Thanks for running the contests and doing this write-up!
That is, he is giving a 5-10% probability of negative billionaire growth, i.e., of losing a billionaire, as, in fact, happened. In hindsight, this seems like a neat example of quantification capturing some relevant tail risk.
Perhaps if people had looked to this estimate when making decisions about earning to give or personal budgeting decisions in light of FTX’s largesse, they might have made better decisions. But it wasn’t the case that this particular estimate was incorporated into the way that people made choices. Rather my impression is that it was posted in the EA Forum and then forgotten about. Perhaps it would have required more work and vetting to make it useful.
I was going to write that this is giving me way too much credit, but looking back at my post I’m not so sure!
I modeled the number of billionaires as Number of EAs × P(Is a billionaire | Is EA). At first I thought all the probability mass below 0 came from the number of EAs dropping below current levels, not from P(Is a billionaire | Is EA) dropping. But then I remembered that I assumed a P(Is a billionaire | Is EA) lower than the current base rate, by using the base rate for Ivy league graduates as a lower bound.
I wrote (emphasis added):
As far as I can tell, P(billionaire|effective altruist) is somewhat smaller than P(billionaire|Harvard graduate) in the wake of FTX, though Harvard graduates have some advantages over effective altruists, like being much older on average, better credentialed and perhaps more talented and well-connected. I think this is evidence that there really is something special about effective altruists. They (or I suppose I should say, we) seem to be unusually likely to be very wealthy, and perhaps also unusually likely to become very wealthy. But I also think it suggests that P(billionaire|effective altruist) will at some point regress towards the saner Ivy League mean.
I guess it regressed sooner than I expected (or anyone wanted).
It’s also worth pointing out that there are now at least two, not one, fewer EA billionaires than last summer (SBF and Gary Wang, AFAIK). If that remains the case in 2027, my forecast will have been very bad; I did not account for the fact that all billionaires, and these two especially, were to some extent correlated. Of course there’s a chance there are new ones too in the next years to make up for the deficit; one can hope.
Thanks for running the contests and doing this write-up!
I was going to write that this is giving me way too much credit, but looking back at my post I’m not so sure!
I modeled the number of billionaires as Number of EAs × P(Is a billionaire | Is EA). At first I thought all the probability mass below 0 came from the number of EAs dropping below current levels, not from P(Is a billionaire | Is EA) dropping. But then I remembered that I assumed a P(Is a billionaire | Is EA) lower than the current base rate, by using the base rate for Ivy league graduates as a lower bound.
I wrote (emphasis added):
I guess it regressed sooner than I expected (or anyone wanted).
It’s also worth pointing out that there are now at least two, not one, fewer EA billionaires than last summer (SBF and Gary Wang, AFAIK). If that remains the case in 2027, my forecast will have been very bad; I did not account for the fact that all billionaires, and these two especially, were to some extent correlated. Of course there’s a chance there are new ones too in the next years to make up for the deficit; one can hope.