I think this diagnosis is an important and true option. (And thanks for the write-up!) However, I find myself deeply wishing the treatment was focused more on something objective, rather than "convincing" yourself the pain isn't from real tissue damage—especially since the diagnosis is so probabilistic.

What happens if you are wrong about the tissue damage? My own experiences with chronic pain have in fact all come after what I am quite sure was real and significant tissue damage (one exception). And I have had numerous other experiences in sports of assuming some small-to-medium pain was not significant tissue damage, then playing through it and aggravating it in ways I believe were also real tissue damage. There are also borderline cases—like right now, I feel moderately sure my knee should have healed from its injury long ago and the existing pain is "learned" in some sense, but also it literally swells up when I use it. So I'm guessing it's more learned at the scale of the knee and not at the scale of the spinal cord/brain? Regardless, seems really important to be able to tell which is which, rather than just deciding to treat it one way and hoping you're right!

A second big reason I wish it was more objective is that the subjective nature of the intervention seems very tangled. In particular, if fear is a problem, it can then be very easy to have an anxiety/fear response to having the fear, which obviously gets tricky quickly. (As well as the difficulties of talking about the fact that I don't literally experience fear or anxiety at the pain, it's more like some tiny nearly-unconscious fragments of concern/monitoring/flinching/protecting that are similar scaled-down reactions as one would have with fear.) Or, background stress in life that would have felt fine suddenly becomes stressful. And the belief barrier framed this way makes it seem like the default affordance is a weird self-coercion into believing something you don't believe.

I think I'll be leaning as far as possible toward objective treatments, to provide myself some grounding and minimize the tangledness. Seems like these mostly track the "traditional medical treatments" you discuss above. So e.g. PT and building strength/mobility back up is a clear win in both tissue damage and no-damage cases (and you can be a little more aggressive if you expect there isn't damage). Capsaicin, NSAIDs, etc also seem pretty good in both cases. Some parts of the threat reprocessing, like actively engaging with the pain, seem robustly good if you're focusing more on curiosity and less on convincing yourself it's fine.

But also, maybe I'm too anti-coercion, that's why I still have a bit of chronic pain, and if I just believed hard enough then I'd get over it.

Kelly always applies

Re rockets, I might be misunderstanding, but I’m not sure why you’re imagining doubling the number of molecules. Isn’t the idea that you hold molecules constant and covalent energy constant, then reduce mass to increase velocity? Might be worth disambiguating your comparator here: I imagine we agree that light hydrogen would be better than heavy hydrogen, but perhaps you’re wondering about kerosene?

I'm aware this is kind of hard to justify, but I'm basically an apologist on this one. I think he was mostly right and just exaggerated the measurable magnitude. It's just so damn hard to come up with examples that are not only true and illustrative and compelling and not confusing, but also very measurably true. I do wish he had provided a more justifiable core example and overstated the result less, but I do basically the same when I'm trying to make a point. On my list of metrics I think he satisficed basically fine—I can't think of any better examples off the top of my head from pre-COVID. 

[ETA: someone asked why I thought the effect size was more than 0, which is a good question that I was trying to dodge... Here's my attempt at some justification.

First: the “reality drives straight lines on graphs” thing. The line of your economy growing stays straight because you keep doing things to make the economy keep growing. Every time someone does something to boost the economy, that line gets a little straighter. If they didn’t fix the money supply they probably would have started growing less, but they did fix it because it was the next bottleneck and that’s how lines stay straight. I’ve seen a lot of times where an intervention that has to have a clear effect by any model of the world just doesn’t show a clear effect on graphs. So at this point I’m not that convinced by being unable to pick signal out of the noise.

Second, as someone else pointed out, they once again didn’t print enough money. So while Eliezer did exaggerate to say that they had actually fixed the problem and seen his preferred result, I think he was still directionally right: they did a small intervention, it helped some, and doesn’t really show up because they didn’t do enough. It wasn’t a Volcker situation where he really drilled it into people.

