I really like the idea behind this post/series. I’d already come across Lindy’s Law/delta T and the rule of succession, by reading other people use it in their predictions, but I had already thought that this was a really inefficient way to learn. I skimmed a few statistics textbooks, but I did not come across a lot of techniques that I actually ended up using.
I also liked the examples you gave. I felt like 1-3 explicit practice Problems at the end would also have been nice like:
Tesla was founded in 2003.
How many years from now does tesla have a 25/75% chance to exist?
Or maybe this is silly?
Anyway...
I knew that the Lifetime of something depends on the time it stuck around and had a rough mental image of the distribution, but so far I did not actually bother calculating it explicitly. So thanks for the heuristics.
Your post actually made me think about how very often the lifetime of something is very dependent on the lifetime of something else whose distribution is better known. Often you can just substitute one probability for the other, but sometimes this is more difficult. For example, when someone is 60 and he has been in the same company for 45 years then I don’t expect him to stay another 45, because I roughly know when people tend to retire which in turn is dependent on the expected lifetime of someone. The most extreme/ridiculous form of this is of course how every long-term forecast you make can be totally dominated by your timelines for AGI.
I really like the idea behind this post/series. I’d already come across Lindy’s Law/delta T and the rule of succession, by reading other people use it in their predictions, but I had already thought that this was a really inefficient way to learn. I skimmed a few statistics textbooks, but I did not come across a lot of techniques that I actually ended up using.
I also liked the examples you gave. I felt like 1-3 explicit practice Problems at the end would also have been nice like:
Tesla was founded in 2003.
How many years from now does tesla have a 25/75% chance to exist?
Or maybe this is silly?
Anyway...
I knew that the Lifetime of something depends on the time it stuck around and had a rough mental image of the distribution, but so far I did not actually bother calculating it explicitly. So thanks for the heuristics.
Your post actually made me think about how very often the lifetime of something is very dependent on the lifetime of something else whose distribution is better known. Often you can just substitute one probability for the other, but sometimes this is more difficult. For example, when someone is 60 and he has been in the same company for 45 years then I don’t expect him to stay another 45, because I roughly know when people tend to retire which in turn is dependent on the expected lifetime of someone. The most extreme/ridiculous form of this is of course how every long-term forecast you make can be totally dominated by your timelines for AGI.