Interview with Martin Baron

In Der Spiegel, shortly before his retirement.

On mistakes in journalism:

We [journalists] make mistakes all the time, regardless of who’s in office. We are a highly imperfect profession, like every profession.

[…]

We have to recognize that we have certain flaws. We’re making decisions in real time, we’re moving quickly, we don’t have time to sit back and think about a lot of the implications of what we do. We should do more of that. But things move at a very fast pace.

On Jeff Bezos as a “patron”:

We’re not a charity. Bezos has made clear from the very beginning that we [Washington Post] would operate like a business. We’ve been profitable for years now. We got another profitable year last year despite everything. So, that’s how we function. And that’s a good thing because it’s really important that we have a sustainable business model – if we were operated like a charity and some day he [Bezos] was tired of operating this charity, we would be in a precarious place. I don’t think that the future of journalism depends on so-called “patrons”.

[…]

It does depend on good owners who have a long-term view and will invest strategically. You have to come up with the right strategic model, like Jeff Bezos did for the Post. He changed our strategy from being a regional publication to being a national and even international one. That was a very smart move.

Scott Alexander on ‘the tragedy of legible expertise’

In Astral Codex Ten:

WebMD is the Internet’s most important source of medical information. It’s also surprisingly useless. Its most famous problem is that whatever your symptoms, it’ll tell you that you have cancer.

[…]

This is actually a widespread problem in medicine. The worst offender is the FDA, which tends to list every problem anyone had while on a drug as a potential drug side effect, even if it obviously isn’t.

[…]

The essence of Moloch is that if you want to win intense competitions, you have to optimize for winning intense competitions – not for some unrelated thing like giving good medical advice. Google apparently has hard-coded into their search algorithm that WebMD should be on the front page for any medical-related search; I would say they have handily won the intense competition that they’re in. […]

WebMD is too big, too legitimate, and too canonical to be good.

[…]

Dr. Anthony Fauci is the WebMD of people.

[…] He’s a very smart and competent doctor, who wanted to make a positive difference in the US medical establishment, and who quickly learned how to play the game of flattering and placating the right people in order to keep power. In the end, he got power, sometimes he used it well, and other times he struck compromises between using it well and doing dumb things that he needed to do to keep his position.

[…]

Dr. Fauci (and WebMD) are legibly good (or at least legibly okay). They sit on a giant golden throne, with a giant neon arrow pointing to them saying “TRUST THIS GUY”. […] In order to stay on that throne, Dr. Fauci will need to get and keep lots of powerful allies (plus be the sort of person who thinks in terms of how to get allies rather than being minimaxed for COVID-prediction).

[…]

This means experts can play an important role; they’re people who are legibly mediocre. […] I think our system for producing legibly-mediocre people is a good start. It doesn’t always pick the most trustworthy people. But it almost always gets someone in the top 50%, sometimes the top 25%. There are few biologists who deny evolution, few epidemiologists who think vaccines don’t work, and few economists who are outright communists.

Somewhat related, from Paul Graham:

How to get good advice from experts: ask what they’d do in your situation. Many experts feel they should just tell you all the options and let you decide. But they usually know which is the right one, and asking what they’d do gives them permission to tell you.

Tyler Cowen has changed his mind about Brexit

In Bloomberg:

I no longer think Brexit is a bad idea. I’m not ready to endorse it, because I don’t feel comfortable with the nationalism and populism surrounding so much of the Leave movement, but I no longer wish the referendum had gone the other way.

[…] The problem is that, especially in the last year, the EU has become a less workable political union, especially for the U.K.

Covid-19 has helped to clarify my thinking. […] When rapid, emergency responses become more salient, the case for staying in the EU weakens.

[…]

I still don’t view Brexit as a great decision, but neither do I see it as a terrible one.

Uncertainty and nuance in the COVID-19 era

Raj Bhopal and Alasdair P S Munro, in The BMJ:

Increasing use of traditional and social media by academics has brought many benefits. However, these platforms foster extreme viewpoints by design. Some, such as Twitter, value brevity over nuance, leaving no room for important qualification or uncertainty. Emotional rewards focusing on numbers of followers, likes, or onward transmissions (such as re-tweets), are best achieved by strong opinions, repeated often. Measured, nuanced, unemotional views do not go “viral.” Furthermore, the system creates groups of like minded individuals that listen only to each other.

