WillNickols

• WillNickols 1 Sep 2023 15:37 UTC

  2 points

  0 ∶ 0

  in reply to: Ryan T’s comment on: Re­quest­ing syl­labus feed­back for our new Har­vard Col­lege biose­cu­rity group

  Thanks for the comments! I’ve updated the syllabus according to the ones you left this morning.

  Regarding policy obstacles for technical issues, I think this is a good thing to focus on. I’ve included some of that in my separate list of discussion points, but it would be good to look into that more.

• WillNickols 29 Aug 2021 4:12 UTC

  1 point

  0 ∶ 0

  in reply to: Stefan_Schubert’s comment on: Ex­trap­o­lated Age Distri­bu­tions af­ter We Solve Aging

  Thanks for the questions!

  Yes, “average” means “mean” throughout.

  I calculated those estimates with a LOTUS (population weighted average) approach: multiplying the population’s probability of dying at each age by that age itself and taking the sum of those products from ages 0 to infinity. In practice, I took the sum of the products from ages 0 to 1 million because the probability of living past 1 million was so low in these models that there was no contribution to the sum that was more than R’s round-to-zero threshold.

  And though none of these models account for it, it does seem like the risk of dying from homicide and suicide ought to decrease with age as society presumably finds life more valuable.

  For reference, the median age of death for these are as follows:

  • Males currently: 80

  • Females currently: 84

  • Males in the exponential model: 51

  • Females in the exponential model: 55

  • Males in the cure-aging-only model: 372

  • Females in the cure-aging-only model: 827

  • Males in the cure-aging-plus-disease model: 541

  • Females in the cure-aging-plus-disease model: 1688

  • Males in the cure-aging-plus-disease-and-accidents model: 1428

  • Females in the cure-aging-plus-disease-and-accidents model: 6144

  • Males in the last scenario: 1471

  • Females in the last scenario: 6256

  As expected, for all the right-skewed models (everything except reality), the median is less than the mean.

• WillNickols 20 May 2021 22:25 UTC

  3 points

  0 ∶ 0

  in reply to: Max_Daniel’s comment on: Char­i­ta­ble Giv­ing and the Ex­po­nen­tial Distribution

  Thanks Max, you make a good point about differentiating between exponentials, Paretos, and log-normals. It does seem like log-normals are the norm when it comes to these skewed distributions, especially with things like world income. Still, keeping an open mind as to which of the skewed distributions best fits the data can hopefully make these models more robust.

  You mentioned the challenges of distinguishing between these heavy-tailed distributions, and I would only add that this challenge increases when viewing these outcomes as intervals rather than points. For the sake of creating graphable data here, I only used the midpoint of the ranges listed on DCP3, but some of the intervals did span orders of magnitudes.

  Finally, I’m not sure exactly what you mean about the complications of interpreting exponential distributions beyond some cutoff, but if the question was about applying the memoryless property to exponentials (and the tail only depending on the rate parameter), there’s a short derivation on Wikipedia. Again, not sure if that’s what you were getting at, but maybe it’ll clear things up.

  Thanks for the comments!

• WillNickols 20 May 2021 13:42 UTC

  2 points

  0 ∶ 0

  in reply to: karthik-t’s comment on: Char­i­ta­ble Giv­ing and the Ex­po­nen­tial Distribution

  Really interesting, thanks for pointing this out!