LessWrong 2.0 Reader

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.

I don't understand Model-Utility Learning [? · GW] (MUL) section, what pathological behavior does AI do?

Since humans (or something) must be labeling the original training examples, the hypothesis that building bridges means “what humans label as building bridges” will always be at least as accurate as the intended classifier. I don’t mean “whatever humans would label”. I mean they hypothesis that “build a bridge” means specifically the physical situations which were recorded as training examples for this system in particular, and labeled by humans as such.

So it's like overfitting? If I train MUL AI to play piano in a green room, MUL AI learns that "playing piano" means "playing piano in a green room" or "playing piano in a room which would be chosen for training me in the past"?

Now, we might reasonably expect that if the AI considers a novel way of “fooling itself” which hasn’t been given in a training example, it will reject such things for the right reasons: the plan does not involve physically building a bridge.

But "sensory data being a certain way" is a physical event which happens in reality, so MUL AI might still learn to be a solipsist? MUL doesn't guarantee to solve misgeneralization in any way?

If the answer to my questions is "yes", what did we even hope for with MUL?

Do you think this is typical of people you know?