness, and informative news provides it. So you'd want informative news to remain environmentally aware.


The most educated people I know all have at least a handful of opinions that are provably untrue. We're talking about people who correctly modeled economic crashes, developed and implemented SAP modules for a lot of companies you know, and (multiple on some of them) PHDs. Among them, a flat-earther, a guy who believes communism is straight-up awesome, and one who honestly believes that most "corruption" is a made-up attack on the corrupt.

Each time I encounter this, I am stunned. But it still happens.


From the BBC's mission statement: to act in the public interest, serving all audiences through the provision of impartial, high-quality and distinctive output and services which inform, educate* and entertain.*

From the CBC's mandate: the Canadian Broadcasting Corporation, as the national public broadcaster, should provide radio and television services incorporating a wide range of programming that informs, enlightens* and entertains*

From PBS's mission statement: PBS is a membership organization that, in partnership with its member stations, serves the American public with programming and services of the highest quality, using media to educate, inspire, entertain and express a diversity of perspectives. PBS empowers individuals to achieve their potential and strengthen the social, democratic, and cultural health of the U.S.

You'll find that kind of language repeated for most public broadcasters. The corruption of those ideals by private enterprise in the name of profit should not come as a surprise.


"From a teleological perspective, viewing the media as a tool designed “to inform” would be a mistake, it would amount to nothing more than a supposition."

I and perhaps many other people think the media should inform people; that is a statement of its purpose, and it appears that the collective actions of people in society have redesigned it to eliminate most of the informing.

It doesn't have to have ever been designed to inform by specific and aware human intention; if it informed as a side effect of being unoptimized for disinformation, then in that sense it was designed to inform.

By analogy, natural foods are not designed, in one sense, to keep you healthy, as evolution can make some things good for you and others bad.

...but the purpose of eating them is healthy nutrition, and if we started systematically only eating the toxic ones, then we would say something about our society is structured wrong for the purpose that we have of getting nutrition.


> I and perhaps many other people think the media should inform people; that is a statement of its purpose, and it appears that the collective actions of people in society have redesigned it to eliminate most of the informing.

Your expectation that the media should inform people is reasonable but unrealistic. The original designers had fairly specific design goals in mind and it did not explicitly include “to inform”, this can only be tacked on.

Today we have newspapers, radio, TV and of course the Internet as tools of (mass) media.

If you were among the original designers of the newspaper[0], which was the first tool of mass media for centuries, but your preference to inform was out-weighed by competing interests (aka politics), then of course you are well within your rights to be outraged — you are entitled to criticize the design for falling short of your expectations.

In other words, unless you were personally part of the design team, you can’t really speak about what the design should be or should have been, since you are in no position to influence the system’s purpose before it was built.

You can only deal with the consequences of the designers’ creation, which includes working around the limitations of the system as designed, or building a new system from scratch.

A new system that addresses those limitations would not only be expensive to build from scratch, it would also have to compete for attention and funding to be sustainable — the market has to decide if they truly care about being better informed or not.

Facebook, which is perhaps the most modern type of media, is as vulnerable as the others when it comes to ability to misinform.

[0] ”News was highly selective and often propagandistic. Readers were eager for sensationalism, such as accounts of magic, public executions and disasters; this material did not pose a threat to the state, because it did not pose criticism of the state.” culled from Wikipedia: https://en.wikipedia.org/wiki/History_of_newspaper_publishin...


"Your expectation"

I wrote "should"; I am not predicting in my comment that that the media will inform people [more] in the future; therefore calling my "expectation" unrealistic is uncalled for.

Referring to "the original designers" is confusing to me. You seem to be implying that "the media" was designed by some person or entity as a monolith, which is a theory that is unfamiliar to me and not mainstream (that I'm aware of).

Also, you seem to be positing that "the media" was not only created as a monolith but is controlled as one today, since you say that it can only be worked around or replaced. It really seems like a heterodox and far out theory, that "the media" is not subject to to change. Perhaps you could elaborate, since I thought the discussion was about recent changes to the media?


Let us leave aside my use of designers for a moment and turn to the actual businesses that constitute the media.

On the one hand, media businesses are, in most cases, for-profit enterprises similar to other enterprises.

On the other hand, “to inform” or to have better informed citizens is in the public interest. “To inform” essentially an altruistic goal. Something done for the greater good is hard to monetize unless you ask for donations. Altruism as a business model will not pay the salaries of journalists.

