Iâm not sure what you mean by fair exactly, but youâre right that I donât distinguish the billionaire â EA pipeline from the EA â billionaire pipeline in the model (only mentioning it in text). It seems possible that your proposal of splitting these is good, though that may make it harder to calculate reasonable base rates (are there even any examples of EA â billionaire people left post-FTX?). Numbers on earning-to-give EAs could definitely be useful here if you have them.
Well, youâre probably right that there are reasons why we might expect the Ivy League base rate to be skewed high when using it for EA (the average age of Ivy League alumni being much higher than that of EAs is the most obvious one IMO), but also, the Ivy League base rate is actually substantially lower than the EA base rate (at least it was pre-FTX), and so including it has the effect of reducing the overall estimate, and if you didnât include it youâd increase the overall estimate, which seems like the opposite of what youâre arguing for? Itâs definitely possible that EAs are less likely to become and/âor be billionaires even than the average Ivy League alumnus/âa, but in that case itâs pretty suspicious that there have been several EA billionaires already.
Yeah this is trueâthereâs definitely some sort of diffusion/âmomentum thing going on that my model isnât fully accounting for. (Then again, I think thereâs something to be said for âsimple model + qualitative reasoning around itâ over âcomplicated model that accounts for everythingâ!) I guess when I calculate base rates I do only include actual billionaires, not about-to-become billionaires, so it shouldnât be that biased.
Thanks for responding. Take only what you think is useful from my commentsâyou have thought much more deeply about this than I have and seem on top of the issues I have raised. Just a couple of responses in case it might be helpful (otherwise please disregard them):
Sorry, I have not seen such numbers. Just thought perhaps there might be some numbers lying around somewhere, e.g. results from surveys. I actually think perhaps the best number would be E2G EAs that pursue for-profit entrepreneurshipânot sure if even the quant traders have a high probability of becoming billionaire donors. But this number might be even harder to come by.
I think I would not exclude the Ivy League base rate. Instead some possibilities could be (and please disregard this if it does not seem promisingâI have not thought deeply about it!):
Perhaps one path could be to actually discard the EA base rate. My intuition here is that the number of EAs who later become billionaires is so low that the base rate calculated from it does not carry much weight (not sure if statistical significance is the right term here, and if not something close to it). Instead one could use an adjusted Ivy League base rate. And adjusting it based on some assumptions about âstrength of talentâ, fraction of population that pursues becoming rich and maybe some other adjustments, which would lower the final estimate.
Alternatively keep both base rates but still adjust the Ivy League base rate downwards due to the observations I made. That should also lower the final estimate.
Your point of having a simple model is a good oneâI am not sure how much more accurate the forecast would be by making a more complex model. And I think you point out well in the post that one should not lean too heavily on the model but take into consideration other sources of evidence.
Thanks, Iâm glad you found it useful!
Iâm not sure what you mean by fair exactly, but youâre right that I donât distinguish the billionaire â EA pipeline from the EA â billionaire pipeline in the model (only mentioning it in text). It seems possible that your proposal of splitting these is good, though that may make it harder to calculate reasonable base rates (are there even any examples of EA â billionaire people left post-FTX?). Numbers on earning-to-give EAs could definitely be useful here if you have them.
Well, youâre probably right that there are reasons why we might expect the Ivy League base rate to be skewed high when using it for EA (the average age of Ivy League alumni being much higher than that of EAs is the most obvious one IMO), but also, the Ivy League base rate is actually substantially lower than the EA base rate (at least it was pre-FTX), and so including it has the effect of reducing the overall estimate, and if you didnât include it youâd increase the overall estimate, which seems like the opposite of what youâre arguing for? Itâs definitely possible that EAs are less likely to become and/âor be billionaires even than the average Ivy League alumnus/âa, but in that case itâs pretty suspicious that there have been several EA billionaires already.
Yeah this is trueâthereâs definitely some sort of diffusion/âmomentum thing going on that my model isnât fully accounting for. (Then again, I think thereâs something to be said for âsimple model + qualitative reasoning around itâ over âcomplicated model that accounts for everythingâ!) I guess when I calculate base rates I do only include actual billionaires, not about-to-become billionaires, so it shouldnât be that biased.
Thanks for responding. Take only what you think is useful from my commentsâyou have thought much more deeply about this than I have and seem on top of the issues I have raised. Just a couple of responses in case it might be helpful (otherwise please disregard them):
Sorry, I have not seen such numbers. Just thought perhaps there might be some numbers lying around somewhere, e.g. results from surveys. I actually think perhaps the best number would be E2G EAs that pursue for-profit entrepreneurshipânot sure if even the quant traders have a high probability of becoming billionaire donors. But this number might be even harder to come by.
I think I would not exclude the Ivy League base rate. Instead some possibilities could be (and please disregard this if it does not seem promisingâI have not thought deeply about it!):
Perhaps one path could be to actually discard the EA base rate. My intuition here is that the number of EAs who later become billionaires is so low that the base rate calculated from it does not carry much weight (not sure if statistical significance is the right term here, and if not something close to it). Instead one could use an adjusted Ivy League base rate. And adjusting it based on some assumptions about âstrength of talentâ, fraction of population that pursues becoming rich and maybe some other adjustments, which would lower the final estimate.
Alternatively keep both base rates but still adjust the Ivy League base rate downwards due to the observations I made. That should also lower the final estimate.
Your point of having a simple model is a good oneâI am not sure how much more accurate the forecast would be by making a more complex model. And I think you point out well in the post that one should not lean too heavily on the model but take into consideration other sources of evidence.