The Power of Inflexibility in Improving Science and Fighting COVID-19

In case you’re new to this blog, you might not be aware of the ongoing crisis of confidence—also known as the Replication Crisis—in social and life sciences, including but not limited to psychology, medicine and economics. (To learn more, see weeks I-II of my course Critical Appraisal of Research Methods and Analysis.)

In short, major problems include:

  • Less than half (exact number depending on the field) of studies can be replicated
  • Way too few studies can be computationally reproduced, that is, getting the same results from the same data and same analysis code
  • Research tends to ignore context, making generalisability difficult
  • Published studies are reported intransparently, so it’s hard to tell what was actually done – and if p-hacking practices were used (e.g. the results were cherry picked from a large pool of random data)
  • … etc.

There are several initiatives to address these concerns, but where do they spring from, and how can we eventually fix science in large scale? I’m going to suggest a solution which will rub a lot of people the wrong way. Incidentally, it is the same tool we need to fight the Coronavirus. But first, we need to understand Nassim Taleb’s presentation of the minority rule.

The basic idea is, that under particular conditions, once a stubborn niche population reaches a small level such as 3-4% of the total population, the majority will have to submit to the preferences of the minority. For example, consider a children’s party, where the organiser needs to make the decision on whether to offer milk products, as some of the guests are lactose-intolerant. Let us call these the inflexible ones: They would suffer great harm from milk products, so they avoid them. The majority of the guests, the flexible ones, can consume both lactose-free products, as well as those which contain milk. Given that the lactose-free supplies are easily available and of not significantly inferior quality, it makes the organiser’s (as well as those party guests who are inflexible) life much easier to serve no milk products at all.

As another example, during my previous life as a business person, I did a degree where my peers were about 50% Finnish, and 50% other nationalities ranging all the way from Russia to Peru. Us Finns spoke Finnish with each other, but whenever a non-Finnish person entered the group, we switched to English. The proportion of non-Finnish speakers was irrelevant, whenever it was above 0%.

So, an inflexible minority can drastically affect how the majority acts. But the infexibility can also stem from one’s worldview; if you had to decide on a daytime activity with a bunch of friends during Ramadan, and one of them was Muslim, you wouldn’t go to a steak house.

What does this mean for improving science and weakening the Coronavirus?

  • In order to promote good research, transparency advocates need to be inflexible about questionable research practices. To the point that they lose potential career opportunities – although they may, in turn, gain better ones as they can work with likeminded people.
  • In order to smash COVID-19, citizens need to be inflexible about risk behaviours. To the point that some people consider them overzealous and rigid – although it may not matter, if it leads to surviving the crash.

Both of these causes have a very important fractal, or multiscale component: Much of the action is not top-down but happens bottom-up; the individual reels in their family (or immediate research group), who then become norm-setters in their apartment building/neighbourhood (or scientific society of their research area), who again affect local governance (or scientific discipline).

But there are at least three crucial success factors for the behaviour change effect to work:

  1. The inflexible group needs to be spatially spread widely, instead of being confined in particular geographic (or intellectual) pockets, in which case the majority can just isolate and ignore them.
  2. The cost of aligning with the inflexible group needs to be small for the flexible group. For minority members to change behaviour, therefore, it may be necessary to take up some of its costs to the majority – at least initially. The other option is to move steps that are so small they are almost imperceptible.
  3. Crucially, the inflexible group… Does. Not. Budge. People always tend to say that one “must not be so strict”, but there is a reason it is not okay to steal, murder, or cheat upon your spouse “just a bit”. If the inflexibles are perceived to be flexible, after all, the majority can expect to dominate over them.
No rest for the wicked, and no stretching for the inflexible! (Photo: Alora Griffiths on Unsplash)

For our case examples, spatial spread is mostly taken care of: The internet has done much to allow for the minority members to connect, while being perhaps the only ones in their own immediate vicinity passionate about their cause. So I’ll address #2-#3.

Lowering the cost of transparency: In the scientific transparency scene, this means the minority representatives need to spend tons of time learning about transparent research practices (e.g. pre-registration and data sharing, the TOP Factor, etc.). This knowledge they can then either disseminate to the rest of their research group, or act as the person who does most of the heavy lifting required in reporting reproducible work.