[…] The need for influence is another contributing factor. Many academics seek influence because it is judged favourably in research excellence and impact evaluations. Some have huge followings on social media, which can help achieve rapid public involvement in research—an important aim.

However, an insatiable appetite for rapid dissemination of evidence has undermined traditional publication in peer reviewed journals. This circumvents the normal checks and balances that ensure appropriate styles of communication. Information about important developments is often made available only in brief press releases and then disseminated without adequate scrutiny through social media channels. Despite a need for speed, the covid-19 pandemic is extremely complex. Collegiate, thoughtful, and mutually respectful dialogue that fully acknowledges uncertainty is essential.

That is why the most reliable sources of information usually do not have huge followings. People don’t like nuance. Social media doesn’t like nuance.

The authors have an interesting suggestion:

Science communication, including appropriate use of social media, should be part of postgraduate training. Learning from the humanities may also help to foster a more holistic perspective on the role of science in public life and policy.

The article is a good complement to another editorial published in The BMJ: “Covid-19’s known unknowns“, by George Davey Smith, Michael Blastland, and Marcus Munafò. From the subtitle – “The more certain someone is about covid-19, the less you should trust them” – to the last paragraph, it is a great read.

In the “science” of covid-19, certainties seem to be everywhere. Commentators on every side—academic, practitioner, old media or new—apparently know exactly what’s going on and exactly what to do about it.

[…]

[W]e are thinking of the many rational people with scientific credentials making assertive public pronouncements on covid-19 who seem to suggest there can be no legitimate grounds for disagreeing with them. If you do, they might imply, it’s probably because you’re funded by dark forces or vested interests, you’re not evidence based, you’re morally blind to the harm you would do, you’re ideologically driven (but I’m objective), you think money matters more than lives, your ideas are a dangerous fantasy . . . . On they go, duelling certitudes in full view of a public desperate for simple answers and clarity—even when, unfortunately, these may not exist.

[…]

Views polarise alongside the increasing certainty with which they are expressed, as if we are in a trench war where giving an inch risks losing a mile

[…]

Acknowledging uncertainty a little more might improve not only the atmosphere of the debate and the science, but also public trust. If we publicly bet the reputational ranch on one answer, how open minded can we be when the evidence changes?

[…]

Similarly, to allege that anyone who speaks of uncertainty is a “merchant of doubt” or exposes science to attack from these quarters, is to concede vital scientific ground by implying that only certainty will do. Generally, and particularly in the context of covid-19, certitude is the obverse of knowledge.

[…]

When deciding whom to listen to in the covid-19 era, we should respect those who respect uncertainty, and listen in particular to those who acknowledge conflicting evidence on even their most strongly held views. Commentators who are utterly consistent, and see whatever new data or situation emerge through the lens of their pre-existing views—be it “Let it rip” or “Zero covid now”—would fail this test.

It was published in October, and it still holds true. I don’t think there have been any improvements in this regard. Actually, things have probably gotten worse.

The WHO guideline on drugs to prevent COVID-19 is flawed

This is from James Watson (the researcher behind that open letter about the fraudulent hydroxychloroquine paper in The Lancet):

This recommendation is basically saying that HCQ has either no effect or a negligible effect -> should not be used

This would suggest that the uncertainty around the primary outcomes of interest are very well characterised. Have they been for HCQ (or any drug) in prevention of COVID-19?

The top outcome for the guidelines panel is mortality (separate debate whether this is right choice)

Only 1/6 trials had deaths (Mitja et al): 5 deaths in HCQ arm (n=1116) and 8 deaths in control (n=1198). This gives the highly noisy estimated OR of 0.67 with a CI of 0.2-2

At the lower end this would be a fantastically good drug – at the upper end this would be a killer drug!

So how the heck does this endpoint get decided as “High quality evidence” that HCQ confers “No important difference in mortality”? That’s bonkers – this is orders of magnitude underpowered to say anything of interest.

