by bertman on 11/14/24, 8:32 AM with 63 comments
by tacitusarc on 11/15/24, 5:11 PM
This is nonsensical.
When people talk about high risk situations, they tend to conflate two things: the magnitude of potential downsides and the likelihood of success. To make his model meaningful, Tao would need to incorporate that from the start.
I strongly recommend Fooled By Randomness as primer for thinking about this.
There exists some (often non-Gaussian) curve for describing risk vs reward. I don’t know how you simplify that… Maybe take for number, one for the average negative value outcome and one for the average positive value outcome, and their associated probabilities. And then just add those together? I’m not sure.
by mont_tag on 11/14/24, 6:04 PM
from statistics import NormalDist
def var(nd):
'Value at risk'
return nd.stdev - nd.mean
safe = NormalDist(5, 3)
bold = NormalDist(9, 10)
shock = NormalDist(-5, 10)
print('Safe: %s\tBold: %s\tBaseline' % (var(safe), var(bold)))
print('Safe: %s\tBold: %s\tConstant shock' % (var(safe - 5), var(bold - 5)))
print('Safe: %s\tBold: %s\tUncorrelated shock' % (var(safe + shock), var(bold + shock)))
print('Safe: %s\tBold: %s\tCorrelated shock' % (var(safe * 2), var(bold *2)))
by biomcgary on 11/14/24, 6:21 PM
I.e., if people think that they are in economic doldrums or decline, taking a risk seems better than the status quo?
Voting out the incumbent party seems popular during economic downturns (real or perceived) unless the party clearly inherited the economic crisis AND seems to be addressing the problem energetically (e.g., post-1929 FDR).
by RA_Fisher on 11/15/24, 12:22 PM
by darepublic on 11/15/24, 7:13 PM
by gmays on 11/14/24, 7:46 PM