PhD in math. MIRI Summer Fellow in 2016. Worked as a professor for a while, now I run my family’s business.
NormanPerlmutter
Karma: 150
I love blackboards, I was a research mathematician for many years and they have a special place in my heart along with a stick of Hagoromo chalk. But they don’t fit my purposes here for much the same reasons as dry erase boards—they erase accidentally and don’t allow for small writing.
I’m not familiar with them and am curious to learn more. My main concern would be whether they allow for fine-scale writing and erasing, since I am writing in small print with lines close together and erasing line by line. Is there a particular brand that you would recommend?
How is the older version better than the newer version (other than meedstrom’s comment)?
Thanks. This is similar to what I’m looking for, but a bit too small. I’d prefer something the size of an 8.5x11 sheet of paper, but I might give Remarkable a try.
I just quickly browsed this post. Based on the overall topic, you might also be interested in these inconsistency results in infinitary utiliatarianism written by my PhD advisor (a set theorist) and his wife (a philosopher).
I’m curious to learn more about the thesis that caffeine or other stimulant use can completely mitigate the effects of sleep deprivation until 30+ hours without sleep. My own (subjective, anecdotal) experience with caffeine is that occasional (once or twice a week) caffeine use fairly effectively mitigates occasional sleep deprivation if I got say 5-6 hours of sleep the night before as opposed to my preferred 7-8, but is not too effective if I slept less than 4 hours the night before. The more often I use caffeine, the less effective the caffeine becomes, and that furthermore, during periods of time when I use caffeine regularly (say a cup of coffee every day), I get a time several hours after I have my coffee when I have a “caffeine drop” and feel sleepy, so that my overall productivity isn’t much better than if I had no caffeine. I haven’t tried dealing with this by having several cups of coffee a day. That might work with regard to productivity, but I expect the side effects (jitters, energy/crash cycles, difficulty sleeping, and also reduced sensitivity to caffeine negating the euphoric effect of occasional caffeine consumption) would be quite unpleasant to me.
What was your old job, and what is your current job?
“If we add all the percentage point increases (i.e. how many more percentage points serology positive participants experienced persistent symptoms vs serology negative participants—data from table 2) then we get 20.3%.”
I am not sure whether this reasoning is correct. It seems to be dependent on how the symptoms are categorized. For instance, suppose we divided fatigue into moderate fatigue and severe fatigue. The increased probability for each might be 5%, and then you would get 25.3% rather than 20.3%. Or suppose we combined fatigue and poor attention, which are likely correlated. The combined increased probability of “fatigue or poor attention” is likely less than 7.8%, and this would bring you down from 20%.
It seems to me that the best argument against this is that there are less harmful ways to obtain an additional inoculation benefit, through additional vaccination. Either by getting additional shots of Pfizer/Moderna beyond the third shot, or by getting RadVac in addition to Pfizer and Moderna. I would imagine that there is some very large number of Pfizer/Moderna/RadVac vaccinations shots that would have comparable negative effects on health as getting Omicron once (maybe 10 or 100?), and that getting this many vaccination shots would provide much more protection against covid than intentionally getting Omicron.
In the case of Pfizer/Moderna, in my understanding, I don’t think it’s too difficult to get more than 3 shots, as many vaccination sites do not ask about prior vaccine status. I remember reading a news story about a person who got dozens of covid vaccines in order to collect the government incentives for doing so.
I’m not currently planning to get a 4th mRNA shot in the next couple months (let along 10), but neither am I plan but neither am I planning to intentionally give myself Omicron.
Looks like Zvi just wrote a whole post in response to the healthdata.org update. In particular, January 19 was his prediction of a peak of reported cases, not of actual cases.
This is true to an extent. Unvaccinated people are still able to attend. They just would need to forge their vaccination card. I think this is not particularly hard to do, though it’s not trivially easy and many unvaccinated people would not do it for ethical reasons.
Thank you, good explanation. But see also my response to tivelen below.
Healthdata.org (the University of Washington team) released a new projection January 8, projecting that cases in the US (actual cases, not reported cases) peaked January 6. Had you seen this already when you wrote this post, and if not, does it impact your projection of a January 19 peak for the US?
(Edit: added hyperlink)
How is that different from what CraigMichael said? Attending that sort of event is a type of risk compensation.
This is a very helpful analysis. I was independently undertaking a similar analysis, and it’s nice to have this for comparison. I hadn’t thought to exclude pedestrians, pedecyclists, and other non-occupants, nor of excluding single-vehicle crashes.
