Guy who launches the nukes: "YEET"

I just provided a list of possible deescalations. In particular, sabotaging nordstream 2, I can’t imagine was a good idea. We don’t know who did it, but I assume it wasn’t Russia, it’d make no sense for them to do it

If and when Russia makes a credible nuclear threat against the US conditional on it continuing to supply Ukraine then I think we should stop, but that doesn't mean Ukrainians have to stop fighting for their land themselves. I do agree that not giving in to threats of terrorism is generally right, as it takes away the incentive for (costly) threats against the same target in the future. But with nukes, there is no future for the target as a continuing entity if it gets nuked. When we repeatedly refuse to give into normal terrorists, they'll keep doing terrorism, eventually realize it's not worth it, and stop, but with nuclear threats, we'll just get nuked (again, assuming the threat is credible). However, I don't think we have a significant nuclear risk against the US yet; basically, I think Tegmark substantially overestimates Putin's willingness to nuke Ukraine if he's about to lose and NATO's willingness to respond to such an attack with a counterattack against Russia.

I don't think Putin will use nukes at all, basically for the reason you mentioned: it would turn even his own people against him. But I think that if he \*were\* considering using nukes, presumably because he felt that a loss would be politically even worse for him, he'd still want to put it off for as long as possible (i.e., until he's about to lose), hoping that maybe the situation would change and he'd be able to win without nukes. The difference from the raise example you gave is that asking for a raise isn't normally an extremely dangerous action, and it's more likely to succeed if one does it when one is in good standing rather than when one is about to be fired, but nuking Ukraine risks escalation and domestic unrest, and it pretty much guarantees a win against Ukraine itself (even if the West counterattacks) no matter when it's done. Nuking sooner would result in fewer Russian troops lost (at least in the short run), but it would totally make sense for Putin to see this as less of a benefit than potentially avoiding nukes and the concomitant risks.

