Thanks for the comment, Stephen.
Vasco, how do your estimates account for model uncertainty?
I tried to account for model uncertainty assuming 10^-6 probability of human extinction given insufficient calorie production.
I donāt understand how you can put some probability on something being possible (i.e. p(extinction|nuclear war) > 0), but end up with a number like 5.93e-14 (i.e. 1 in ~16 trillion). That implies an extremely, extremely high level of confidence.
Note there are infinitely many orders of magnitude between 0 and any astronomically low number like 5.93*10^-14. At least in theory, I can be quite uncertain while having a low best guess. I understand greater uncertainty (e.g. higher ratio between the 95th and 5th percentile) holding the median constant tends to increase the mean of heavy-tailed distributions (like lognormals), but it is unclear to which extent this applies. I have also accounted for that by using heavy-tailed distributions whenever I thought appropriate (e.g. I modelled the soot injected into the stratosphere per equivalent yield as a lognormal).
As a side note, 10 of 161 (6.21 %) forecasters of the Existential Risk Persuasion Tournament (XPT), 4 experts and 6 superforecasters, predicted a nuclear extinction risk until 2100 of exactly 0. I guess these participants know the risk is higher than 0, but consider it astronomically low too.
Putting ~any weight on models that give higher probabilities would lead to much higher estimates.
I used to be persuaded by this type of argument, which is made in many contexts by the global catastrophic risk community. I think it often misses that the weight a model should receive is not independent of its predictions. I would say high extinction risk goes against the low prior established by historical conflicts.
I am also not aware of any detailed empirical quantitative models estimating the probability of extinction due to nuclear war.
Likewise! Thanks for the thoughtful comment.
It seems like a fair representation.
Agreed. However:
I think migration will tend to decrease deaths because people will only want to migrate if they think their lives will improve (relative to the counterfactual of not migrating).
The deaths from non-optimal temperature I mentioned are supposed to account for all causes of death, not just extreme heat and cold. According to GBD, in 2021, deaths from environmental heat and cold exposure were 36.0 k (I guess this is what you are referring to by heat stress), which was just 1.88 % (= 36.0*10^3/ā(1.91*10^6)) of the 1.91 M deaths from non-optimal temperature. My post is about how these 1.91 M deaths would change.
This makes sense. On the other hand, one could counter global warming will be good because:
There are more deaths from low temperature than from high temperature.
The disease burden per capita from non-optimal temperature has so far been decreasing (see 2nd to last graph).
Agreed. I would just note that i) can affect prioritisation across causes.