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Douglas_Knight comments on Why We Can't Take Expected Value Estimates Literally (Even When They're Unbiased) - Less Wrong
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This seems so vague and abstract.
Let me suggest a concrete example: the existential risk of asteroid impacts. It is pretty easy to estimate the distribution of time till the next impact big enough to kill all humans. Astronomy is pretty well understood, so it is pretty easy to estimate the cost of searching the sky for dangerous objects. If you imagine this as an ongoing project, there is the problem of building lasting organizations. In the unlikely event that you find an object that will strike in a year, or in 30, there is the more difficult problem of estimating the chance it will be dealt with.
It would be good to see your take on this example, partly to clarify this article and partly to isolate some objections from others.
Sort of. The possibility of mirror matter objects makes this pretty difficult. There's even a reasonable-if-implausible paper arguing that a mirror object caused the Tunguska event, and many other allegedly anomalous impacts over the last century. There's a lot of astronomical reasons to take this idea seriously, e.g. IIRC three times too many moon craters. There are quite a few solid-looking academic papers on the subject, though a lot of them are by a single guy, Foot. My refined impression was p=.05 for mirror matter existing in a way that's decision theoretically significant (e.g. mirror meteors), lower than my original impression because mirror matter in general has weirdly little academic interest. But so do a lot of interesting things.
Yes, you should compute the danger multiple ways, counting asteroids, craters, and extinction events. If there are 3x too many craters, then it may be that 2/3 of impacts are caused by objects that we can't detect. Giving up on solving the whole or even most of the problem may sound bad, but it just reduces the expected value by a factor of 3, which is pretty small in this context.