I'm a rationalist who believes in the value of these kinds of numerical predictions (though I'm pretty bad at them myself!), and I'll try to explain what I see as a problem with your argument. You argue convincingly that we have tremendous uncertainty about the various factors that will affect Putin's options and decisions, and thus any model of the situation that we come up should be recognized as shaky and subject to change dramatically (note that Tegmark does say that he intends to update his probabilities regularly, and he welcomes feedback from readers on his numbers, so he is at least trying to avoid becoming wedded to his particular model!). But I don't think you really explain why this means we should use qualitative models instead of quantitative models in this kind of situation. The radical skeptic could say that we shouldn't use either kind of model because there's so much haziness, and we should just cross all our bridges when we come to them (unless of course they're blown up), always focusing on the present. But I think we'd both agree that that's silly: we have a choice to make every day (whether to continue supplying Ukraine) with significant future repercussions, and we have a better chance of things turning out well if we make our choice based on our best (flawed) model of the future than if we throw up our hands and refuse to think about the future at all. If you agree with that, then it seems like you don't really mean that "we can't model anything" in this case because there's "no point of comparison" and any model would be "garbage in, garbage out" or that the only useful model is going to have to account for "a million variables," just that all this is true for \*quantitative\* models. So, why are qualitative models different? You cite the anchoring effect, but this intuitively applies to both quantitative and qualitative models, and Wikipedia seems to confirm this. People are loath to give up their preconceived notions, whether quantitative or qualitative, and policy makers should vigorously guard against this, but I think it's possible to learn to largely avoid this bias, by constantly asking oneself questions that poke holes in one's own views and listening to others who disagree with you, which is largely what the rationalist community is about. If quantitative models really are especially risky for anchoring (but I don't see why they would be), then this justifies especial care to avoid anchoring when using them, but it's just fundamentally gross to me to use an inferior model just because we think we'll be bad at reasoning unbiasedly about one that would otherwise be better, rather than using the most plausible model and trying our darndest to reason correctly about it. Similarly, I don't think quantitative models are obviously more susceptible to bullshirting than qualitative models (in which one can often use various kinds of rhetorical tricks and emotional appeal to obfuscate one's core argument). And I certainly agree that Tegmark is no expert at predicting this kind of niche thing and thus shouldn't be given much weight by himself, but I don't think nonexperts shouldn't give thoughtful quantitative or qualitative arguments about big, complicated issues. Laymen largely have no choice but to defer to experts that they deem trustworthy (who may not be the official "experts," depending on the issue), but many supposed experts don't know as much as they think they do or have different values from certain laymen, so there has to be a process for experts to earn and maintain credibility and trust. I think one part of this needs to be laypeople regularly discussing important big issues, necessarily on a naive and shaky level, but with the aim not of totally understanding them but simply of determining which experts (or schools of thought) are able to provide prima facie plausible answers to natural FAQs and seem to be credible and working for their values. For example, if simple models seem to suggest a high probability of nuclear war, and certain politicians and experts agree with this while the others don't seem to have a reasonable answer (Incidentally, I don't think all this is actually the case!), then it would make sense for a layperson to trust the former more than he did before and consider whether the latter might be biased or blinded in some relevant way. In addition, there's just a lot of joy in thinking about interesting, important problems, and as long as one doesn't get too overconfident, I don't think there's anything wrong with regular people theorizing publicly about a complicated topic. You also argue that the American failure in Vietnam was due to McNamara et. al. being too quantitative, and I can totally buy that the particular models they were using were bad, but it's hard to see how this reflects on quantitative models in general. From your description, it seems like the issue was maybe more that their model was wrong about specific things: the Vietnamese didn't want a democracy if it was going to be under American influence, and they were willing to fight hard to prevent that, even at the cost of GDP. If they hadn't used numbers, why should we think they would have gotten those things right? Why is it easier to think about unknown unknowns with a qualitative model? Perhaps you favor qualitative models that don't incorporate uncertainty (quantitative/qualitative) at all, by coming up with a single best guess as to what Putin will do in response to each one of our present options (maybe based on a certain standard IR theory)? But I don't see why it would be better to have a model that considers only one possible action by Putin in each scenario over one that weighs the different actions he could take. And if you want a model that incorporates different possible responses by Putin, then I think you have to either (a) use quantitative probabilities to express how likely (i.e., unsurprising) these different responses are under this model, (b) use qualitative probabilities ("almost surely," "plausibly," "quite likely," "a small chance," etc.) instead, or (c) avoid modeling any response in your model as more likely than any other. To me it is clear that b and c are worse than a. Qualitative probabilities say the same thing as quantitative probabilities except less clearly. They're still worth using sometimes to avoid unnecessary detail, just like it's worth saying "I was a little late" rather than specifying the exact time, but when one is making a careful, important decision, it's crucial to communicate meaning clearly. When I say that an event has probability A/B to me (i.e., under my best model of the future, as I don't think there's a single objective probability), I mean that to me the event is about as plausible as one of A specific contestants winning a random lottery with B contestants in all, which is a scenario that is pretty clearly defined. A random person will quite likely know what I mean if I say that there's a 60% chance that such-and-such will happen, but it's less clear if I just say it's "quite likely" (see what I did there?). In both cases, I could be full of feces, but I'm making a clearer, more falsifiable claim in the former case. It really seems to me that in a context where one is modeling the future, the only reason to use at least the specific qualitative probability phrases I've been lampooning is when the precision of the model isn't really important (not true when the model is used to determine war strategy) or when one wants (or is part of a culture designed to allow one) to weasel away from having one's predictions checked. Now, you could use more precise phrases that aren't numerical, like "as likely as a getting struck by lightning," "as likely as three heads in a row," but I don't think either of us would prefer that: you'd presumably say that those are still bad because they're ultimately referring to numbers, and I'd say they're bad (in technical contexts) because many people don't really have intuition/knowledge about lightning or betting on coins, but people do talk about numerical probabilities all the time. And what about option (c), to consider different possible responses Putin could take but not assign them any kind of likelihood? I think this is bad because it seems basically equivalent to assigning all possible Putin-responses equal probability, which seems worse than actually thinking about how relatively plausible the various responses are. Tl;dr I think predictive models should generally involve quantitative probabilities: getting rid of them usually results in vaguer qualitative probabilities in their stead, which have all the same issues of relying on limited information, facilitating bullshirting, and leading to anchoring.

>But absolutely not for something like this. There is no sample size - this is the first time a nuclear power has declared an expansionist, imperialist war on its neighbor in Europe in this millennium. Until the very end most non-pro-Russian analysts strongly doubted this could even happen. We have no point of comparison to even attempt to accurately model an outcome. All numbers, regardless of if you agree or disagree with them, will be arbitrary because we are facing a very strong fog of war. The percentages are about making people put their money where their mouth is a little bit, and seeing how accurate people actually are at predicting the course of conflicts. Fox News, CNN, MSNBC, etc. get experts on all the time to talk about the likely outcomes of war, but they don't make firm predictions with percents, so you can't really know which experts are actually the best at predicting outcomes. If Max Tegmark gives percents, 5 years later, we can see if 1/10 of his 10% predictions came true, if 2/10 of his 20% predictions came true, etc. and have a very easy way to see if he's full of BS or good at predicting. Also you typed a lot of words about how everything is fundamentally unknowable, and while I agree we'll have trouble getting accurate numbers, I think we still have to try. I don't know what the alternative is, besides just implicitly using numbers instead of explicitly using them. We have to be aware of what could provoke Russia to use nukes. Say we keep up the strategy of providing Ukraine with weapons and supplies, and it keeps working, and Russia faces total defeat. What happens next? Does Ukraine only take back post-2014 borders? Pre-2014 borders? Grab any additional land as punishment for Russian invasion? Demand war reparations? Demand Russia reform its government and depose Putin? If there was a 0% chance of nukes, at the very least I think forcing the Russian government to reform would be good. But there might not be a 0% of nukes, that's what we have to think about and do our best to place a number, however inaccurate, on.