> The observation that the uncertainty is only present in the observer is certainly true in the card context, but really questionable in the world states one. F

> The observation that the uncertainty is only present in the observer is certainly true in the card context, but really questionable in the world states one. For one, don't we need to assume a really aggressive "deterministic evolution of future world states" to argue this uncertainty is only present in the observer? I, for one, am totally uncomfortable with this assumption... My objection is to Deutsch's special pleading with regards to games of chance, or whatever else he thinks is "based on a physical understanding of the situation where a randomising process had approximated probabilities". The difference in irreducible uncertainty between "shuffled cards" and "anything else" is a matter of degree, not kind. > Also, there's a clear computational difference between these two settings. You kind of point this out by acknowledging that explicit Bayesian calculation are unreasonable in many settings - but in practice, I'm on a rationalist email thread where folks are trying to calculate explicit probabilities about the increased likelihood of nuclear war over the past 6 months. It's totally silly. Is it? Rationalists did better than pretty much any other set of people that could usefully be regarded as a "community" when it came to COVID, in terms of seeing it coming and dealing with it successfully. Do you think they did this by gut feeling? (Sure, some of them did, but much of that gut feeling was informed by explicitly probabilistic reasoning performed by others.) I'd love to have a better toolset for deciding when the level of risk involved in staying in [random major city] crosses a threshold that justifies moving. Right now we have mechanistic reasoning (what effects do nuclear explosions have) and reference class forecasting (how likely is Putin to do [some unusual thing]), both of which spit out credences with respect to the questions we care about (how likely am I to die if I stay here). The point isn't to follow the numbers off a cliff, but if you're well-calibrated (as in, have a track record with a decent brier score, or something), then pretending those numbers are totally useless is a bit silly. The question is not "are these numbers wrong" - yes, of course they are! The question is what alternative you're proposing, and whether it's any better. If you aren't well-calibrated and don't have experience using explicit Bayesian reasoning to make major life decisions like "I should leave this city because I think the risk of it getting bombed in a nuclear war has crossed some threshold", then I don't suggest you start now. But you could do worse than looking at people who do have such track records. Well, you'd be able to do that in a world where we cared about keeping track of that sort of thing. Too bad people keep finding reasons not to do that, huh? > We need to look at the actual way that Bayesian tools are actually used (and usable in practice) by it's adherents. As far as I've observed, it's mostly just silly signaling games where people make up numbers to justify whatever story they want to tell (given that the rationalist communities spend so much time worrying about confirmation bias as a fundamental one, I'm not sure this is even surprising). Surely you aren't telling me that you've updated your priors on observed evidence, and as a result have a different expectation about future world states (that is, "how useful would explicit Bayesian reasoning be if I tried using it")? > Also, superforcasters are most certainly not "proof that explicit Bayesian calculations are still extremely useful." There are literally _millions_ of experts who make prediction - there's no version of history where this isn't a random subset who performs dramatically better than average just due to statistical chance! Misunderstanding this as a proof of the usefulness of the probabilistic reasoning tool is the classic example of being fooled by randomness. Superforecasters consistently perform much better than chance (about as well as domain experts who aren't superforecasters, for difficult questions, in fact). Their level of performance is not compatible with the "the small number of people out millions for whom the the coin flip came up heads 10x in a row" hypothesis - if you had conducted an experiment to distinguish between the hypotheses "Superforecasters will perform no better than chance" and "Superforecasters will perform [x] better than chance", the posterior likelihood ratio in favor of the second would be truly absurd, even if you were pretty far off on where exactly in the distribution they'd be.