Lowering the cost of Coronavirus safety: The anti-Coronavirus advocates, on the other hand, need to make information easily available (as they do in, share it, and translate it – both literally and figuratively. An example would be sharing research studies, ways to make and wear masks correctly, or how to acquire them (if you’re in Finland, check this out to have masks made for you, while donating some to healthcare workers). They may also need to learn about technicalities of video conferencing and other solutions, so that they can readily teach their peers after refusing face-to-face meetings.

Not budging in research transparency: The research transparency people obviously need to refuse co-authoring papers which contain p-hacking, hyperbole or other ways of distorting the findings to improve chances of publication. They need to refuse projects which do not plan to share analysis code (and data, within privacy constraints), ask about transparency before peer reviewing, and walk away from papers where the first author insists on presenting exploratory hypotheses as confirmatory ones, or is not willing to properly discuss constraints to generalisability, model assumptions (stationarity, homogeneity, independence, interference, ergodicity… see here if these are strange words) and sensitivity analyses.

Not budging in Coronavirus safety: The anti-Coronavirus folks need show example by performing hand hygiene, self-isolating, wearing masks, social distancing, and taking their kids off school/daycare – but also making sure their family does the same. In addition, they need to speak out when they see their friends or neighbours acting out risk behaviours, such violating the 2-meter (6-feet) physical distance requirement. They need to make it clear they are only available for meetings via video conferencing, which they’re happy to help setting up.

Remaining steadfast and vocal is not for everyone, and calling out behaviour you perceive to be wrong, can be extremely anxiety-provoking. That’s also why one needs to start with those closest to them. And it is hard to be inflexible in the beginning, when the majority norms are against you and everyone is expected to play along. The “happy” news is, that not everyone needs to be inflexible – just the small minority. (I’m putting happy in quotes, because the minority rule can be leveraged to gradually promote any fascist ideology the majority is foolish enough to tolerate.)

Hence, if you’re the type of person who feels strongly enough to be inflexible about these things, perhaps you can feel comforted by the idea that you don’t need to convert the majority: The stubborn few can create the critical mass and change the world.

Suomenkielisiä työkaluja COVID-19 taisteluun; yksilöille, yrityksille ja päätöksentekijöille

This post curates Finnish translations (mostly NECSI guidelines) for stopping the Coronavirus pandemic. Tälle sivulle olen koonnut hyvinä pitämiäni suomenkielisiä tekstejä. Suomentajana Thomas Brand, ellei toisin mainita. Katso myös pandemioita pitkään tutkineen kompleksisuustieteilijä Yaneer Bar-Yamin haastattelu Suomen tilanteeseen liittyen.

Marraskuussa 2019 sain stipendin turvin mahdollisuuden osallistua Nassim Talebin riskinhallintaryhmän koulutukseen New Yorkissa. Siellä käsittelimme pandemiankaltaisia riskejä ja toimintaa niiden välttämiseksi. Muutamaa kuukautta myöhemmin pääsinkin elämään painajaista nähdessäni, että käytännössä kaikki länsimaat toimivat täysin vastoin varovaisuusperiaatetta (ts. joukkotuhon uhka on aina vältettävä agressiivisin toimin), luottaen “parhaaseen nykytietoon” viiveellä ilmenevän riskin torjumisen sijaan.

Kokous, joka käytiin vuoden 2020 alussa jokaisessa maailman maassa. Lähde: xkcd

Alla hyviä kirjoituksia, jotka ovat pääosin alunperin NECSI-instituutin tuottamia.  NECSI:lla on pitkä historia hallitusten ja järjestöjen kuten WHO:n konsultoinnissa mm. Ebola ja Zikavirus-epidemioita nitistettäessä, mutta myös muissa kompleksisissa ongelmissa, joihin perinteinen matemaattinen mallinnus ei pure. Koronavirus-pandemiaan liittyvään vapaaehtoisten globaaliin verkostoon voi liittyä täältä; tekemistä on käännöksistä some-aktiviteettiin, maskien ompeluun, hengityslaitteiden suunnitteluun, verkkosivujen ja mobiilisovellusten luomiseen ym.!

