jsteinhardt's Comments

Covid-19 6/11: Bracing For a Second Wave

My prediction: infections will either go down or only slowly rise in most places, with the exception of one or two metropolitan areas. If I had to pick one it would be LA, not sure what the second one will be. The places where people are currently talking about spikes won't have much correlation with the places that look bad two weeks from now (i.e. people are mostly chasing noise).

I'm not highly confident in this, but it's been a pretty reliable prediction for the past month at least...

Estimating COVID-19 Mortality Rates

Here is a study that a colleague recommends: https://www.medrxiv.org/content/10.1101/2020.05.03.20089854v3. Tweet version: https://mobile.twitter.com/gidmk/status/1270171589170966529?s=21

Their point estimate is 0.64% but with likely heterogeneity across settings.

Quarantine Bubbles Require Directness, and Tolerance of Rudeness

I don't think bubble size is the right thing to measure; instead you should measure the amount of contract you have with people, weighted by time, distance, indoor/outdoor, mask-wearing, and how likely the other person is to be infected (I.e. how careful they are).

An important part of my mental model is that infection risk is roughly linear in contact time.

Quarantine Bubbles Require Directness, and Tolerance of Rudeness

As a background assumption, I'm focused on the societal costs of getting infected, rather than the personal costs, since in most places the latter seem negligible unless you have pre-existing health conditions. I think this is also the right lens through which to evaluate Alameda's policy, although I'll discuss the personal calculation at the end.

From a social perspective, I think it's quite clear that the average person is far from being effectively isolated, since R is around 0.9 and you can only get to around half of that via only household infection. So a 12 person bubble isn't really a bubble... It's 12 people who each bring in non trivial risk from the outside world. On the other hand they're also not that likely to infect each other.

From a personal perspective, I think the real thing to care about is whether the other people are about as careful as you. By symmetry there's no reason to think that another house that practices a similar level of precaution is more likely to get an outside infection than your house is. But by the same logic there's nothing special about a 12 person bubble: you should be trying to interact with people with the same or better risk profile as you (from a personal perspective; from a societal perspective you should interact with riskier people, at least if you're low risk, because bubbles full of risky people are the worst possible configuration and you want to help break those up).

Quarantine Bubbles Require Directness, and Tolerance of Rudeness

I think the biggest issue with the bubble rule is that the math doesn't work out. The secondary attack rate between house members is ~30% and probably much lower between other contacts. At that low of a rate, these games with the graph structure buy very little and may be harmful because they increase the fraction of contact occurring between similar people (which is bad because the social cost of a pair of people interacting is roughly the product of their infection risks).

Estimating COVID-19 Mortality Rates

I'm not trying to intimidate; I'm trying to point out that I think you're making errors that could be corrected by more research, which I hoped would be helpful. I've provided one link (which took me some time to dig up). If you don't find this useful that's fine, you're not obligated to believe me and I'm not obligated to turn a LW comment into a lit review.

Estimating COVID-19 Mortality Rates

The CFR will shift substantially over time and location as testing changes. I'm not sure how you would reliably use this information. IFR should not change much and tells you how bad it is for you personally to get sick.

I wouldn't call the model Zvi links expert-promoted. Every expert I talked to thought it had problems, and the people behind it are economists not epidemiologists or statisticians.

For IFR you can start with seroprevalence data here and then work back from death rates: https://twitter.com/ScottGottliebMD/status/1268191059009581056

Regarding back-of-the-envelope calculations, I think we have different approaches to evidence/data. I started with back-of-the-envelope calculations 3 months ago. But I would have based things on a variety of BOTECs and not a single one. Now I've found other sources that are taking the BOTEC and doing smarter stuff on top of it, so I mostly defer to those sources, or to experts with a good track record. This is easier for me because I've worked full-time on COVID for the past 3 months; if I weren't in that position I'd probably combine some of my own BOTECs with opinions of people I trusted. In your case, I predict Zvi if you asked him would also say the IFR was in the range I gave.

Estimating COVID-19 Mortality Rates

Ben, I think you're failing to account for under-testing. You're computing the case fatality rate when you want the infection fatality rate. Most experts, as well as the well-done meta analyses, place the IFR in the 0.5%-1% range. I'm a little bit confused why you're relying on this back of the envelope rather than the pretty extensive body of work on this question.

Ben Hoffman's donor recommendations

I don't understand why this is evidence that "EA Funds (other than the global health and development one) currently funges heavily with GiveWell recommended charities", which was Howie's original question. It seems like evidence that donations to OpenPhil (which afaik cannot be made by individual donors) funge against donations to the long-term future EA fund.

RFC: Philosophical Conservatism in AI Alignment Research

I like the general thrust here, although I have a different version of this idea, which I would call "minimizing philosophical pre-commitments". For instance, there is a great deal of debate about whether Bayesian probability is a reasonable philosophical foundation for statistical reasoning. It seems that it would be better, all else equal, for approaches to AI alignment to not hinge on being on the right side of this debate.

I think there are some places where it is hard to avoid pre-commitments. For instance, while this isn't quite a philosophical pre-commitment, it is probably hard to develop approaches that are simultaneously optimized for short and long timelines. In this case it is probably better to explicitly do case splitting on the two worlds and have some subset of people pursuing approaches that are good in each individual world.

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