Isn’t the counterfactual trolley problem setup backwards? It should be counterfactual Omega giving you the better setup (not tying people to the tracks) if it predicts you’ll take the locally “worse” option in the actual case, not the other way around, right?
Because with the current setup you just don’t pull and Omega doesn’t tie people to tracks.
As an example of differentiating different kinds of footnotes, waitbutwhy.com uses different appearances for “interesting extra info” notes vs “citation” notes.
Both kinds also appear as popups when interacted with (certainly an advantage of the digital format).
Somehow I missed that bit.
That makes the situation better, but there’s still some issue. The refund is not earning interest, but you liabilities are.
Take the situation with owing $25 million. Say that there’s a one year time between the tax being assessed and your asset going to $0 (at which time you claim the refund). In this time the $25 million loan you took is accruing interest. Let’s say it does so at a 4% rate per year, when you get your $25 million refund you therefore have $26 million in loans.
So you still end up $1 million in debt due to “gains” that you were never able to realize.
Scenario: you have equity worth (say) $100 million in expectation, but of no realized value at the moment.
You are forced to pay unrealized gains tax on that amount, and so are now $25 million in the hole. Even if you avoid this crashing you immediately (such as by getting a loan), if your equity goes to $0 you’re still out for the $25 million you paid, with no assets to back it.
The fact that this could be counted as a prepayment for a hypothetical later unrealized gain doesn’t help you, you can’t actually get your money back.
But if UDT starts with a broad prior, it will probably not learn, because it will have some weird stuff in its prior which causes it to obey random imperatives from imaginary Lizards.
I don’t think this necessarily follows? For there to be a systematic impact on UDT’s behavior there would need to be more Lizard-Worlds that reward X than Anti-Lizard-Worlds that penalize X, so this is only a concern if there is reason to believe that there are “more” worlds (in an abstract logical-probability sense) that favor a specific direction.
Clearly this could still potentially cause problems, but (at least to me) it doesn’t seem like the problem is as ubiquitous as the essay makes it out to be.
My benchmark for thinking about the experience machine: imagine a universe where only one person and the stuff they interact with exist (with any other “people” they interact with being non-sapient simulations) and said person lives a fulfilling life. I maintain that such a universe has notable positive value, and that a person in an experience machine is in a similarly valuable situation to the above person (both being sole-moral-patients in a universe not causally impacting any other moral patients).
This does not preclude the possibility of improving on that life by e.g. interacting with actual sapient others. This view is fully compatible with non-experience-machine lives having much more value than experience-machine ones, but it’s a far cry from the experience-machine lives having zero value.
Also related: Yudkowsky on making Solvable Mysteries:
If you have not called upon your readers explicitly to halt and pay attention, they are already reading the next sentence. Even if you do explicitly ask them to pay attention, they are already reading the next sentence. If you have your character think, “Hm… there’s something funny about that story, I should stop and think about that?” guess what your reader does next? That’s right, your reader goes on to read the next sentence immediately, to see what the character thinks about it.
You can’t just trivially scale up the angular resolution by bolting more sensors together (or similar methods). It gets more difficult to engineer the lenses and sensors to meet super-high specs.
And aside from that, the problem behaves nonlinearly with the amount of atmosphere between you and the plane. Each bit of distortion in the air along the way will combine, potentially pretty harshly limiting how far away you can get any useful image. This may be able to be worked around with AI to reconstruct from highly distorted images, but it’s far from trivial on the face of it.
My guess is the largest contributor is the cultural shift to expecting much more involved parenting (example: the various areas where parents had CPS called on them for letting their kids do what the parents were allowed to do independently as kids)
Another big thing is that you can’t get tone-of-voice information via text. The way that someone says something may convey more to you than what they said, especially for some types of journalism.
I’d imagine that once we see the axis it will probably (~70%) have a reasonably clear meaning. Likely not as obvious as the left-right axis on Twitter but probably still interpretable.
I think a lot of the value that I’d get out of something like that being implemented would be getting an answer to “what is the biggest axis along which LW users vary” according to the algorithm. I am highly unsure about what the axis would even end up being.
To lay out some of the foundation of public choice theory:
We can model the members of an organization (such as the government) as being subject to the dynamics of natural selection. In particular, in a democracy elected officials are subject to selection whereby those who are better at getting votes can displace those who are worse at it, through elections.