Lyhyitä perusohjeistuksia:

Ehdotuksia henkilökohtaiselle toiminnalle tilanteen parantamiseksi:

Jos koet lieviä tai kohtalaisia oireita:

Jos osaat ommella, tai muuten luotat kätevyyteesi:

Ohjeita elinkeinoelämän toimijoille:

Ohjeita ja esseitä yhteiskunnallisille päättäjille:

Mallintamiseen, ennakointiin ja pandemiatutkimukseen liittyviä kirjoituksia



Coronavirus, lifestyle diseases and the Shadow Mean

In this post, I introduce fat-tailed distributions and the concept of the Shadow Mean, with implications to how seriously multiplicative events should be taken in the society. [Addendum: If you want a technical treatment of the proper Shadow Mean approach instead of my caricature, see this]

I keep getting struck by how often we see well-meaning educated people comparing phenomena such as terrorism and epidemics to the “as much or more” dangerous lifestyle diseases. I even saw one of the smartest health psychologists I know commit this error in their professorial inauguration speech. Note, that I’m not against preventing non-communicable diseases; in fact, that’s what my dissertation is about. But we need to be vigilant on how risks work.

Here’s a chart from the aforementioned presentation, where you can clearly see that, all else equal, we should be diverting almost all our prevention resources to the biggest killers, which are lifestyle diseases:

Rik causes of death

The problem is, that all else is not equal. Why?

It has to do with a concept called “Shadow Mean” (capitalised for ominosity), which relates to “fat tailed” distributions. I’ll explain more later.

But let us first consider some properties of the Coronavirus pandemic, and how they differ from the common flu – and, by extension, to lifestyle diseases. To do so, I’ll give the floor to Luca Dellanna (Twitter, website), who kindly contributed his thoughts to this blog:

Luca Dellanna: Six unintuitive properties of the current pandemic

1/6: Asymmetry (part I)

“The cost of paranoia is bounded. The sooner we get paranoid, quicker we can get a handle on things, sooner we can confidently go back to business as usual the cost of “letting it happen” is unbounded. Here is the tradeoff in the US: Restrict international travel now and maintain our ability to move freely domestically or keep the flows coming and inevitably have to restrict movement both internationally and domestically. The choice is clear.” – Joe Norman (link)

There is enough evidence that the pandemic is inevitable. The only question is how big and how fast we want it.

The costs of preventing the pandemic are mostly linear. Closing down schools today for one month costs roughly as much as closing them for one month in April. Closing down 3 schools costs roughly half as closing down 6 (assuming the same size).

Instead, the costs of letting the pandemic grow are nonlinear.

Letting the pandemic run today might mean 100 more people infected tomorrow. Letting the pandemic run next week might mean 1000 more people infected the following day.

And it gets worse (see the next point).

2/6: Nonlinearities

“In the US, we have 2.3 million people in prison. I cannot imagine a way to stop #coronavirus from spreading like wildfire among that population. How will federal, state, & local authorities handle this?” – Jon Stokes (link)

Another example of the non-linear consequences of the pandemic.

A pandemic that “knocks-off” (i.e. prevents from working, for any reason) 0.1% of the workforce is bad but not that bad.

A pandemic that “knocks-off” (i.e. prevents from working, for any reason) 0.1% of the workforce in a clustered way is much worse: it means that some companies lose a large percentage of their workforce for a few days or weeks and must close the operations (whereas others are directly unaffected).

A pandemic that “knocks-off” (i.e. prevents from working, for any reason) 0.2% of the workforce is ten times worse than a 0.1% pandemic – for there are less workers to covers those who are sick, for one company closing creates problems downstream the supply chain, and so on.

The worst case is so bad that it makes sense planning for it even if it has low chances to happen (which is itself a strong assumption on too uncertain variables).

3/6: Impact

“The difference between the flu and the coronavirus is that between a tide and a tsunami. The same amount of water, but the impact is different because the tsunami arrives all at once.” – Roberto Burioni (link)

As I explained on Twitter, the problem is not (only) the current mortality, but the mortality we can get if our healthcare system gets overwhelmed. People won’t receive the care they need, even for conditions unrelated to the coronavirus.

“If a juggler can juggle 4 balls letting them drop 1% of time,  then he can also juggle 10 balls letting them drop 1% of time.” – this is how most people estimate mortality. As if healthcare was a fully elastic system.

4/6: Asymmetry (part II)

“Asymmetry. Convex decision. So long as there is no risk of harm from masks & disinfectants, the decision is wise, in spite of the absence of evidence– Nassim Nicholas Taleb (link)

Face masks do not offer full protection, but they do offer some protection. As long as you remove them carefully and they don’t make you sweat (so that you’re tempted to touch your face), they’re better than nothing.