Third, least convincingly, I’m just schizo enough to be able to eyeball those graphs and say sure, does look a bit like prime LFPR was up faster. And after a crunch you’re supposed to see catch-up growth, and in this case it does seem like the catch-up growth of Japan was slower than I’d expect and the post-catch-up was equally fast but relatively faster (compare Germany’s RGDP, the first non-US country I looked at). Also, I hear there was some sort of economic problem around 2014-2015 maybe? Anyways this is of course in the context of point 1, where normally it’s hard to see anything on a graph.

Fourth, again in the context of point 1, in cases like this I'd lean more on models and historical context for what we know must be going on, rather than actually expecting to see the results clearly in any given case. And I feel pretty confident that increasing the liquidity available in a faltering economy HAS to increase GDP—like, decreasing it surely decreases GDP, right? So by the reversal test…]

Clinton's campaign was against Bush, so they were throwing these words back at him.

A Few Lessons from Dominic Cummings on Politics

Barbell model of voters (or "delusion of the centre"), where many in the electorate are far to the left of politicians on white collar crime and higher taxes on the rich but far to the right of politicians on violent crime, anti-terrorism, and immigration. 

You want to be empirical in a way almost all in politics aren't: run tons of focus groups and really listen to how your voters think, not just what policies they want.

Use a best-in-class data model. Polls naturally swing all over, much polling is bad; if you use these, make them Bayesian and get great people who really know what they're doing to figure them out. Then use these models to focus relentlessly on whatever has the largest effect size, which is swing voters. [Some other tricks here that seem worth not being as explicit about.]

Don't be patronizing, do have integrity—very hard in politics. 

Stay on message. Bill Clinton's campaign had 3 talking points, each phrased to maximize punch. "It's the economy, stupid", "read my lips", and another that I forget. Carville was incredible at focusing relentlessly on turning every interview question into a response on one of these three. People won't care about most of the stuff you could talk about, and you can't optimize everything, so just choose the few best messages that are most powerful to people and drive everything back to them. Watch The War Room about the Clinton campaign if you haven't yet.

Note that
A) zooming in on most city hubs will find you monetary concentrations like this, e.g. Manhattan has a GDP pc of $370k
B) I have never actually heard anyone argue that making the city richer is the path to solving homelessness despite living there for a long time, so suspect this might be an error—are you conflating this with deregulating the housing market? Or do people actually argue somewhere that more money would solve homelessness?

~same. I use a Kinesis Freestyle with 20" cord, that finally ~fixed my wrists after 4 years, and I'm extremely excited for the Kinesis Advantage360 coming out some time this year.

I think my current expectation of risk reduction from antigen tests is more like 20-60% than <10%, but I'll also note that it matters a lot what your population is. In Elizabeth's social circle my guess is that most people aren't coming to parties if they've had any suspected positive contact, have any weak symptoms, etc, such that there's a strong selection effect screening out the clearly-positive people. (Or like, imagine everyone with these risk factors takes an antigen test anyways—then requiring tests doesn't add anything.)

I haven't read this whole thread but for the record, I often agree with Michael Mina and think he does great original thinking about these topics, yet think in this case he's just wrong with his extremely high estimates of antigen test sensitivity during contagion. I think his model on antigen tests specifically is theoretically great and a good extrapolation from a few decent assumptions, but just doesn't match what we see on the ground.

For example, I've written before about how even PCRs seem to have 5-10% FNR in the hospitalized, and how PCR tests look even worse from anecdata. Antigen tests get baselined against PCR so will be at least this bad.

We also see things like a clinical trial on QuickVue tests that shows only ~83% sensitivity. Admittedly other studies of antigen tests show ~98% sensitivity, but I think publication bias and results-desirability bias here means that if the clinical trial only shows 83%, then that's decent evidence that studies finding higher are a bit flawed. I would not have guess they could get to 98% though so there's something that doesn't make sense here.

I know the standard heuristic is to trust scientific findings over anecdata, but I think in this case that should be reversed if you're extremely scientifically literate and closely tracking things on the ground. Knowing all the things that can go wrong with even very careful scientific findings, I just don't trust these studies claiming very high sensitivity much—I think they also contradict FDA data on Cue tests, data/anecdata about nasal+saliva tests working better than just nasal, etc. 

(Maybe I'm preaching to the choir and you know most of this, given your range was 25-90%. But I guess I see pretty good evidence it can't possibly be at the high end of that range.)