The title suggests (weakly perhaps) that the estimates themselves peer-reviewed. Would be clearer to write “building on” peer reviewed argument, or similar.
In the early stages, I had in mind that the more info any individual anon-account revealed, the more easily one could infer what time they spent at Leverage, and therefore their identity. So while I don’t know for certain, I would guess that I created anonymoose to disperse this info across two accounts.
When I commented on the Basic Facts post as anonymoose, It was not my intent to contrive a fake conversation between two entities with separate voices. I think this is pretty clear from anonymoose’s comment, too—it’s in the same bulleted and dry format that throwaway uses, so it’s an immediate possibility that throwaway and anonymoose are one and the same. I don’t know why I used anonymoose there. Maybe due to carelessness, or maybe because I lost access to throwaway. (I know that at one time, an update to the forum login interface did rob me of access to my anon-account, but not sure if this was when that happened).
“A Russian nuclear strike would change the course of the conflict and almost certainly provoke a “physical response” from Ukraine’s allies and potentially from the North Atlantic Treaty Organization, a senior NATO official said on Wednesday.
Any use of nuclear weapons by Moscow would have “unprecedented consequences” for Russia, the official said on the eve of a closed-door meeting of NATO’s nuclear planning group on Thursday.
Speaking on condition of anonymity, he said a nuclear strike by Moscow would “almost certainly be drawing a physical response from many allies, and potentially from NATO itself”.
I have heard of talk that the US might instead arm Ukraine with tactical nukes of its own, although I think that would be at least comparably risky as military retaliation.
The reasoning is that retaliating is US doctrine—they generally respond to hostile actions in-kind, to deter them. If Ukraine got nuked, the level of outrage would place intense pressure on Biden to do something, and the hawks would become a lot louder than the doves, similar to after the 9/11 attacks. In the case of Russia, the US has exhausted most non-military avenues already. And US is a very militaristic country—they have many times bombed countries (Syria, Iraq, Afghanistan, Libya) for much less. So military action just seems very likely. (Involving all of NATO or not, as michel says.)
I think your middle number is clearly too low. The risk scenario does not require that NATO trigger article 5 necessarily, but just that they carry out a strategically significant military response, like eliminating Russia’s Black Sea Fleet, nuking, or creating a no-fly zone. And Max’s 80% makes more sense than your 50% for he union of these possibilities, because it is hard to imagine that the US would stand down without penalising the use of nukes.
I would be at maybe .2*.8*.15=.024 for this particular chain of events leading to major US-Russia nuclear war.
All of these seem to be good points, although I haven’t given up on liquidity subsidy schemes yet.
Some reports are not publicised in order not to speed up timelines. And ELK is a bit rambly—I wonder if it will get subsumed by much better content within 2yr. But I do largely agree.
It would be useful to have a more descriptive title, like “Chinchilla’s implications for data bottlenecks” or something.
It’s noteworthy that the safety guarantee relies on the “hidden cost” (:= proxy_utility—actual_utility) of each action being bounded above. If it’s unbounded, then the theoretical guarantee disappears.
For past work on causal conceptions of corrigibility, you should check out this by Jessica Taylor. Quite similar.
It seems like you’re saying that the practical weakness of forecasters vs experts is their inability to make numerous causal forecasts. Personally, I think the causal issue is the main issue, whereas you think it is that the predictions are so numerous. But they are not always numerous—sometimes you can affect big changes by intervening at a few pivot points, such as at elections. And the idea that you can avoid dealing with causal interventions by conditioning on every parent is usually not practical, because conditioning on every parent/confounder means that you have to make too many predictions, whereas you can just run one RCT.
You could test this to some extent by asking the forecasters to predict more complicated causal questions. If they lose most of their edge, then you may be right.
I don’t think the capital being locked up is such a big issue. You can just invest everyone’s money in bonds, and then pay the winner their normal return multiplied by the return of the bonds.
A bigger issue is that you seem to only be describing conditional prediction markets, rather than ones that truly estimate causal quantities, like P(outcome|do(event)). To see this, note that the economy will go down IF Biden is elected, whereas it is not decreased much by causing Biden to be elected. The issue is that economic performance causes Biden to be unpopular to a much greater extent than Biden shapes the economy. To eliminate confounders, you need to randomiser the action (the choice of president), or deploy careful causal identification startegies (such as careful regression discontinuity analysis, or controlling for certain variables, given knowledge of the causal structure of the data generating process). I discuss this a little more here.
I would do thumbs up/down for good/bad, and tick/cross for correct/incorrect.
What do you want to spend most of your time on? What do you think would be the most useful things to spend most of your time on (from a longtermist standpoint)?
You say two things that seem in conflict with one another.
[Excerpt 1] If a system is well-described by a causal diagram, then it satisfies a complex set of statistical relationships. For example … To an evidential decision theorist, these kinds of statistical relationships are the whole story about causality, or at least about its relevance to decisions. [Excerpt 2] [Suppose] that there is a complicated causal diagram containing X and Y, such that my beliefs satisfy all of the statistical relationships implied by that causal diagram. EDT recommends maximizing the conditional expectation of Y, conditioned on all the inputs to X. [emphasis added]
In , you say that the EDT agent only cares about the statistical relationships between variables, i.e. P(V) over the set of variables V in a Bayes net—a BN that apparently need not even be causal—nothing more.
In , you say that the EDT agent needs to know the parents of X. This indicates that the agent needs to know something that is not entailed by P(V), and something that is apparently causal.
Maybe you want the agent to know some causal relationships, i.e. the relationships with decision-parents, but not others?
Under these conditions, it’s easy to see that intervening on X is the same as conditioning on X.
This is true for decisions that are in the support, given the assignment to the parents, but not otherwise. CDT can form an opinion about actions that “never happen”, whereas EDT cannot.
Many people don’t realize how effective migraine treatments are. High-dose aspirin, tryptans, and preventers all work really well, and can often reduce migraine severity by 50-90%.
Also, most don’t yet realise how effective semaglutide is for weight loss, due to the fact that weight loss drugs have generally been much less effective, or had much worse side-effects previously.
Balding treatments (finasteride and topical minoxodil) are also pretty good for a lot of people.
Another possibility is that most people were reluctant to read, summarise, or internalise Putin’s writing on Ukraine due to finding it repugnant, because they aren’t decouplers.
Off the top of my head, maybe it’s because Metaculus’ presents medians, and the median user neither investigates the issue much, nor trusts those who do (Matt Y, Scott A) and just roughly follows base rates. I also feel there was some wishful thinking, and that to some extent, the fullness of the invasion was at least somewhat intrinsically surprising.