rossry
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I like this framing, especially as it gracefully handles the way that communication isn’t like Guess Who—you have priors that don’t look like “uniform over the following N possibilities”, your payoffs for actually finding the answer might be nonconstant depend on what the answer is, some resource limitation might make the maxi-p(win) strategy different from the optional discriminator—but once you start thinking about how you’d win a game with those rules, strategies for smarter search suggest themselves.
If I’m not the first, was this posted before?
No, I’m referencing an in-person conversation. (Incidentally, the fact that ialdabaoth fielded that suggestion and still wrote this post with ‘dragon’ makes me worry that they’ve got at least an instinct that it’s the right word in some way I’m missing.)
And I think I see the worry that you’re pointing at here. I think it’s a valid one, though not one that I expect can be resolved entirely through theory; I’d like to see some people work with the ontology for a bit to see which words work in useful ways.
You’re not the first to suggest s/Dragon/Hydra/g here, and I’d be tempted to agree, if not for the fact that dragon-slaying is significantly more poetic than hydra-slaying. OTOH, “Hydra” serves as a mnemonic that attacking symptoms is a Known Bad Strategy.
(Do note that the existence of a dragon can cause a series of not-obviously-related symptoms—this stuff is on fire, and this stuff is smashed up, and these people got eaten...)
Right, so if we’re using a uniform distribution over 2^30000, there should be exactly zero ants sharing observer-moments, so in order to argue that ants’ overlap in observer-moments should discount their total weight, we’re going to need to squeeze that space a lot harder than that.
I’ve also spent some time recently staring at ~randomly generated grids of color for an unrelated project, and I think there’s basically no way that the human visual system is getting so much as 5000 bits of entropy (i.e., 50x50 grid of four-color choices) out of the observer-experience of the visual field. So I think using 2^#receptors is just the wrong starting point. Similarly, assuming that neurons operate independently is going to give you a number in entirely the wrong realm of numbers entirely. (Wikipedia says an ant has ~250,000 neurons.)
I think that if you want to get to the belief that two ants might ever actually share an experience, you’re going to need to work in a significantly smaller domain, like your suggestion of output actions, though applying the domain of “typical reactions of a human to new objects” is going to grossly undercount the number of human possible observer-experiences, so now I’m back to being stuck wondering how to do that at all.
How many distinct possible ant!observer-moments are there? What is the entropy of their distribution in the status quo?
How many distinct possible human!observer-moments are there? What is the entropy of their distribution in the status quo?
(Confidence intervals okay; I just have no intuition about these quantities, and you seem to have considered them, so I’m curious what estimates you’re working with.)
My experience has been that in practice it almost always suffices to express second-order knowledge qualitatively rather than quantitatively. Granted, it requires some common context and social trust to be adequately calibrated on “50%, to make up a number” < “50%, just to say a number” < “let’s say 50%” < “something in the ballpark of 50%” < “plausibly 50%” < “probably 50%” < “roughly 50%” < “actually just 50%” < “precisely 50%” (to pick syntax that I’m used to using with people I work with), but you probably don’t actually have good (third-order!) calibration of your second-order knowledge, so why bother with the extra precision?
The only other thing I’ve seen work when you absolutely need to pin down levels of second-order knowledge is just talking about where your uncertainty is coming from, what the gears of your epistemic model are, or sometimes how much time of concerted effort it might take you to resolve X percentage points of uncertainty in expectation.
I’m confused; why don’t you just pick black with probability 100%? (Assuming that your utility is 1 if you make the on-further-reflection-correct choice, 0 else.)
This isn’t the same thing as knowing what you’re going to think at the end of your fretting—you still don’t know—but the correct response to uncertainty is not half speed, and just picking the 40%-to-be-right option all of the time is the expectancy-maximizing response to uncertainty.
Pretty confident they meant it that way:
I am not thinking about physics-time, I am thinking about logical-time. If something is in your past, but has no effect on what algorithm you are running on what observations you get, then it might as well be considered as space-like separated from you. If you compute how everything in the universe evaluates, the space-like separated things are the things that can be evaluated either before or after you, since their output does not change yours or vice-versa. If you partially observe a fact, then I want to say you can decompose that fact into the part that you observed and the part that you didn’t, and say that the part you observed is in your past, while the part you didn’t observe is space-like separated from you.
It seems pretty easy for such mechanisms to be adapted for maximizing reproduction in some ancestral excitement but maladapted for maximizing your preferences in the modern environment.
