I'm not convinced this is a problem with the reasoner rather than a problem with the scenario.
Let's say we start with an infinite population of people, who all have as a purpose in life to play Russian Roulette until they die. Let's further say that one in a trillion of these people has a defective gun that will not shoot, no matter how many times they play.
If you select from the people who have survived 1000 rounds, your population will be made almost entirely out of people with defective guns (1 / 1e12 with defective guns vs 1/6e80 with working guns who have just gotten lucky).
Alternatively, we could say that none of the guns at all are defective. Even if we make that assumption, if we count the number of observer moments of "about to pull the trigger", we see that the median observer-moment is someone who has played 3 rounds, the 99.9th percentile observer-moment has played 26 rounds, and by the time you're up to 100 rounds, approximately 99.999999% of observer-moments are from people who have pulled the trigger fewer times than you have survived. If we play a game of Follow the Improbability, we find that the improbability is the fact that we're looking at a person who has won 1000 rounds of Russian Roulette in a row, so if we figure out why we're looking at that particular person I think that solves the problem.
For example, let's say that I think I have about 20 years left to live (because of AI timelines). Then a micromort is 10 expected minutes of life lost. According to Wikipedia, skydiving costs 9 micromorts per jump so I lose 90 expected minutes of life every jump. Also according to Wikipedia, motorcycling costs 1 micromort every 6 miles so I lose about 2 expected minutes of life per mile I spend motorcycling.
An interesting conclusion here is that if you save more than 2 minutes per mile (going 15 mph instead of 10mph, for example) of time sitting in traffic by riding a motorcycle instead of driving a car, the number of not-sitting-in-traffic minutes of your life actually increases by riding your bike.
Though note that the above calculation assumed that your risk of dying in a motorcycle crash while weaving through stopped traffic is not higher than 1/6 micromorts per mile. Also note that if your expected life span is 60 years, you now need to save 6 minutes per mile to "break even", which is unlikely -- if traffic is moving that slowly (remember that this is based on the average speed across the entire trip) you're probably better off walking.
How many seconds have you been in the room?
Let's say the time between t1 and t2 is 1 trillion seconds. Let us further assume that all people go through the rooms in the same amount of time (thus people spend 1 second each in room A, and 1 million seconds each in room B).
100 trillion of the 100.1 trillion observer moments between 0 and 1 seconds in a room occur in room A. All of the observer moments past 1 second occur in room B (this is somewhat flawed in that it is possible that the observers don't all spend the same amount of time in a given room, but even in the case where 100 million people stay in room A for 1 million seconds each, and the rest spend zero time, an observer who's been in a room for 1 million seconds is still overwhelmingly likely to be in room B. So basically the longer you've been in the room, the more probably you should consider it that you're in room B).
If an observer doesn't know how long they've been in a given room, I'm not sure how meaningful it is to call them "an" observer.
I have taken the survey.
What does 0.01% on the wrong answer get you?
It was a memetic hazard.
(not really)
I'm not sure, not having read the paper, but I would expect that "Lifetime manic features at age 22-23 years" means "number of manic features experienced in the time prior to 22-23 years of age" (i.e. we measured IQ of a bunch of 8-year-olds 15 years ago, and those people are now in the range of 22-23 years of age, and we ask how many manic episodes they've had in that time).
Why tunnels, not canals? Particularly in the case of Denver, you've got a huge elevation gain, so you'd need the locks anyway, and digging tunnels is expensive (and buying farmland to put your canal through is relatively cheap).
My impression is that DNA repair mechanisms get dramatically less effective with age, and that piRNA and siRNA (and other such transposon repression mechanisms) are effective but not 100% effective even in germ cells. Since germ cells in males continue to divide through the entire lifespan, my naive expectation would be that the children of very old men to age faster than the children of younger men (not just "have worse health outcomes in general" but specifically "express the specific marks of senescence earlier").
Is that a valid prediction of the "transposons make more transposons and eventually the exponential increase in the number of transposons kills the cell" hypothesis?