Start watching 4K videos on streaming services when possible, even if you don’t have a 4K screen. You won’t benefit from the increased resolution since your device will downscale it back to your screen’s resolution, but you will benefit from the increased bitrate that the 4K video probably secretly has.
I'm not sure if anyone still does this, but there was also a funny point early in the history of 4k streaming where people would encode 4k video at the same bitrate as 1080p, so they could technically advertise that their video was 4k, but it was completely pointless since it didn't actually have any more detail than the 1080p video.
The 1080p video at the same bitrate is also a lossy compression of the original 1080p, and the end result of decoding it will be an approximation of the original 1080p video that isn't quite correct, because the exact same amount of information was thrown out.
That said, an ideal video encoding algorithm would always do better with a 1080p video because it has more options[1], but it's not clear to me that actually-existing encoders meet this ideal.
- ^
If the optimal way to encode a video is to downscale it to 360p, an optimal 1080p encoder can downscale to 360p. If the optimal way to encode the video is to use information that's not visible in 360p, the 1080p encoder can use it, but a 360p encoder can't.
Yeah, doing an incremental rollout doesn't save you if you're not monitoring it.
Even though they'd take the same action, it still seems like Alice and Bob disagree more than Bob and Claire. I'd argue that Bob and Claire probably have more similar world models and are more likely to agree on other actions than Alice and Bob are.
I guess it depends on what you're trying to achieve with an argument. If Alice and Bob have to agree on a decision for a single hand, then it's convenient that they can agree on the action, but I suspect if they had to team up long-term, Bob and Claire would find it easier to work together than Alice and Bob would, and Alice and Bob are more likely to have major disagreements which would improve their play to resolve.
I agree about grandma getting scammed, but I think you're wrong about the banks. Credit card refunds are already trivial, and the customer almost always wins (even when their bank thinks they're committing refund fraud). The problem is that the banks know that these charges will have a high chance of fraud and they will charge high transaction fees to cover the expected losses.
Browser support doesn't seem necessary for this if it was a viable model. Websites could do something similar with minimal friction using Stripe (for example, instead of a subscription paywall, you could have one-click payment for the one article). There would be some setup, but it would mostly be "put your phone number in and then type the SMS code" once per site.
I wonder how much of this is the difficulty of deciding if a single article or video is worthwhile. If I'm a heavy NYT reader, I can predict that the whole subscription is worth it, and if an individual article turns out to be uninteresting, I can just read a different one. But if I spend money on a single article and then it's uninteresting, it feels like I wasted money. This would feel particularly bad if I get charged automatically as soon as I click a link.
It's sort of weird that it keeps increasing in difficulty even if you suck at the current difficulty. Or maybe 40% is considered good enough?
Isn't this how prediction markets are supposed to work? The LessWrong team is effectively paying you a small amount of (fake) money to improve the predictions. Seems win/win to me.
(I'm not saying there's anything wrong with the post though, your strategy seems good)
1You ask "Are FICO scores effective?" but to answer that you need to ask a further question, "Effective at what?".
2b seems to not be important enough to make up a significant portion of the score. The risk from credit not intended to be repaid is separate from risk accounted for via past loan delinquency base rates and future changes in financial situations, mostly as a separate, rare-but-consequential event. I don't think that adding the two tells you a lot about the person.
The purpose of a FICO score is not to tell you something about a person. The purpose of a FICO score is to not lose money.
If I'm considering lending you $100 for 1 year at 10% interest, the (simplified) outcomes are:
- You pay me back and I make $10.
- You don't pay me back and I lose $100.
An important consequence of this is that I care a lot about the case where you don't pay me back, even if it's rare. If you pay me back 85% of the time, I still lose money.
So, I might use credit scores for less important things like determining interest rates, but the most important decision to make is "Do I offer you a loan at all?".
With credit cards this is even harder since a typical customer doesn't use their full balance (and may not pay interest at all), while nearly bankrupt customers will use as much of their balance as they can. If your average customer pays you 2% in interchange fees and uses 10% of their balance and your worst customers cost you 100% of their balance, even 1/500 customers not paying you back is a problem.
So, keeping that in mind, we can look at the pieces again:
Payment history (35%)
Amount owed (30%)
Length of credit history (15%)
New credit (10%)
Credit mix (10%)
Payment history is obvious. If you don't pay other people, you probably won't pay me. Yes, this is "only" 35% of the score, but 850 x (1 - 35%) = 550. No one will give you a credit card with a FICO score of 550.
Amount owed and new credit is a signal that you're about to go bankrupt. Yes, this doesn't tell me much about the person and whether they're the kind of person who usually pays people back, but it does tell me that I shouldn't lend them money right now.
Length of credit history matters because a short credit history prevents lenders from using any other metric to determine risk. Losing money is the default, so you're guilty until proven innocent.
I'm not really sure on credit mix, but the fact that it's only 10% means it will basically never be the reason you do or don't get a loan (unless you're already borderline for some other reason) but it might effect rates. I assume part of this is reducing fraud risk: If you've successfully convinced someone to give you a mortgage, you're probably a real person).
One other part of this is that while the factors are weighted in the way you mention, the factors are not calculated in a straightforward way. For example, amount owed is 30% of your score, but that doesn't mean that reducing the amount you owe from 50% to 0% improves your credit score by 15%. Each factor is calculated as "Looking at your X, how risky does that make you?"
For length of history, that means a history of <1 year is insanely risky[1], while any history above 5 years is basically the same. Or for amounts owed, anything under 30% is low risk, 80% is getting up there, and 99% is insanely risky.
Even for something that sounds straightforward like credit mix, it's not necessarily the case that only having one credit account means you get a zero on that factor.
So all of that together:
- Credit scores measure something less intrinsic about a person. Lenders care if they're going to lose money, not if you're a good person.
- Losses per default are much higher than average profit per customer, so it's expected that the filtering process will look "too strict".
- Credit age matters because a short credit age prevents lenders from using any other signals, and the people are high risk until proven otherwise.
- You can try to game your credit score, but lenders don't care about high credit scores (it doesn't matter if your credit score is above 780), and gaming won't save you if any individual metric is bad enough (no one will lend to you at all below 580).
- A lot of the magic is how each category is calculated, not the high-level weights.
- ^
Source: I made it up.
1
It seems like it would be hard to detect if smart lawyers are using AI since (I think) lawyers' work is easier to verify than it is to generate. If a smart lawyer has an AI do research and come up with an argument, and then they verify that all of the citations make sense, the only way to know they're using AI is that they worked anomalously quickly.