This creates a selection dynamic where over time the elected officials will become better at vote-gathering, whether through conscious or unconscious adaptation by the officials to their circumstances, or simply through those who are naturally better at vote-gathering replacing those worse at it.
This is certainly not a bad thing per se. After all, coupling elected officials’ success to what the electorate wants is one of the major purposes of democracy, but “what gets votes” is not identical to “what’s good for the electorate”, and Goodhart’s law can bite us through that gap.
One of the classic examples of this is “doling out pork”, where concentrated benefits (such as construction contracts) can be distributed to a favored sub-group (thus ensuring their loyalty in upcoming elections) while the loss in efficiency from that favoritism is only indirectly and diffusely suffered by the rest of the electorate (making it much less likely that any of them get outraged about it enough to not vote for the pork-doler).
The application of this to market failures is that you can look at a market under government regulation as two systems (the market and the government), each with different incentives that imperfectly bind their constituent actors to the public good. The market generally encourages positive-sum trades to happen, but has various imperfections, especially regarding externalities and transaction costs, and the government generally encourages laws/regulations that benefit the public, but has its own imperfections, such as pork-doling and encouraging actions which look better to the public than their actual results would merit.
The result of this is that it is not necessarily clear whether whether changing how much influence market vs government dynamics have on a specific domain will improve it or not. Moving something to more government control may fix market failures, or it may just encourage good-looking-but-ineffective political posturing, and moving something to the market may cut down on corruption, or may just hit you with a bunch of not-properly-accounted-for externalities.
In the particular case of “government action to solve market failures”, the incentives may be against the government actors solving them, as in the case of the coal industry providing a loyal voting bloc, thereby encouraging coal subsidies that make the externality problem worse.
Therefore, my presentation of the market-failure-idea-skeptic’s position would be something like “we should be wary of moving the locus of control in such-and-such domains away from the market toward the government, because we expect that likely the situation will be made worse by doing so, whether due to government action exacerbating existing market failures more than it solves them, or due to other public-choice problems arising”.
Just because the US government contains agents that care about market failures, does not mean that it can be accurately modeled as itself being agentic and caring about market failures.
The more detailed argument would be public choice theory 101, about how the incentives that people in various parts of the government are faced with may or may not encourage market-failure-correcting behavior.
For chess in particular the piece-trading nature of the game also makes piece handicaps pretty huge in impact. Compare to shogi: in shogi having multiple non-pawn pieces handicapped can still be a moderate handicap, whereas multiple non-pawns in chess is basically a predestined loss unless there is a truly gargantuan skill difference.
I haven’t played many handicapped chess games, but my rough feel for it is that each successive “step” of handicap in chess is something like 3 times as impactful as the comparable shogi handicap. This makes chess handicaps harder to use as there’s much more risk of over- or under-shooting the appropriate handicap level and ending up with one side being highly likely to win.
Also note that socks with sandals being uncool is not a universal thing. For example, in Japan it is reasonably common to wear (often split-toed) socks with sandals, though it’s more associated with traditional garb than modern fashion.
A way of implementing the serving-vs-kitchen separation that avoids that problem (and actually the way of doing it I initially envisioned after reading the post) would be that within each workplace there is a separation, but different workplaces are split between the polarities of separation. That way any individual’s available options of workplace are, at worst, ~half of what they could be with mixed workplaces, regardless of their preference.
(Caveat that an individual’s options could end up being less than half the total if there is a workplace-gender correlation overall (creating an imbalance of how many workplaces of each polarity there are), and an individual has a workplace-gender matchup which is opposite to the trend, but in this case at least that individual’s lesser amount of choices is counterbalanced by the majority of people having more than 50% of the max choices of workplace fitting them.)
It kind of passed without much note in the post, but isn’t the passport non-renewal one of the biggest limiters here? $59,000 divided by 10 years is $5,900 per year, so unless you’re willing to forgo having a passport that’s the upper limit of how much you could benefit from non-payment (exclusive of the tax liability reduction strategies). That seems like a pretty low amount per year in exchange for having to research and plan this, then having your available income and saving methods limited (which could easily lower your income by more than $5,900 just by limiting the jobs available to you).
One other way of putting the reverse order, though it sounds a bit stilted in English: “beagles have Fido”. I don’t think it’s used commonly at all but it came to mind as a form in the reverse order without looping.
Thank you for making and publishing this! It’s interesting to see some more of the background and process that goes into works like HPMOR, both for the sake of appreciation of the work, and for reference in my own story-crafting.