My argument is that the two goals: “for profit” and “to inform” are incompatible goals — either you found a non-profit media entity with the altruistic goal of better informing people, or you found a for-profit without the pretension of altruism.

By arguing that “I and perhaps many other people think the media should inform people”, that the media’s collective purpose is to better inform, essentially what you are really saying is that you want for-profit enterprises to behave in the manner expected of non-profit enterprises? As I said, this is a noble but unrealistic ask. This is the crux of our disagreement.


"what you are really saying is that you want for-profit enterprises to behave in the manner expected of non-profit enterprises"

I mean, I do want them to behave differently that they have been previously, I don't know how you could interpret my comments otherwise.

I feel like you are arguing by definition - you have some definition of "for profit entities" that excludes morality. A definition can be anything you like, but I don't think that particular one has a referent in the real world - and it's obvious to me that it shouldn't.

So, for profit businesses operate under constraints already, and the only debate I can imagine is under what sort of constraints.


And particularly, as the concept applies to mass media, quite a few have noted that the purpose seems to be to create the consent of the populace for what the leadership wants to do.

Sometime's that's informing from a real benevolent perspective of let's get everyone on the same page for what's best to the best of our knowledge. Other times it's an obfuscation scheme to allow some to profit off of the rest of us.

https://en.wikipedia.org/wiki/Manufacturing_Consent


> the purpose seems to be to create the consent of the populace for what the leadership wants to do.

Having spent a lot of time working for and with governments I can assure you that most of the time, behind closed doors, they truely hate the press, and wish it would go away. And this is true of even the most open and democratic governments.

Autocratic governments, again and again have shown what they think of a free press.

The idea that mainstream media is a mouthpiece of government just doesn’t add up.


I think it's a question of how far outside the Overton window your own viewpoint lives.

Newspapers may pester governments with annoying questions about specific institutional failures and corrupt individuals, but they'll tend to be aligned on what the institution was supposed to achieve, what faithfully executing an office would look like.

The New York Times will ask the NYPD, "Why did you beat up that guy in a routine traffic stop?" or "How is it possible that you billed more overtime hours than there are in a week?". Not "Why haven't you shot the rich yet?"


That's not an issue unless you think of leadership as a homogeneous blob. Of course they don't like the portion of the media pushing the other guy's cognitive structures; and ultimately that's what they mean when they complain about the media.

The way you can particularly see this is in the suppression of news that doesn't ultimately help out _any_ of the current leaders with it's dissemination, only the populace.


> POSIWID

Can we either make a reasonable-sounding word or phrase that means the thing or just say the thing? Who is going around really saying “POSIWID”?


> If journalists do what this paper suggests, they make less money.

This is clearly true, but I wonder if must always be so.

In academia, lying in a publication can be career-ending, which acts to broadly 'scare them straight'. For academics, it's desirable to be highly read and cited, but to be seen to be dishonest is the end of the road. Could it be possible to create similar incentives for journalists?


>In academia, lying in a publication can be career-ending,

Can be. Among others, Matthew Walker still has a job. https://yngve.hoiseth.net/why-we-sleep-institutional-failure...


> In academia, lying in a publication can be career-ending, which acts to broadly 'scare them straight'. For academics, it's desirable to be highly read and cited, but to be seen to be dishonest is the end of the road. Could it be possible to create similar incentives for journalists?

The problem is that these incentives for academics aren't created out of purity of heart, but because of the system to which they belong; you will no longer be highly read and cited once it is clear that your results can't be trusted. Unfortunately, this seems not to be true in journalism—you can purvey intentionally, and explicitly, wrong information, and it will still be consumed actively by those whose biases are confirmed by it. We can discuss how to change that, but changing what kind of news people want to read is surely even harder than changing what kind of news journalists write and publishers publish.


Well, also as a journalist you can not get away with making up stories. There was a recent case of a high class journalist, who did that and was caught.

https://en.wikipedia.org/wiki/Claas_Relotius

But he did so very brutal so to say. The problem with journalism is mostly not straight lying, but missleading and bending the truth until it fits the agenda. So a classic journalist should not have another agenda than the truth. But this type seems to be very rare today.


