I’ve personally consulted for a pharma company on biological contamination due to behavioral mistakes (though at lower security levels than the labs involved here) and, based on my experience, I find very plausible that one day a rushed or tired employee makes a misstep.
Of course, I would like to believe that after SARS, protocols for way stronger.

But I also believe that virological labs will have leaks again, eventually. Not if, but when.

And dismissing this risk is, well, very risky.
4/ By the way, “SARS is documented to have escaped a lab four times” is likely to neglect times we don’t know about.

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More from @DellAnnaLuca

22 Feb
TROLLEY PROBLEMS We spend too much time on deciding which way to pull the lever, and not nearly enough time on slowing trolleys down and asking ourselves "why are there people bound to the tracks"?

Thread with examples, 1/N
2/ One example: should Trump be banned? Was the election stolen?

These are lever questions.

The trolley question is: how come fraud and/or "changing the rules at the last minute" are plausible?
3/ It's important to focus on trolley questions, because pulling a lever doesn't stop the trolley – it will keep being a problem in the future.
Read 7 tweets
19 Feb

A WSJ piece claims the US might achieve herd immunity by April (pic below).

Does the claim make sense?

A thread, 1/N
2/ First of all, there are excellent arguments by @EricTopol here.

But let’s talk more in detail about herd immunity from a statistical point of view.

3/ Virus and vaccinations are NOT homogenous. The map below shows that the virus spreads in clusters.

We cannot just average it all over the country. Doing so is detached from reality.
Read 9 tweets
16 Feb
Let me understand. It took hundreds of thousands of years to understand that cows can contribute to greenhouse gases, but a few years of small-scale development of lab-grown meat are enough to say it doesn’t have negative side-effects?
Also: the side-effects of something (not just the product, but the infrastructure needed to produce it, it’s byproducts etc) are different whether it’s “lab-studied” and “industrialized. Small scale and large scale can’t be equated.
Read 5 tweets
15 Feb
and the importance of considering what’s “out of scope”

Thread, 1/N
2/ A classic “surprising phenomenon” is that people offered a bet such as “I flip a coin; if heads, you win $1000, if tails, you lose $950” tend to refuse playing.

The surprise comes from the fact that, in theory, the bet has positive expected outcome: $25.

3/ However, there are two explanations for which it’s rational to refuse such a bet.
Read 11 tweets
12 Feb

Most examples of non-ergodicity are activities in which the outcome of a person completing them many times is lower than the outcome of many people completing it once. For example, Russian Roulette.

But there are cases in which its higher.

2/ First, if you don’t know about ergodicity, I suggest reading this thread:
3/ One classic example of non-ergodicity is Russian Roulette. The expected outcome of 600 people playing it once is 100 dead and 500 winners, whereas the outcome of a single person playing it 100 times is 1 dead and no winner.
Read 11 tweets
8 Feb

How come that as we get better tools to be more productive (software, …), in some jobs, productivity didn’t increase too much?

More tasks that don’t add value, of course. But why do we choose to engage with them, rather than being productive?

2/ To explain this phenomenon, called productivity homeostasis (which roughly translates to “stays the same”), we must first look at a similar phenomenon: risk homeostasis.
3/ The Fence Paradox (see image below) is an example of risk homeostasis: the idea that, when an activity becomes safer, people often react by increasing their risk taking.


Read 13 tweets

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