Their cost is minimal and bounded, their benefit is large and unbounded (at least for you: they might save your life).

Of course, there is the argument that face masks are finite and they should be allocated where they’re the most needed. It’s a valid argument. But let’s focus on the asymmetry of the cost-benefit, because it applies to another method as well: washing hands and disinfecting.

Their cost is extremely low. I’m baffled that so few people are doing it first thing while arriving home.

Don’t be penny-wise but pound-foolish with your time.

5/6: Testing

“True epidemic in Iran and South Korea, community spread in Italy, confirmed transmission from Iran to multiple countries, the US basically isn’t testing anybody… and as far as I can tell it’s gauche even to mention [the virus] in public in the United States.” – @toad_spotted (link)

If a country doesn’t like to talk about a problem, it will have to talk about that problem.

Problems grow the size they need for you to acknowledge them.
The virus is already here, it’s just not evenly detected. – Balajis Srinivasan (link)

6/6: Infection

“I just realized that when people say ‘yeah but you won’t die’ they mean ‘yeah you’ll become a carrier and make everyone you see sick but not die’.” – Paul McKellar (link)

There are many replies to “the coronavirus is not that mortal”.

  • “15% mortality in older people (80+ years old) almost means a Russian Roulette if they get infected”.
  • One’s chances of dying depend on the number of infected people he meets in his day-to-day (because the more he meets, the more the chances he gets the virus).
  • We don’t know! There are many reasons that prevent us from pinpointing the mortality of the virus in a way that is predictive of the future. We should assume the worst scenarios until we can rule them out. (Why? Because asymmetry and nonlinearities; the content of points #1 and #4 above.)


[Luca’s newsletter is pretty much the only one I’ve ever found positively thought-provoking; if you want to hear more of his ideas, subscribe here]


Horizontally challenged tails

What does this have to do with lifestyle diseases? Well, while the incidence of the common flu is quite unlikely to quadruple from one year to the next, it is much, much less likely, that the incidence of e.g. cardiovascular disease would do the same.

Let’s look at an example. In the left plot below, you see what a mortality rate from a fat tailed distribution would look like. There are two years, when you have an extreme case – something psychologists are used to just eliminating from the data. Note, that outliers are different from extremes; an outlier may be a badly measured observation, whereas an extreme lies within the conceivable boundaries of the phenomenon.

fat and thin tails
Figure by me; code available here

The left plot could signify a viral epidemic. Say we are living year 26; the mean observed annual mortality would be around 900, and you probably aren’t too worried; things are almost exclusively very calm. But, given the fat-tailed distribution, extreme values are possible and upon surviving year 27, the mean would be almost 6000. Before it’s seen, this is known as the Shadow Mean; there are yet unobserved cases we can infer from the mechanics that produce the fat-tailed distribution, but which are not (yet) observed empirically.

Contrast the situation with that on the right plot, which could signify deaths from accidents in a country like Finland. In 900 years, we still have not observed one with over 2500 deaths (nb. this is just simulated data from a thin-tailed distribution). The mean is about 1000 and if we omit the maximum observation, it remains practically identical.

Figure by Stefan Gasic; see his work here!

N-th order matters

Time and second-order effects – that is, things that happen as an indirect consequence of an event – are of great importance when something extreme happens. Let us run a small scenario. Finland has 5½ million people. Let us consider that 25% would get infected (with a maximum of, say, 50%), and 5% (max. 20%) would require care in a hospital. This would already mean, that we would suddenly have 70 000 (max 550 000) extra patients in the healthcare system, which has been “streamlined” for years. Very different scenario than having the same number of extra patients over the course of a year or a decade – one, which lays fertile ground to second-order effects. These include the impact on people, who wouldn’t have big problems under normal situations, due to having hospital care capacity readily available.

Finally: This is not fearmongering or a call for hysteria. Cold-headed rational decision making calls for taking precautions here. If you stock up so that you can self-quarantine yourself for 14 days in the case of getting ill, and do it gradually by buying little extra every time you go to the store anyway, you are making a good decision. Here’s one more figure by Luca, illustrating the point:

Figure by Luca Dellanna; source

Relevant resources and references: