Thanks!
Vasco Grilo
Karma: 5
Nice post, Luke!
with this handy reference table:
There is no table after this.
He also offers a chart showing how a pure Bayesian estimator compares to other estimators:
There is no chart after this.
Thanks for this clarifying comment, Daniel!
Great post!
The R-square measure of correlation between two sets of data is the same as the cosine of the angle between them when presented as vectors in N-dimensional space
Not R-square, just R:
Nice post! I would be curious to know whether significant thinking has been done on this topic since your post.
Great!
Thanks for writing this!
Have you considered crossposting to the EA Forum (although the post was mentioned here)?
With a loguniform distribution, the mean moral weight is stable and roughly equal to 2.
Thanks for the post!
I was trying to use the lower and upper estimates of 5*10^-5 and 10, guessed for the moral weight of chickens relative to humans, as the 10th and 90th percentiles of a lognormal distribution. This resulted in a mean moral weight of 1000 to 2000 (the result is not stable), which seems too high, and a median of 0.02.
1- Do you have any suggestions for a more reasonable distribution?
2- Do you have any tips for stabilising the results for the mean?
I think I understand the problems of taking expectations over moral weights (E(X) is not equal to 1/E(1/X)), but believe that it might still be possible to determine a reasonable distribution for the moral weight.
“These two equations are algebraically inconsistent”. Yes, combining them results into “0 < 0″, which is false.
Hi there,
Assuming 10^6 bit erasures per FLOP (as you did; which source are you using?), one only needs 8.06*10^13 kWh (= 2.9*10^(-21)*10^(35+6)/(3.6*10^6)), i.e. 2.83 (= 8.06*10^13/(2.85*10^13)) times global electricity generation in 2022, or 18.7 (= 8.06*10^13/(4.30*10^12)) times the one generated in the United States.