Compare this to the same WHO guideline on dexamethasone:

The RECOVERY trial had 930 deaths out of 3883. The odds ratio for death was 0.82 (0.72-0.94). This was graded as “Moderate evidence with serious risk of bias”.

???

[…]

I’m definitely not a HCQ true believer – but we really need these guidelines to make some sense

The guideline was published in The BMJ.

Paying people to be vaccinated could backfire

By George Loewenstein and Cynthia Cryder, in the New York Times:

Two prominent economists, N. Gregory Mankiw and Robert Litan, and the politicians John Delaney and Andrew Yang have proposed or supported paying Americans to receive the vaccine. At first glance, this seems like a reasonable idea; economics teaches us that people respond to incentives. But behavioral research suggests this strategy could backfire.

Humans don’t respond to incentives like rats pressing levers for food; they try to interpret what being offered payment means. In this case, the offer risks implying that the vaccine is not a thing of value.

[…]

A more promising approach might be to make desired activities, such as travel, contingent on vaccination. […] If a vaccination becomes associated with enjoyable outcomes, such as travel and access to large public events, vaccination itself will become positively valued. When people perceive the various benefits of vaccination, skepticism is likely to evaporate, at least for some.

Agnes Callard: ‘I want you to listen’

In the New York Times:

For you, there is only one question: how much suffering can she legitimately lay claim to?

You are so busy trying to answer this question — trying to serve as judge in the pain/suffering/disadvantage Olympics — that you cannot hear anything I am trying to tell you. And that means I can’t talk to you. No one can sincerely assert words whose meaning she knows will be garbled by the lexicon of her interlocutor.

The Economist’s country of the year: Malawi

It’s all about democracy:

But this year’s prize goes to a country in southern Africa. Democracy and respect for human rights regressed in 80 countries between the start of the pandemic and September, reckons Freedom House, a think-tank. The only place where they improved was Malawi.

To appreciate its progress, consider what came before. In 2012 a president died, his death was covered up and his corpse flown to South Africa for “medical treatment”, to buy time so that his brother could take over. That brother, Peter Mutharika, failed to grab power then but was elected two years later and ran for re-election. The vote-count was rigged with correction fluid on the tally sheets. Foreign observers cynically approved it anyway. Malawians launched mass protests against the “Tipp-Ex election”. Malawian judges turned down suitcases of bribes and annulled it. A fair re-run in June booted out Mr Mutharika and installed the people’s choice, Lazarus Chakwera. Malawi is still poor, but its people are citizens, not subjects. For reviving democracy in an authoritarian region, it is our country of the year.

I keep a list of all the countries that won The Economist’s “country of the year” award.

The most important statistical ideas of the past 50 years

By Andrew Gelman and Aki Vehtari:

The eight ideas below represent a categorization based on our experiences and reading of the literature and are not listed in chronological order or in order of importance. They are separate concepts capturing different useful and general developments in statistics.

Each of these ideas has pre-1970 antecedents, both in the theoretical statistics literature and in the practice of various applied fields. But each has developed enough in the past fifty years to havebecome something new.

The ideas are:

  • counterfactual causal inference
  • bootstrapping and simulation-based inference
  • overparameterised models and regularisation
  • multilevel models
  • generic computation algorithms
  • adaptive decision analysis
  • robust inference
  • exploratory data analysis

We consider the ideas listed above to be particularly important in that each of them was not so much a method for solving an existing problem, as an opening to new ways of thinking about statistics and new ways of data analysis.

To put it another way, each of these ideas was a codification, bringing inside the tent an approach that had been considered more a matter of taste or philosophy than statistics[.]

There is also a section on the most important ideas of the next few decades.

Article here. Gelman’s post here.

You can’t have the best of all possible worlds using virtual machines

It would be great to have these:

  1. A computer with Apple silicon and virtual machines (preferably on VMware Fusion)

  2. A computer with Windows, Hyper-V (for Windows Subsystem for Linux 2 and Windows Sandbox) and virtual machines (preferably on VMware Workstation)

Number 1 is impossible for now. Number 2 seems to be a little hit-or-miss. Both VMware Workstation and VirtualBox can run when Hyper-V is enabled, but the performance is subpar (especially for the latter).

Apple silicon and Hyper-V are great, except when they’re not.