🧵 View Thread
🧵 Thread (11 tweets)

it's ludicrous how few people know about this paper, so, friendly reminder that the fermi paradox was completely resolved in 2018 and it turned out to be because multiplying point estimates of highly uncertain parameters is very bad actually https://t.co/c7NSHTruyb https://t.co/uZjynZA9dG



there was even an SSC post about this paper and still nobody knows about it smh https://t.co/dM5preneZG

just an embarrassing chapter in the intellectual history of humanity tbh. decades of ink spilled over what amounts to a failure to understand that the product of a bunch of independent random variables is ~lognormal (ish) and a highly uncertain lognormal has a very heavy tail https://t.co/g5E5LQnqSc


my favorite point that isn't just "lol git gud at probability" is that the most uncertainty by far in the drake equation is about the rate at which earth-like planets produce life; they argue for uncertainty over 200 orders of magnitude which is where the tail comes from

wow this blew up way more than i expected. if anyone wants to dig into this more @anderssandberg wrote a nice blog post with an FAQ, that includes links to the supplements mentioned in the main paper, and addresses the great filter: https://t.co/cVgG3QkSKl https://t.co/ANrVr0kLf2


i want to be clear that by "resolving the fermi paradox" i don't mean this paper definitely answers the question of whether we're alone in the universe, it in fact argues for tremendous uncertainty about that the *paradox* is about why the drake equation spits out such a big # https://t.co/lFD7wNmnH9


and the analysis in this paper IMO answers that question: it's because multiplying point estimates of numbers that are uncertain across multiple orders of magnitude ignores heavy tail behavior, and especially in this case ignores extreme uncertainty in P(intelligence | planet)

the specific numbers in the paper are also mostly a proof of concept, the analysis i think ends up being pretty robust to fairly different distributions on the drake equation parameters, the broad qualitative picture involving a heavy tail remains https://t.co/Yi2nZMdeh6