What you're saying is probably broadly speaking true for journalists in general. But what many people actually consume news-wise can only be compared on the surface to actual journalism. Consumers of this material have no interest in determining if what they're reading is true, and there are no consequences for those spreading misinformation while pretending to be journalists. https://news.ycombinator.com/item?id=22784665


Best we can tell, Bloomberg's Chinese Big Hack story was completly made up, but it doesn't appear anyone involved suffered any consequences.

https://appleinsider.com/articles/19/10/04/editorial-a-year-...


> The problem is that these incentives for academics aren't created out of purity of heart, but because of the system to which they belong; you will no longer be highly read and cited once it is clear that your results can't be trusted.

That's a feature, not a bug. A system which depends on people being saints, isn't going to work.

> changing what kind of news people want to read is surely even harder than changing what kind of news journalists write and publishers publish

Agreed, I think that's the root here.


It's easy to lie without lying. As a society we replaced any sort of value for our value proxy, money.

So truth ethics etc. are all secondary values.


This sounds good in an edgy r/im14andthisisdeep sort of way. But in reality, having integrity and ethics in business is actually more profitable over the long term.


That really depends on the size of the fish and the size of the pond. If you're a whale in an ocean of minnows (hello Amazon) you can effectively dictate the terms of business, ethics be damned.


> But in reality, having integrity and ethics in business is actually more profitable over the long term.

This seems like a fairy tale you tell young naive business majors with no experience. The real world isn't like that.

You think oil companies, tech companies, defense companies, tobacco companies, banks, etc have been profitable for decades because of integrity and ethics? No you become profitable and wealthy by being amoral and cutthroat then paying PR firms to spread the news about how good you were all along.

Do you think Google, Facebook, Apple, etc are so profitable because they are moral? Do you think Nike became Nike by having integrity and ethics or by exploiting cheap labor overseas?


Thank you for thinking I'm deep an edgy.... But why is long term important? people need to see long term effects to know that it's more profitable. But right now, people see success without ethics, so why should they think long term? There might be a day of reckoning, where these short term gains result in long term catastrophe, but until then, people do what works. And today the only measure of what works is money.


> > It's easy to lie without lying. As a society we replaced any sort of value for our value proxy, money.

> But in reality, having integrity and ethics in business is actually more profitable over the long term.

Both your parent's view and your view could spring from a belief about how the world is, rather than from non-anecdotal hard data. I'd like to believe the more optimistic version, but I find it hard to do so. Do you have some data to support it?


That doesn't sound right. It can be a sustainable way to run a business, but some publications do fine despite a well-earned reputation for dishonesty, such as British tabloids.

More generally, plenty of companies cause negative externalities, profiting by causing damage that they don't have to pay for.


The two richest people on the planet right now are both known for being almost entirely void of morals when it comes to their business dealings.


They're known for this because both activists and entrenched business interests benefit from promoting that narrative. Gates and Bezos both regularly talk about the impact they have on the world and why that matters more than just money - there are reasonable arguments that they've done bad things, but the idea that they're completely amoral voids is just silly.


https://en.wikipedia.org/wiki/United_States_v._Microsoft_Cor....

https://www.theverge.com/interface/2020/4/1/21201162/amazon-...

https://www.cbsnews.com/news/inside-an-amazon-warehouse-trea...

"The only way that I can see to deploy this much financial resource is by converting my Amazon winnings into space travel. That is basically it." - Bezos

Gates's philanthropy is fine, but my comment was on his business dealings. He has a long history of anti-competitive behavior, completely void of morals.


Nestle poisons entire cities and has killed thousands of babies.

Name anyone who died from a single thing 90s Microsoft did.

The modern tech giants take actions every day that are far more anti competitive than anything Microsoft ever did.

kick 4 months ago [flagged]

https://en.wikipedia.org/wiki/List_of_fallacies#Red_herring_...

    Fallacy of relative privation (also known as "appeal
    to worse problems" or "not as bad as") – dismissing
    an argument or complaint due to what are perceived 
    to be more important problems. First World problems
    are a subset of this fallacy.
Nestle is evil. Microsoft is evil. Nestle kills more than Microsoft. Microsoft is still evil.


My bar for evil is a lot higher than "signed exclusive distribution agreements with some OEMs in the 90s."

Illegal? Sure. Evil? So some other millionaires didn't get to become billionaires. Meanwhile the world got a single API to write software against for almost two decades, how many ISVs existed because Microsoft provided an insanely stable (in regards to API churn) platform to develop against?

