Why is Peace Better Than War? – A Conclusion

We set out to investigate what the probability distribution of outcomes looks like for a modern global war. Some IR research has shown that a power law distribution is a plausible fit given the battle death data we have. But few analyses have compared the fit to that of other distributions, and the few that have found that it’s not clearly better than a log-normal or cut-off power law distribution. Plus, even if fitting a power law is appropriate, the extent to which we can extrapolate this distribution to infer the likelihood of events outside of the available data is unclear. So we have to go beyond the data and ask: given what we know about how wars are fought, does a war so large it constitutes an existential catastrophe seem implausible?

There are many different factors one could consider, and unfortunately a dearth of literature to rely on. Focusing on the reach of modern weapons, we found no strong reasons to think that an extinction-level war is not technologically possible. A 21st century great power war could see weapons of mass destruction deployed on an unprecedented scale. Since we cannot rule out extinction-level scenarios following the use of bioweapons or advanced military AI systems, it’s plausible that the distribution of possible outcomes includes extinction.

Of course, there could be other limiting factors. Future research could examine other candidates. For example, how do the political costs of further escalation change? How does the ratio of civilian to military deaths change? Are there logistical, tactical, or economic factors that limit how large wars can get? And for any proposed “limiting factor”, how does it interact with the existential risk of a runaway bioweapon or military AI system?

Given the destructive potential of nukes, bioweapons, and AI systems, though, our guess is that it will be hard to rule out the possibility that a war could get very, very bad indeed. We don’t think there’s an upper bound: not at 5%, nor at 90%, nor at any point in between.

Figure 4. The Battle of Stalingrad
  1. ^

    The severity of a war refers to the number of battle deaths it causes. World War II killed about 65 million people, which was ~3% of the global population at the time. So a modern extinction-level war would be about 100 times more severe in absolute terms and 30 times more severe in proportional terms. In this post, we also sometimes refer to the intensity of war. This refers to the number of battle deaths divided by the pooled populations of the countries involved.

  2. ^

    Note that this uses Braumoeller’s estimates of the distribution of severity: the number of battle deaths. This may underestimate the chance of an extinction war for at least two reasons. First, the world population has been growing over time. If we instead considered the proportion of the global population killed per war instead, extreme outcomes may seem more likely. Second, he does not consider civilian deaths. Historically, the ratio of civilian-deaths-to-battle deaths in war has been about 1-to-1 (though there’s a lot of variation across wars). So fewer than 8 billion battle deaths would be required for an extinction war since many civilians would also be killed.

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    These are: lognormal, exponential, stretched exponential, and power law with cut-off.

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    “In particular, the distributions for birds, books, cities, religions, wars, citations, papers, proteins, and terrorism are plausible power laws, but they are also plausible log-normals and stretched exponentials” (Clauset, Shalizi, and Newman, 2009, p. 26).

    Note that stretched exponential is not found to be a good fit for the war data; see Table 6.2 on p. 28 of the paper for details.

    It should also be noted that, confusingly, a “cut-off” power law distribution doesn’t actually have a hard upper bound; instead, the distribution is multiplied by an exponential function. This “thins” the tail but doesn’t actually change the range.

  5. ^

    Thanks to Ben Garfinkel for bringing this point to our attention in this comment.

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    Stephen previously discussed this in “How likely is World War III?” and his report for Founders Pledge.

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    See pp. 113-4 of Braumoeller’s Only the Dead for more discussion.

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    “In cases such as these, it is important to look at physical motivating or theoretical factors to make a sensible judgment about the which [sic] distributional form is more reasonable—we must consider whether there is a mechanistic or other non-statistical argument favoring one distribution or another” (Clauset, Aaron, Cosma Rohilla Shalizi, and Mark EJ Newman. “Power-law distributions in empirical data.” SIAM review 51, no. 4 (2009): 26).

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    More specifically, they compare a distribution fitted to the entire Correlates of War dataset to a distribution fitted to the “top decile of international wars” (n=13). They find that the parameters are significantly different. This violates one of the properties of “true” power law distributions, which is that the same parameters describe the data everywhere in its range.

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    “there are just so many belligerents, so many possible war alliances, so much armament, so many combat fronts that can be managed simultaneously, and so forth. As a result, the theoretically possible largest magnitudes of warfare are never actually realized due to the underlying anite dynamics” Cioffi-Revilla, C., & Midlarsky, M. I. (2004). Power laws, scaling, and fractals in the most lethal international and civil wars. In The Scourge of War: New Extensions on an Old Problem (p. 23).

  11. ^

    This focus may seem too narrow. But if we can find just one limiting factor among the various inputs needed for war, then we can be confident in lowering our upper bound.

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    We’re using the war intensity dataset Braumoeller used for Only the Dead. You can see a copy of it here.

  13. ^

    What about before the 18th century? Luke Muehlhauser has estimated that 9.5% of the world’s population died in Ghengis Khan’s conquests. That’s still well short of an extinction-threatening catastrophe (Muehlhauser, Luke. “How big a deal was the Industrial Revolution?”, Luke Muehlhauser (blog), https://lukemuehlhauser.com/industrial-revolution).

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    On bioweapons, you might want to read 80,000 Hours’ article on biological risks and W. Seth Carus’ review of biological weapons programs since 1915. For military AI, we recommend Christian Ruhl’s report for Founders Pledge.

  15. ^

    MacAskill, William. What we owe the future. Basic books, 2022, p. 112

  16. ^

    Paul Scharre, “Debunking the AI arms race”, http://dx.doi.org/10.26153/tsw/13985

  17. ^

    In fact, Paul Scharre of the Center for a New American Security has previously speculated that AI could make escalation more likely (Scharre, “ AI arms race”).

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