I think I agree that your point is generally under-considered, especially by the sort of people who compulsively tear down Chesterton’s fences.
RCTs (and p-values) don’t seem to be popular in physics or geology.
What evidence makes you say p-values aren’t popular in physics? My passing (and mainly secondhand) understanding of cosmology is that it uses ~no RCTs but is very wedded to p-values (generally around the 5 sigma level).
A single house in a single market levered up 5x isn’t the good kind of diversification
Hm, I can see your point. Are you saying because you’ve worked through a back-of-the-envelope calculation, or are you just guessing? (Me, I was just guessing.)
and will make your expected portfolio performance worse.
Do you mean “will make the expected dollar-returns of your portfolio worse”, or are you making a claim about the expected utility-adjusted returns?
There is no compelling reason to think such outperformance will continue into the future.
The market disagrees with you.
What market instruments express opinions on the future returns of US equity investments? Everything I can think of serves to express an opinion on present prices of equity investments or on things like the future risk-free rate of return, not the future return on, e.g., the S&P500 index.
What text from the post suggests accounting for the emotional disutility of indebtedness?
Here’s a quote that to me seems to argue against it:
The question becomes: what risk-free rate of return is equivalent to your best available investment? If you pay a higher interest on your debt than that, you should pay it off. If the rate you’re paying is lower, you should invest.
Following this algorithm suggests investing in a risk-free 3.01% return before paying off debts costing a net 3% in interest.
There’s a bit of section 5 that accepts emotional disutility of volatility, but that’s not the same thing.
As for ChristianKl, they comment that to them it doesn’t make sense to value indebtedness at zero, agreeing with you that the disutility should be accounted for.
I think you are in agreement with ChristianKl (i.e., you both think the OP overlooks the emotional disutility of knowing that you have debt), but your tone seems to me to indicate disagreement.
Don’t hold any debt at above 4%.
It depends. If debt is tax deductible then it can make sense to hold it. Or based on the size of the opportunity.
Note that this is in the section of the OP’s advice to themself, and presumably accounts for them already checking for tax-deductible opportunities and opportunity costs.
An anecdote for an anecdote: I made up and plugged in some numbers that roughly corresponded to buying my last (rented) apartment, and the calculator said it’d be cheaper than my rent by ~10%.
Tl;dr if buying vs renting was an obvious win the market would arbitrage that.
As I understand it, lots of the win comes from limit-one-per-person tax advantages, so “the market” can’t straightforwardly arbitrage it away.
In any case, along the lines of the OP’s section 4 above, the typical person should probably, ceteris paribus, prefer some investment in some real estate over additional investment in whatever market portfolio they already have, because not-perfectly-correlated assets add expectations but less-than-add volatilities. I think the calculator you linked is getting this wrong.
Also not sure if it’s a standard concept in path search, but searching from both ends seems fruitful, even in its most naive implementations:
if the graph is sufficiently random, I think it should take the expected fraction of the maze you have to walk from N/2 to sqrt(N)
you can apply it to either BFS or DFS, though I suspect that in practice DFS works better (and even better still if you DFS by switching your first decision (rather than the OP’s algorithm of switching your last)
naturally, there’s plenty of room for layering heuristics on top, like seeking towards the opposing cursor, or even the nearest point of the opposing explored-so-far surface...
Answering my own musing: it’s implementable as a 2-state cellular automaton, rather than requiring the ~5 states that distributed DFS does. So there’s that.
(Side question: there’s at least one more-human-intuitive way to apply chunking to mazes. Can you figure it out?)
What comes to my mind is a kind of algorithm for auto-chunking: repeatedly take a dead end and fill it in until the point where it branches off from a hallway or intersection. Eventually you’re left with a single hallway from entrance to exit.
I’m not certain how it’s an improvement over DFS, though.
I’ve been enjoying this sequence on privacy, in no small part because I disagree with some of its fundamental premises so strongly. (Hopefully, someday soon I’ll make the time to pull together a sequence laying out the grounds of my disagreement.)
But without backing up that teaser (sorry), I’ll say that the brief mention of privacy as a guard against falling into inferential-distance chasms seems very straightforwardly true in hindsight (even to one skeptical of the guard-against-coercive-control angle), though I hadn’t been able to put it nearly so cleanly in my own thoughts. If you’ve got even a short post’s worth of content on related ideas in deploying privacy in deeply cooperative/aligned contexts, I’d be especially interested to hear more thoughts in that direction.