I have one material issue with the model structure, which I think may reverse your bottom line. The scenario full-scale countervalue attack against Russia has a median smoke estimate of 60Tg and a scenario probability of 0.27 x 0.36 = ~0.1. This means the probability of total smoke exceeding 60Tg has to be >5%, but Total smoke generated by a US-Russia nuclear exchange calculates a probability of only 0.35% for >60Tg smoke.
What seems to be going on is that the model incorporates estimated smoke from each countervalue targeting scenario as {scenario probability x scenario amount of smoke} in all Monte Carlo samples, when I figure you actually want it to count {scenario amount of smoke} in the appropriate proportion of samples. This would give a much more skewed distribution.
Sampling properly (as I see it) seems to be a bit fiddly in Guesstimate, but I put something together for Smoke that would be generated as a result of countervalue targeting against the US in an ‘Alternative 3.4.4’ section here. (I figured making a copy would be the easiest way to communicate the idea.)
I also redirected the top-level smoke calculation to point to the above alternate calculation to see what difference it makes. (Things I’ve added are marked with [KH] in the copy to make the differences easy to spot.) Basically every distribution now has two humps: either there is a countervalue strike and everything has a high chance of collapsing, or there isn’t and things are awful but probably recoverable. Some notable conclusions that change:
~15% chance of getting into the 50Tg+ scenarios that you flag as particularly concerning, up from ~1%.
~13% chance that corn cultivation becomes impossible in Iowa, and 6% chance that Ukraine cannot grow any of the crops you focus on, both from <1%. I don’t know whether still being able to grow some amount of barley helps much.
Your bottom-line ~5% chance of 96% population collapse jumps to ~16%, with most of that on >99% collapse. On the bright side, expected deaths drop by ~1bn.
Obviously, all these numbers are hugely unstable. I list them only to illustrate the difference made by sampling in this way, not to suggest that the actual numbers should be taken super seriously.
As above, these changes are just from adjusting the sampling for Smoke that would be generated as a result of countervalue targeting against the US. Doing the same adjustment for Smoke that would be generated as a result of countervalue targeting against Russia would add additional risk of extreme nuclear winter. For example, I think your model would imply a few % chance of all the crops you focus on becoming impossible to grow in both Iowa and Ukraine.
Before exploring your work, I hadn’t understood just how heavily extinction risk may be driven by the probability of a full-scale countervalue strike occurring. This certainly makes me wonder whether there’s anything one can do to specifically reduce the risk of such strikes without too significantly increasing the overall risk of an exchange. In general, working through your model and associated text and sources has been super useful to my understanding.
I have one material issue with the model structure, which I think may reverse your bottom line. The scenario full-scale countervalue attack against Russia has a median smoke estimate of 60Tg and a scenario probability of 0.27 x 0.36 = ~0.1. This means the probability of total smoke exceeding 60Tg has to be >5%, but Total smoke generated by a US-Russia nuclear exchange calculates a probability of only 0.35% for >60Tg smoke.
What seems to be going on is that the model incorporates estimated smoke from each countervalue targeting scenario as {scenario probability x scenario amount of smoke} in all Monte Carlo samples, when I figure you actually want it to count {scenario amount of smoke} in the appropriate proportion of samples. This would give a much more skewed distribution.
Sampling properly (as I see it) seems to be a bit fiddly in Guesstimate, but I put something together for Smoke that would be generated as a result of countervalue targeting against the US in an ‘Alternative 3.4.4’ section here. (I figured making a copy would be the easiest way to communicate the idea.)
I also redirected the top-level smoke calculation to point to the above alternate calculation to see what difference it makes. (Things I’ve added are marked with [KH] in the copy to make the differences easy to spot.) Basically every distribution now has two humps: either there is a countervalue strike and everything has a high chance of collapsing, or there isn’t and things are awful but probably recoverable. Some notable conclusions that change:
~15% chance of getting into the 50Tg+ scenarios that you flag as particularly concerning, up from ~1%.
~13% chance that corn cultivation becomes impossible in Iowa, and 6% chance that Ukraine cannot grow any of the crops you focus on, both from <1%. I don’t know whether still being able to grow some amount of barley helps much.
Your bottom-line ~5% chance of 96% population collapse jumps to ~16%, with most of that on >99% collapse. On the bright side, expected deaths drop by ~1bn.
Obviously, all these numbers are hugely unstable. I list them only to illustrate the difference made by sampling in this way, not to suggest that the actual numbers should be taken super seriously.
As above, these changes are just from adjusting the sampling for Smoke that would be generated as a result of countervalue targeting against the US. Doing the same adjustment for Smoke that would be generated as a result of countervalue targeting against Russia would add additional risk of extreme nuclear winter. For example, I think your model would imply a few % chance of all the crops you focus on becoming impossible to grow in both Iowa and Ukraine.
Before exploring your work, I hadn’t understood just how heavily extinction risk may be driven by the probability of a full-scale countervalue strike occurring. This certainly makes me wonder whether there’s anything one can do to specifically reduce the risk of such strikes without too significantly increasing the overall risk of an exchange. In general, working through your model and associated text and sources has been super useful to my understanding.