LessWrong 2.0 Reader

In academic biomedicine, at least, which is where I work, it’s all about tech dev. Most of the development is based on obvious signals and conceptual clarity. Yes, we do study biological systems, but that comes after years, even decades, of building the right tools to get a crushingly obvious signal out of the system of interest. Until that point all the data is kind of a hint of what we will one day have clarity on rather than a truly useful stepping stone towards it. Have as much statistical rigor as you like, but if your methods aren’t good enough to deliver the data you need, it just doesn’t matter. Which is why people read titles, not figure footnotes: it’s the big ideas that really matter, and the labor going on in the labs themselves. Papers are in a way just evidence of work being done.

That’s why I sometimes worry about LessWrong. Participants who aren’t professionally doing research and spend a lot of time critiquing papers over niche methodological issues be misallocating their attention, or searching under the spotlight. The interesting thing is growth in our ability to measure and manipulate phenomena, not the exact analysis method in one paper or another. What’s true will eventually become crushingly obvious and you won’t need fancy statistics at that point, and before then the data will be crap so the fancy statistics won’t be much use. Obviously there’s a middle ground, but I think the vast majority of time is spent in the “too early to tell” or “everybody knows that” phase. If you can’t participate in that technology development in some way, I am not sure it’s right to say you are “outperforming” anything.

X and Y are cooperating to contain people who object-level care about A and B, and recruit them into the dialectic drama. X is getting A wrong on purpose, and Y is getting B wrong on purpose, as a loyalty test. Trying to join the big visible org doing something about A leads to accepting escalating conditioning to develop the blind spot around B, and vice versa.

X and Y use the conflict as a pretext to expropriate resources from the relatively uncommitted. For instance, one way to interpret political polarization in the US is as a scam for the benefit of people who profit from campaign spending. War can be an excuse to subsidize armies. Etc.

I wrote about this here: http://benjaminrosshoffman.com/discursive-warfare-and-faction-formation/

IDK, but I'll note that IME, calling for empathy for "the other side" (in either direction) is received with incuriosity / indifference at best, often hostility.

One thing that stuck with me is one of those true crime Youtube videos, where at some stage of the interrogation, the investigator stops being nice, and instead will immediately and harshly contradict anything that the suspect Bob is saying to paint a story where he's innocent. The commentator claimed that the reason the investigator does this is to avoid giving Bob confidence: if Bob's statements hung in the air unchallenged, Bob might think he's successfully creating a narrative and getting that narrative bought. Even if the investigator is not in danger of being fooled (e.g. because she already has video evidence contradicting some of Bob's statements), Bob might get more confident and spend more time lying instead of just confessing.

A conjecture is that for Susan, empathizing with Robert seems like giving room for him to gain more political steam; and the deeper the empathy, the more room you're giving Robert.

If we can get good enough models of however the scissors-statements actually work, we might be able to help more people be more in touch with the common humanity of both halves of the country, and more able to heal blind spots.

E.g., if the above model is right, maybe we could tell at least some people "try exploring the hypothesis that Y-voters are not so much in favor of Y, as against X -- and that you're right about the problems with Y, but they might be able to see something that you and almost everyone you talk to is systematically blinded to about X."

We can build a useful genre-savviness about common/destructive meme patterns and how to counter them, maybe.  LessWrong is sort of well-positioned to be a leader there: we have analytic strength, and aren't too politically mindkilled.

Do you have any tips on improving my writing, or my worldview? 

Maybe you could address these problems, but could you do so in a way that is "computationally cheap"? E.g., for forecasting on something like extinction, it is much easier to forecast on a vague outcome than to precisely define it.

frequentist correspondence is the only type that has any hope of being truly objective

I'd counter this.

If I have enough information about an event and enough computation power, I get only objectively true and false statements. There are limits to my knowledge of the laws of the universe, the event in question (e.g. due to measurement limits) and limits to my computational power. The situation is further complicated by being embedded in the universe and epistemic concerns (e.g. do I trust my eyes and cognition?).

The need for a concept "probability" comes from all these limits. There is nothing objective about it.

That's why I said: "In expectation", "win or lose"

That the coinflip came out one way rather than another doesnt prove the guy had actual inside knowledge. He bought a large part of the shares at crazy odds because his market impact moved the price so much.

But yes, he could be a sharp in sheeps clothings. I doubt it but who knows.

Point is that the winners contribute epistemics and the losers contribute money. The real winner is society [if the questions are about socially-relevant topics].

I have a writeup on solar storm risk here [LW · GW] that could be of interest

So a proportional vote interstate compact? :)

I like it - I think one could specify an automatic method for striking a fair bargain between states (and only include states that use that method in the bargain). Then you could have states join the compact asynchronously.

E.g. if the goal is to have the pre-campaign expected electors be the same, and Texas went 18/40 Biden in 2020 while California went 20/54 Trump in 2020, maybe in 2024 Texas assigns all its electors proportionally, while California assigns 49 electors proportionally and the remaining 5 by majority. That would cause the numbers to work out the same (plus or minus a rounding error).

Suppose Connecticut also wants to join the compact, but it's also a blue state. I think the obvious thing to do is to distribute the expected minority electors proportional to total elector count - if Connecticut has 7 electors, it's responsible for balancing 7/61 of the 18 minority electors that are being traded, or just about exactly 2 of them.

But the rounding is sometimes awkward - if we lived in a universe where Connecticut had 9 electors instead, it would be responsible for just about exactly 2.5 minority electors, which is super awkward especially if a lot of small states join and start accumulating rounding errors.

What you could do instead is specify a loss function: you take the variance of the proportion of electors assigned proportionally among the states that are on the 'majority' side of the deal, multiply that by a constant (probably something small like 0.05, but obviously you do some simulations and pick something more informed), add the squared rounding error of expected minority electors, and that's your measure for how imperfect the assignment of proportional electors to states is. Then you just pick the assignment that's least imperfect.

Add in some automated escape hatches in case of change of major parties, change of voting system, or being superseded by a more ambitious interstate compact, and bada bing.