3. Some of the key variables get to be negative, which worries me. You can use “truncateLeft()” to remove the negative values, or “SampleSet.map({|x| x < 0 ? 0 : x})” to set them to 0. (Which is likely more appropriate here).
The observant reader may have noticed that the model allows for a number of additional billionaires in 2027 in the negative. That makes sense in that we may lose some of the ones we have currently (they may no longer be billionaires or effective altruists), but I don’t know if Patel is predicting the number of new billionaires, or the difference between how many there are then and how many there are now. E.g. if we get 10 new billionaires but lose one old one, my model would say we have 9 additional ones, but I suspect Patel’s bet would resolve in the positive (because there are 10 new ones).
So congrats, you are officially an observant reader. ;) (Edit: Though I realise that I’m muddling things by using “new” when I actually mean the difference between then and now.)
4 -- Nice, looks like he’s modelling future capital (not merely # of billionaires) but seems similar enough. I’m not sure if it’s in a finished state, but I see Nuno’s getting a chanceOfNewBillionnairePerYearOptimistic of ~18% which seems significantly more pessimistic than me, which is interesting given that some other ppl here seem to be more optimistic than me.
A few very quick points:
1. Neat to see this being modeled! It’s an important see of variables, could use more interesting attempts.
2. Note that you can click “copy share link” in Squiggle to have a link that opens that direct model. https://www.squiggle-language.com/playground/#code=eNqtVE1vgkAQ%2FSsTTmArLkZrStImmpjGU5PaI6kZy6IbYdFlqTHW%2F96hWkMVEJty3Hlf%2B2bD1kjm8XqcRhGqjeEGGCb89vts6AsdK8PVKqUTIYUWGI5XqZjNQj7WSsiZ4RoyjaZcPQdDTNqs3YYHuLO7C9AxOJ2FJz3ZaoEWEQchYcNRJYCB5goysCd%2F2H2fzEQsMSSdvn4lgqktEtt6EuhDQmazJxWv9fwFNadZk9msmzkxu7OHnYZpgGkWUG%2FAseANtOXJXY0MFwDNU9vs1svJQIRhBhaKj4LRx2aACT8Ep9yMOb3eGWyIOVTXZtCCe2qzAEfzCgvqpFT5TzvJaeWr%2Bd16ST%2BN8yxZhsNmKQoeeTDN%2BZSLn6dx9%2BD6hNt66tRI72pts2sd5CPuC5T%2FLQ6fj7BKUWoRctM%2Bmi0nDivjjuSJmUOPq3md5bsfmA7L3Io2xzG5%2BBrcqmHFRvZNVb6zisprsQs6pV%2BDsfsCPtfA6w%3D%3D
3. Some of the key variables get to be negative, which worries me. You can use “truncateLeft()” to remove the negative values, or “SampleSet.map({|x| x < 0 ? 0 : x})” to set them to 0. (Which is likely more appropriate here).
4. I know Nuno did some very similar modeling here, but hasn’t written about it much yet.
https://github.com/quantified-uncertainty/squiggle-models/blob/master/bill-gates-wealth/gates.squiggle
5. Be sure to apply to the upcoming Squiggle competition!
https://forum.effectivealtruism.org/posts/ZrWuy2oAxa6Yh3eAw/usd1-000-squiggle-experimentation-challenge
Thanks!
3 -- I think I mention this in a footnote:
So congrats, you are officially an observant reader. ;) (Edit: Though I realise that I’m muddling things by using “new” when I actually mean the difference between then and now.)
4 -- Nice, looks like he’s modelling future capital (not merely # of billionaires) but seems similar enough. I’m not sure if it’s in a finished state, but I see Nuno’s getting a
chanceOfNewBillionnairePerYearOptimistic
of ~18% which seems significantly more pessimistic than me, which is interesting given that some other ppl here seem to be more optimistic than me.5 -- Oh, will do!