Hell right now with 2 major mobile OS players there is a huge tax on developers, imagine if the 90s had been a wild west and there had been 4 or 5 major players.

One can also note that given how mobile played out, there is a good chance that the desktop market would have coalesced around a single dominant OS and a secondary minor OS anyway.

And yeah I know everyone is pissed about BeOS, but they also had their chance to be a player until they tried to hard bargain with Apple and Apple walked away!


While I agree with the general feelings about the evil threshold, Microsoft in the 90-ties built and took advantage of a natural monopoly in order to crush their competition and expand in new markets.

Let's not go to the other extreme and downplay what they did. If they had their way, the Internet wouldn't be an open platform and open source would be illegal.

Personally I don't care about exclusive OEM deals. What I care about were the dirty campaigns against open source and them trying to leverage their huge patents portfolio against Linux and later Android. Or them planting shills inside Nokia, weakening it enough for a takeover of their mobile division and then running it into the ground, thus destroying one of Europe's top tech companies. A conspiracy theorist would say that this was economic warfare, maybe sponsored by the US, but for me incompetence is enough for blame.

Or how about their long battle against open standards, like ODF and their use by public institutions?

And the Microsoft of today, in spite of popular belief, isn't very different. Their priorities may have shifted, but even post Balmer they continued to use their patents portfolio against Android and they continued to fight against the adoption of ODF. Their predatory culture is still there and as seen by the ads and aggressive telemetry in Windows 10, as soon as they can gain some economic advantage, they'll take it, regardless of cost to society.

Their "Microsoft changed" marketing campaign has been genius in its execution. On the other hand I'm glad that they are doing well, because otherwise they have the potential to become the biggest patent troll. Just like when their Windows Phone failed to gain traction, "if you can't innovate, litigate" seems to work well.


Start by defining evil... Or are we supposed to take your subjective categorization as an objective standard?


Nobody earns a billion dollars; the only way to get that kind of money is to pay a whole lot of people significantly less than the economic value of their labor. The internet economy has just made it easier to abstract away ethical problems such that you don't recognize them as hugely problematic when you're making them.

With the current system, we're reliant on the whims of a couple of billionaires. Some are relatively benevolent, but far more are on the level of the Kochs, Larry Ellison or the Sacklers. Business schools have taught the "fiduciary duty" doctrine for a long time, which is basically "maximize profit above all else, or we'll find someone else who will".


I don't see the ethical problem. I'm paid significantly less than the economic value of the labor; this is because my company does a lot of coordination work to connect my labor to clients who need it, and I'm not particularly good at that kind of work so I can't effectively do it myself.

I don't think "reliant on the whims of a couple of billionaires" is a fair characterization. In what way does the average person rely on the whims of Larry Ellison?


At least these days, it's not so easy for a journalist to have a career ending mistake. My example for this is Daniel Lyons, for whom I have practically no respect at all as a journalist. His participation in the SCO debacle should legitimately have been career ending (and IMHO was not at all mitigated by his subsequent apology). His "Fake Steve Jobs" work breathtakingly poor judgement, again IMHO. I mean there is trash journalism and then there is making stuff up and hinting that maybe you know more than others do (all the while saying, "It's just a parody"). Let's not even talk about bit that's mentioned at the end of his Wikipedia article (I really hope that's not true!). But let's face it, he's a talented writer. He writes engagingly on topics that are interesting for a lot a people. The fact that he exercises absolutely no judgement whatsoever in his research is unfortunate, but even this is appealing to a lot of people who want to believe what he writes. I suspect that there are more people on HN who love him than hate him. How would you filter this kind of writing without some draconian censor forbidding him from publishing?


This doesn't really solve the Fox News problem: if an audience agrees with what you're saying, they will begin to trust you regardless of the facts. Many people have no way of knowing what the truth really is when they get their news exclusively from the a small group of propagandists coordinating a message.

Who gets to decide when someone is lying? What happens when that power to determine truth from lies is obtained by a bad actor to create a new truth?


> If there is one less to be learned from this whole Covid-19 debacle (and I'm sure there are several), it is that our entire news ecosystem, public and private, is fundamentally structured wrong for doing what is supposed to be its purpose, which is to make people better informed.

No. What is wrong is that people believe the news industry exists to make people better informed. That is an industry PR lie such as "Do no evil", "Fair and Balanced", "Serve and Protect", etc. The truth is that newspapers have always lied because they were created to lie.

