Wiki Contributions


My previous go-to for understanding why we didn't adopt nuclear power on a massive scale is (even citing some of the same sources and using the same charts). Note that the post summarizes Devanney's book, and the post author does not necessarily agree with the conclusion of the book.

Devanney places a lot of the blame with regulators, in particular the Linear No Threshold model, ALARA legislation and regulator incentives. Do you think this is inaccurate and/or overblown?

If your colleagues are regularly giving unrealistically optimistic estimates, and you are judged worse for giving realistic estimates, clearly your superiors don't care for the accuracy of the estimates all that much. You're trying to play a fair game in a situation where you will be rewarded for doing the opposite of that.

Personally I've had good mileage out of offering to lie to the people asking for estimates. When asked for estimates during a sprint, or the likes, and if I sufficiently trust the people involved I would say something like "You are asking us to do X, which I think will take 2 months. My colleagues are giving estimates of 2-3 weeks, but the previous times they gave estimates like that the project took 6-10 weeks. I'm committed to the project, and if you want to hear that we can do it in 3 weeks I'm happy to tell you that, but I don't think we will finish it within 2 months."

If after that you still find you are being punished for giving realistic estimates, consider not telling the truth?

A shot in the dark, but the Malthusian theory of population suggests war is beneficial to local officials and leaders when they think the younger generation is growing at a sufficiently rapid pace that they are about to be replaced ('vent the testosterone', so to speak). The absence of such a growth spike is a mark against this explanation.

More generously: if the birth rate is below replacement, losing young people in a war has drastic consequences for the population ~20 years from now, since it will at least for a while drop far below replacement. If the birth rate is higher the consequences of losing a fraction of your youngest people are, in the long run, less severe.

The first example seems to be an issue of legibility, not fungibility.

I think the section on Don't Look Up, in particular the comments on the relationship between science and policy, misses the mark in very important ways. The naive model of [science discovers how the world works] --> [policymakers use this to make policy to improve the world (for themselves, or their constituents, or everybody, or whatever)] does not give enough weight to the reverse action - where the policy is fixed, and the science that supports it is promoted until the policy is Scientific(TM). I think most science-that-determines-policy is selected this way (regardless of the intentions of the scientists involved).

If I remember correctly you've mentioned this in a previous COVID post, where you recall that big scientific organisations are subject to all kinds of incentives and constraints, of which "tell the truth, the whole truth and nothing but the truth" is regrettably only one among many. I find this a much more productive lens through which to view policy debates compared to being endlessly frustrated that the Real Science with Actual Answers is not involved in the process.

The link on severity of Omicron infections ( raises an interesting question. They deduce the severity by comparing the number of hospitalisations from Omicron with the spread of the variant 5 to 6 days prior to hospitalisation, which is the correct thing to do if we assume it takes 5 days from infection to developing symptoms severe enough to be admitted to the hospital. My two questions:

  • Are other news sources doing this consistently as well? If they are comparing hospitalisations today with case numbers today that gives the wrong answer by approximately two doubling times, underestimating severity.
  • Is there a reason to believe Omicron may develop symptoms slower/faster than older variants, so that we need to correct these figures?

Since the doubling time of Omicron is so short this has serious knock-on effects.

As an aside, the link does not claim anywhere the patients were admitted for Omicron, just that they were admitted, and diagnosed with Omicron. So for now I'm withholding updating on this.

The paper seems to describe the Delta variant and classify its properties compared to older strains. I'm not an expert so I might well be misunderstanding it, but that paper seems to classify and compare two wild strains, not modify them? Maybe I'm missing something, but what is the relation with omicron?

I thought the fact that South Africa does far more sequencing than other countries in that part of the world (for example, check the reported Delta sequences by country, where South Africa is listed as 25th globally with 11,004 sequenced samples, and the next sub-Saharan country seems to be Nigeria, 49th, 2,075 sequenced Delta samples) is more than sufficient to explain away the surprise that they noticed it first. The fact that they also have a laboratory for virus research is hardly a coincidence.

As far as I know there is insufficient evidence to assume omicron is lab-created, as opposed to, for example, reverse zoonosis or long development time inside a person with a compromised immune response. But even conditional on omicron being lab-created, what reason is there to assume it originates from a lab in South Africa? The twitter threat does not seem to provide an answer.

Would you prefer that the FDA involves itself over that it stands by the sideline?

This seems correct to me, but I don't immediately see the importance/relevancy of this? At any rate the escape is speculative at this point.

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