I think a some important pieces are missing from this analysis, as follows.
1) The final number, 548, is the number of miles that I must drive to accrue one micromort for all passenger vehicle occupants. But I am more interested in how many miles I have to drive to accrue one micromort for myself. The average (mean) car has 1.5 occupants. Assume that the average (mean) crash of interest involves two cars, and deaths are distributed roughly equally between each person involved in a crash, total of 3 people on average. So you would need to drive or ride 3 times as many miles as claimed by Josh’s analysis to accrue one micromort for yourself. A micromort would accrue to any other passengers in your vehicle as well.
(If excluding all accidents with one car, then the assumption of the average accident including only 2 cars is actually off, as some accidents will include more than 2 cars.)
https://css.umich.edu/factsheets/personal-transportation-factsheet (source for average passengers per vehicle)
https://www-fars.nhtsa.dot.gov/Main/index.aspx—starting point for further honing this factor
2) If a passenger car gets in a crash with a larger vehicle such as a semi truck or a bus, likely the occupant of the passenger car will be injured much worse than the occupants of the larger vehicle. (I didn’t look this up but it seems reasonable.) This would bias the results in the opposite direction from excluding motorcycles and pedestrians. More generally, we should be aware of cherry-picking which risk factors we remove from the analysis.
3) Deaths per 100M motor vehicle miles driven stayed approximately constant in a range of 1.0-1.2 per 100 million miles from 2009-2019 but went up dramatically in 2020 to 1.37 deaths per 100M miles (73 miles per micromort) and has stayed at a higher level or even increased in 2021 based on preliminary data.
https://www.usnews.com/news/health-news/articles/2021-06-04/traffic-deaths-increased-in-2020-despite-fewer-people-on-roads-during-pandemic
https://www.nhtsa.gov/press-releases/usdot-releases-new-data-showing-road-fatalities-spiked-first-half-2021
4) Taking out 50% of deaths due to alcohol impairment, so as to account only for the other driver being drunk, seems fine. But I’m less confident that the average lesswrong reader does not drive distracted or drowsy. This seems like an area where we should be careful about being too confident due to self enhancement bias. On the other hand, there are other ways to drive more safely than the average driver. One of the most important of these is maintaining a safe following distance.5) Depending on the context, we might be more interested in the micromorts per mile of interstate highway type driving in particular. This risk is about half the mortality risk per mile as compared with all driving.
http://www.bast.de/EN/Publications/Media/Unfallkarten-international-englisch.pdf?__blob=publicationFile (assuming “motorways” has a similar meaning to interstate-type highways)This figure is relevant, for instance, in answering a question such as “What is my risk of highway death from taking a 1000-mile road trip, as compared with my risk of death from covid over the course of the same vacation?”
Josh’s analysis mutiplies the initial figure of 91 miles per micromort by a factor of roughly 6.
Jost includes factors of approximately
A) 1.51 for excluding motorcycles, bikes, and pedestrians
B) 1.78 for excluding single-car crashes
C) 1.86 for seatbelt wearing
D) 1.24 for drunk, distracted, and drowsy driving. This is dominated by drunk drivingI would reduce factor A, as I think it’s balanced by cases where a passenger car hits a truck. More research on this could be helpful. For now, I’d say to get rid of half of it, make it 1.25
I would get rid of factor B, but replace it with a discretionary safe driving factor (see below)
I would keep factor C
I would keep most of factor D, maybe reduce it to 1.2.Add (or rather multiply) in the following factors
E) Factor of 3 to account for only micromorts accruing to one traveler, not to co-passengers or occupants of other vehicles. This is based on an average of 3 people per crash, a figure that would benefit from further research.
F) Factor to account for being a safer than average driver (beyond the effect of wearing a seatbelt and not driving drunk). I think that it might be reasonable for especially safe drivers to use a factor of 2 or 3 here (which would be comparable to eliminating single-car crashes plus more). A factor of 10 seems like it would be too much. For myself as driver, I think I would be conservative and keep it at 1, absent further analysis. I am safer than average in some of my driving practices but less safe than average in other practices and skills.
One way to get a start at estimating this factor might be to look at your auto insurance rate and compare it to the average rate for comparable coverage of a comparable vehicle in your state. Insurance companies are in the business of rating risk after all.
G) Factor of 2 if considering only interstate highway driving (or other divided limited-access highways).