"Nothing can now be believed which is seen in a newspaper. Truth itself becomes suspicious by being put into that polluted vehicle."

"I will add, that the man who never looks into a newspaper is better informed than he who reads them; inasmuch as he who knows nothing is nearer to truth than he whose mind is filled with falsehoods and errors." -- Thomas Jefferson

The NY Post was created by Alexander Hamilton solely to attack his political opponents. The lies hamilton printed in the ny post about aaron burr is one of the reasons why he got killed in the duel.

Your frustration stems from a false premise : News industry exists to keep you informed.

Instead of banging your head trying to make your conclusions fit a false premise, why not do away with a false premise?

All men are good. Harvey Weinstein is a man. Therefore, Harvey Weinstein is good.

Would you waste your life trying to find ways to show Weinstein is good? Of course not. You'd work your way through your logic and conclude your premise was wrong and that all men are not good.


Let me start with the following caveat: I’m not convinced journalism’s quality now is worse than any time in the past.

That being said, I think content aggregators are partly to blame. I mean if you’re just reading the daily paper, maybe the front page has exaggerated headlines, but once you’re in the issue it can mellow out. But content aggregators are always trying to find the latest splash.


A small (but important) correction: generally, journalists do not write headlines; sub-editors do. Most subs have been journalists but most journalists do not become subs.


I think the distinction is moot. They all work in the newsroom, and are that side of the "journalistic integrity" divide. Subs are not part of the management team, or commercially motivated.


Exactly. I stated calling systems that work this way "Bad by Design."

Sometimes the design is deliberately setup that way by an individual or group with something to gain, but other times it is just because the system contains different parties with competing interests.

Anytime I see a clearly inefficient or ineffective system the first question I try to figure out is whether it works that way by design. In many cases once I learn more about it, the answer is yes.


I get why corrupt politicans hate the media, which is because it holds them accountable. Why do you choose to help them tear it down? Are we better off with no media and just the raw tweets of our leaders?


The article here is about how the media can often help spread misinformation, which can be really bad in a pandemic, so going to guess that's what they're concerned about.


Thia isn't specific to news. It's everything in the marketplace. Anything for sale is optimized for short term profit -- lowest cost of production, highest impulse purchase pressure -- junk food, fake healthy food, furniture made of shoddy materials, shiny appliances that will break, etc.


Incentives matter


The majority of publications and articles did not got it fundamentally wrong. There is subset on right wing that is popular and got it wrong intentionally. But mainstream had bad article here and there while majority being not fundamentally wrong.


I don’t think the deficiencies of major news organizations have ever been as blindingly obvious to me as they have been over the past month. I knew it was bad. I didn’t realize exactly how bad it was. For them, even a national crisis is just an occasion to twist against Trump. Yes Trump is far, far, far from perfect, but there’s a huge difference between honest, factual reporting and these transparent efforts to use a tragedy to take him down. Nothing more obvious than the attempt to pit Dr. Fauci as somehow being "against" Trump or being "silenced" only for him to come on national radio and set the record straight.


The anti-Fauci stuff is mostly posted by GOP-partisan organizations though as a way of bolstering Trump.

On the other hand, it is pretty standard mainstream news when in the same presser, the doctors are saying the opposite of what the President is saying.


This is simply not true. MSNBC, NPR and PBS ran stories, podcasts, and discussions for an entire week about the “growing disconnect” during this time.


A disconnect that one can divine for oneself by listening to Fauci talk anytime Trump is not in the room.

Birx, too. The difference in messaging and information content when she's on that podium or on Fox programming, versus when she's on normal-people programming, is stark.

They're being leaned on and it is starkly apparent when you're not coming in with priors that tend towards conspiracy.


Sorry, this needs to be said: The only conspiracy here is this drivel here that you wrote. Fauci has repeatedly stated, and acted as such, that there is no divide between him and the president. Is it feasible Fauci does not like Trump personally? Of course. But we are adults, and we have to work with people we don't like every day and still be able to perform our duties. Fauci is a professional.


"Sorry", huh? Right.

Fauci is a professional. That's why he's actively correcting what Trump says at every opportunity. He's downplaying that he's correcting Trump--but he is, and it's necessary, and it speaks to the complete mindfucked stupid coming out of the White House and it should alarm you a lot more than what you're handwringing about throughout all of your comments.

Stooging for these dirtbags will not look any better in the light of history than it does in the light of today. Stop.




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