Increased Parallelization Penalty, All Else Equal, Adds 5 Years to Timeline
Going from 0.7 to 0.1 adds 2 years on the aggressive settings, and 3 years on my preferred settings. I think the spirit of your point still stands; this is just a quibble—I’d recommend saying “adds a few years to timelines” instead of “adds 5 years”
I agree that it would be better to say “adds five years under ‘Best Guess’ parameters,” or to just use “years” in the tagline. (Though I stand by the decision to compare worlds by using the best guess presets, if only to isolate the one variable under discussion.)
It makes sense that aggressive parameters reduce the difference, since they reach full automation in significantly less time. At parallelization penalty 0.7, Aggressive gets you there in 2027, while Best Guess takes until 2040! With Conservative parameters, the same .7 to .1 shift has even larger consequences.
BTW thank you for drawing my attention to the parallelization penalty crux; I hadn’t realized how sensitive the model was to that parameter before! Very important to think more about this. ETA: Curious if you have thoughts on my confused question here.
Going from 0.7 to 0.1 adds 2 years on the aggressive settings, and 3 years on my preferred settings. I think the spirit of your point still stands; this is just a quibble—I’d recommend saying “adds a few years to timelines” instead of “adds 5 years”
I agree that it would be better to say “adds five years under ‘Best Guess’ parameters,” or to just use “years” in the tagline. (Though I stand by the decision to compare worlds by using the best guess presets, if only to isolate the one variable under discussion.)
It makes sense that aggressive parameters reduce the difference, since they reach full automation in significantly less time. At parallelization penalty 0.7, Aggressive gets you there in 2027, while Best Guess takes until 2040! With Conservative parameters, the same .7 to .1 shift has even larger consequences.
OK, seems we mostly agree then.
BTW thank you for drawing my attention to the parallelization penalty crux; I hadn’t realized how sensitive the model was to that parameter before! Very important to think more about this. ETA: Curious if you have thoughts on my confused question here.