I would start with 73 miles per micromort, using the rate from 2020. Overall this gives me
73*1.25*1.86*1.2*3 = 611611 miles per micromort for each vehicle occupant, or 1222 miles per micromort considering only interstate highway driving, to be adjusted further by a factor of up to 3 depending on one’s beliefs about one’s relative safety as a driver.
I have not used microcovid much because I am not confident in its predictions and modeling assumptions, or I don’t feel they are clearly enough defined to make the tool useful. The change that would be valuable to me (which I have difficultly operationalizing) would be if Microcovid were improved such that I could be much more confident in its modeling assumptions and could use it without having to try to make lots of guesses about which scenarios are well modeled. Maybe it would be sufficient just to explain which types of assumptions make for robust modeling outcomes (maybe this is already somewhere in the documentation). Otherwise, I will continue not to use it.
I think that in general maybe Microvid works well in low-risk situations but breaks down in high-risk situations.
Prior to the recent Omicron surge and post-vaccination, I tended to estimate my covid risks by looking at reported covid case rates in my area, and assuming that as a fully vaccinated person, my risk of getting covid was likely lower than the average person in my area (Ohio), many of whom are not vaccinated, even if I went to restaurants and bars at about the same rate as I did in 2019.
Some examples of my confusion about microcovid’s modeling assumptions . . .
Looking at the risk profiles for hypothetical other people, for fully vaccinated people in my state (Ohio):
Average person in your area: 11,000 microcovids
Has 4 close contacts whose risk profile you don’t know, in an otherwise closed pod: 6,400
Has 10 close contacts whose risk profile you don’t know, in an otherwise closed pod: 19,000What is the definition of a close contact here? Does this mean somebody who they live with or something like that or just somebody who they regularly hang out with closer than 6 feet? It seems to me that the average person in my area (the mean-risk person since this mean is largely determined by the riskiest people, maybe not the median-risk person) has more or less gone back to normal and would have more than 10 close contacts if you’re counting the people they live with, work with, or hang out with regularly. Or at least closer to 10 close contacts than 4.
Microcovid currently predicts that a fully mRNA-vaccinated person with a cloth mask who spends 8 hours in a bar acquires 380,000 microCovids (38% chance of getting covid), assuming that the average person in the bar went to a bar within the last 10 days. (reduced to 240,000 if the average person within 15 feet 10+ feet away rather than 6+ which seems more likely. But why doesn’t the model care at all about people 20 feet away?) (As a side note, the default assumption was that most of the people in the bar had the risk of “an average person in your area” which doesn’t seem right for a typical bar.)
And furthermore, the risk after 8 hours is equal to the risk after 4 hours, huh? I’d think that in 8 hours more people would be coming in and out, you’d be exposed to more possible infected people.
If this assumption were correct, then over the next week we’d see basically all the bartenders and bar workers here in Ohio getting covid simultaneously. (Or does it just max out at 4 hours so that the covid risk of working at a bar for a week is the same as for 4 hours? That just doesn’t make sense.) Even if half or so of these cases were asymptomatic, it would probably be enough that many of the bars would shut down. Seems unlikely, but I guess we could see if it happens.
Likely one of the missing parameters here is the protection from recent infection. I could imagine that the majority of bar workers who haven’t had covid in the last 3 months will get it over the next month or so, which wouldn’t be enough to shut down many bars.
A one-night stand with somebody who has covid (modeled as kissing for 10 hours) my risk is only 100,000 microcovids. It seems bizarre to me that this risk would be about 1⁄2 to 1⁄4 the risk of going to a bar for 2 hours with 15 random people who had been in bars in the last 10 days. Maybe my intuitions are just way off. I suppose at the bar there could be multiple people near me with covid, and one of them might be much more infectious than the average person with covid. But I would think that all of them together wouldn’t transmit as many viral particles to me as a single person with covid who I am kissing for 10 hours.
Yes, I agree that this nonstandard definition is a crux for this disagreement. Good analysis.
Hmm, suppose an adult had urinary problems and wetted their bed regularly. Which category would you say that fits into? Or somebody whose parents had named them something that they didn’t like and they changed their name and didn’t want others to know their original given name due to aesthetic preferences and social implications of character traits related to that name?
There would be some social harm in sharing this either of these, but would it necessarily be adversarial? Even if others were aligned with the person with the secret, they couldn’t help but look at them a bit different knowing the secret.
Could you give an example of exploring un-endorsed emotional reactions? How is this related to having deeply held values?
Cool idea, I like the historic and low-tech aspect. I will look into it.