No. My model is the sum of a bunch of random variables for possible conflicts (these variables are not independent of each other), where there are a few potential global wars that would cause millions or billions of deaths, and lots and lots of tiny wars each of which would add a few thousand deaths.
This model predicts a background rate of the sum of the smaller ones, and large spikes to the rate whenever a larger conflict happens. Accordingly, over the last three decades (with the tragic exception of the Rwandan genocide) total war deaths per year (combatants + civilians) have been between 18k and 132k (wow, the Syrian Civil War has been way worse than the Iraq War, I didn’t realize that).
So my median is something like 1M people dying over the decade, because I view a major conflict as under 50% likely, and we could easily have a decade as peaceful (no, really) as the 2000s.
Yeah this seems pretty reasonable. It’s actually stark looking at the Our World in Data – that seems really high per year. Do you have your model somewhere? I’d be interested to see it.
It’s not explicit. Like I said, the terms are highly dependent in reality, but for intuition you can think of a series of variables Xk for k from 1 to N, where Xk equals 1/k with probability 1/√N. And think of N as pretty large.
So most of the time, the sum of these is dominated by a lot of terms with small contributions. But every now and then, a big one hits and there’s a huge spike.
(I haven’t thought very much about what functions of k and N I’d actually use if I were making a principled model; 1/k and 1/√N are just there for illustrative purposes, such that the sum is expected to have many small terms most of the time and some very large terms occasionally.)
No. My model is the sum of a bunch of random variables for possible conflicts (these variables are not independent of each other), where there are a few potential global wars that would cause millions or billions of deaths, and lots and lots of tiny wars each of which would add a few thousand deaths.
This model predicts a background rate of the sum of the smaller ones, and large spikes to the rate whenever a larger conflict happens. Accordingly, over the last three decades (with the tragic exception of the Rwandan genocide) total war deaths per year (combatants + civilians) have been between 18k and 132k (wow, the Syrian Civil War has been way worse than the Iraq War, I didn’t realize that).
So my median is something like 1M people dying over the decade, because I view a major conflict as under 50% likely, and we could easily have a decade as peaceful (no, really) as the 2000s.
Yeah this seems pretty reasonable. It’s actually stark looking at the Our World in Data – that seems really high per year. Do you have your model somewhere? I’d be interested to see it.
It’s not explicit. Like I said, the terms are highly dependent in reality, but for intuition you can think of a series of variables Xk for k from 1 to N, where Xk equals 1/k with probability 1/√N. And think of N as pretty large.
So most of the time, the sum of these is dominated by a lot of terms with small contributions. But every now and then, a big one hits and there’s a huge spike.
(I haven’t thought very much about what functions of k and N I’d actually use if I were making a principled model; 1/k and 1/√N are just there for illustrative purposes, such that the sum is expected to have many small terms most of the time and some very large terms occasionally.)