EA - How bad could a war get? by Stephen Clare

The Nonlinear Library: EA Forum - Podcast autorstwa The Nonlinear Fund

Podcast artwork

Kategorie:

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: How bad could a war get?, published by Stephen Clare on November 4, 2022 on The Effective Altruism Forum.Acknowledgements: Thanks to Joe Benton for research advice and Ben Harack and Max Daniel for feedback on earlier drafts.Author contributions: Stephen and Rani both did research for this post; Stephen wrote it and Rani gave comments and edits.Previously in this series: "Modelling great power conflict as an existential risk factor" and "How likely is World War III?"Introduction & ContextIn “How Likely is World War III?”, Stephen suggested the chance of an extinction-level war occurring sometime this century is just under 1%. This was a simple, rough estimate, made in the following steps:Assume that wars, i.e. conflicts that cause at least 1000 battle deaths, continue to break out at their historical average rate of one about every two years.Assume that the distribution of battle deaths in wars follows a power law.Use parameters for the power law distribution estimated by Bear Braumoeller in Only the Dead to calculate the chance that any given war escalates to 8 billion battle deathsWork out the likelihood of such a war given the expected number of wars between now and 2100.Not everybody was convinced. Arden Koehler of 80,000 Hours, for example, slammed it as “[overstating] the risk because it doesn’t consider that wars would be unlikely to continue once 90% or more of the population has been killed.” While our friendship may never recover, I (Stephen) have to admit that some skepticism is justified. An extinction-level war would be 30-to-100 times larger than World War II, the most severe war humanity has experienced so far. Is it reasonable to just assume number go up? Would the same escalatory dynamics that shape smaller wars apply at this scale?Forecasting the likelihood of enormous wars is difficult. Stephen’s extrapolatory approach creates estimates that are sensitive to the data included and the kind of distribution fit, particularly in the tails. But such efforts are important despite their defects. Estimates of the likelihood of major conflict are an important consideration for cause prioritization. And out-of-sample conflicts may account for most of the x-risk accounted for by global conflict. So in this post we interrogate two of the assumptions made in “How Likely is World War III?”:Does the distribution of battle deaths follow a power law?What do we know about the extreme tails of this distribution?Our findings are:That battle deaths per war are plausibly distributed according to a power law, but few analyses have compared the power law fit to the fit of other distributions. Plus, it’s hard to say what the tails of the distribution look like beyond the wars we’ve experienced so far.To become more confident in the power law fit, and learn more about the tails, we have to consider theory: what drives war, and how might these factors change as wars get bigger?Perhaps some factors limit the size of war, such as increasing logistical complexity. One candidate for such a factor is technology. But while it seems plausible that in the past, humanity’s war-making capacity was not sufficient to threaten extinction, this is no longer the case.This suggests that wars could get very, very bad: we shouldn’t rule out the possibility that war could cause human extinction.Battle deaths and power lawsFitting power lawsOne way to gauge the probability of out-of-sample events is to find a probability distribution, a mathematical function which gives estimates for how likely different events are, which describes the available data. If we can find a well-fitting distribution, then we can use it to predict the likelihood of events larger than anything we’ve observed, but within the range of the function describing the distribution.Several researchers have...

Visit the podcast's native language site