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Supply side: It approaches the minimum average total, not marginal, cost. Maybe if people accounted for it finer (e.g., charging self "wages" and "rent"), cooking at home would be in the ballpark (assuming equal quality of inputs and outputs across venues..), but that just illustrates how real costs can explain a lot of the differential without having to jump to regulation and barriers to entry (yes, those are nonzero too!).
Demand side: Complaints in the OP about the uninformativeness of ratings also highlight how far we are from perfect competition (also, e.g., heterogeneous products), so you can expect nonzero markups. We aren't in equilibrium and in the long run we're all dead, etc.
I'm a big proponent of starting with the textbook economic analysis, but I was surprised by the surprise. Let's even assume perfect accounting and competition:
Draw a restaurant supply curve in the middle of the graph. In the upper right corner, draw a restaurant demand curve (high demand given all the benefits I listed). Equilibrium price is P_r*. Now draw a home supply curve to the far left, indicating an inefficient supply relative to restaurants (for the same quantity, restaurants do it "cheaper"). In the bottom left corner, draw a home demand curve (again the point is I demand eating out more than eating at home). Equilibrium price for those is P_h*. It's very easy to draw where P_h* < P_r*.
Cooking at Home Being Cheaper is Weird
I like the argument that the scaling should make the average marginal cost per plate lower in restaurants than at home, but I find cooking at home being cheaper not weird at all. First, there are also real fixed costs to account for, not just regulatory costs.
More importantly, the average price per plate is not just a function of costs, it's a function of the value that people receive. Cooking at home does give some nice benefits, but eating out gives some huge ones: essentially leisure, time savings (a lot of things get prepped before service), no dishes, and possibly lower search costs ("what's for dinner tonight?").
1A classic that seemingly will have to be reargued til the end of time. Other allocation methods are not clearly more egalitarian and are less efficient (depends on the correlation matrix of WTP, need, time budget, etc., plus one's own judgment of fairness, but money prices come out looking great a lot of the time). In some cases, even prices don't perform great (addressed in some comments on this post), but they're better than the alternatives.
For more reading: https://www.lesswrong.com/posts/gNodQGNoPDjztasbh/lies-damn-lies-and-fabricated-options?commentId=nG2X7x3n55cb3p7yB
To get Robin worried about AI doom, I'd need to convince him that there's a different metric he needs to be tracking
That, or explain the factors/why the Robin should update his timeline for AI/computer automation taking "most" of the jobs.
AI Doom Scenario
Robin's take here strikes me both as an uncooperative thought-experiment participant and as a decently considered position. It's like he hasn't actually skimmed the top doom scenarios discussed in this space (and that's coming from me...someone who has probably thought less about this space than Robin) (also see his equating corporations with superintelligence - he's not keyed into the doomer use of the term and not paying attention to the range of values it could take).
On the other hand, I find there is some affinity with my skepticism of AI doom, with my vibe being it's in the notion that authorization lines will be important.
On the other other hand, once the authorization bailey is under siege by the superhuman intelligence aspect of the scenario, Robin retreats to the motte that there will be billions of AIs and (I guess unlike humans?) they can't coordinate. Sure, corporations haven't taken over the government and there isn't one world government, but in many cases, tens of millions of people coordinate to form a polity, so why would we assume all AI agents will counteract each other?
It was definitely a fun section and I appreciate Robin making these points, but I'm finding myself about as unassuaged by Robin's thoughts here as I am by my own.
Robin: We have this abstract conception of what it might eventually become, but we can't use that abstract conception to do very much now about the problems that might arise. We'll need to wait until they are realized more.
When talking about doom, I think a pretty natural comparison is nuclear weapon development. And I believe that analogy highlights how much more right Robin is here than doomers might give him credit for. Obviously a lot of abstract thinking and scenario consideration went into developing the atomic bomb, but also a lot of safeguards were developed as they built prototypes and encountered snags. If Robin is so correct that no prototype or abstraction will allow us address safety concerns, so we need to be dealing with the real thing to understand it, then I think a biosafety analogy still helps his point. If you're dealing with GPT-10 before public release, train it, give it no authorization lines, and train people (plural) studying it to not follow its directions. In line with Robin's competition views, use GPT-9 agents to help out on assessments if need be. But again, Robin's perspective here falls flat and is of little assurance if it just devolves into "let it into the wild, then deal with it."
A great debate and post, thanks!
Paper from the Federal Reserve Bank of Dallas estimates 150%-300% returns to government nondefense R&D over the postwar period on business sector productivity growth. They say this implies underfunding of nondefense R&D, but that is not right. One should assume decreasing marginal returns, so this is entirely compatible with the level of spending being too high. I also would not assume conditions are unchanged and spending remains similarly effective.
At low returns, you might question whether it's good enough to invest more compared to other options (e.g., at 5%, maybe simply not incurring the added deficit to be financed at 5% is arguably preferable; at 7%, maybe your value function is such that simply not incurring the added deficit to be financed at 5% is arguably preferable), but at such high returns, unless you think the private sector is achieving a ballpark level of marginal returns, invest, baby, invest! The marginal returns would have to be insanely diminishing for it not to make sense to invest more, which implies we're investing at just about the optimal level (if the marginal return of the next $1 were 0%, we shouldn't invest more, but we shouldn't invest less either because our current marginal return is 150%). Holding skepticism about the estimated return itself would be a different story.
That is an additional 15% of kids not sleeping seven hours
I was not aware of the concomitant huge drop in sleep (though it's obvious in retrospect). Maybe it's more important to limit screen time at night, when you're alone in your room not sleeping. Being constantly lethargic as a result may also contribute to (and be a) depressive symptoms. It will be very important to figure out the mechanism(s) by which smartphone use hurts kids.
I agree, I was thinking more generally this isn't a "poker" theory specifically, just one about rules and buy-in. But it's about poker night, so I'll let it slide. The main game rules, though, remain extraneous. Loved the post still!
Mira: You should be able to buy anything with a limit order.
“I don’t feel like paying $250 for an anime figurine, but I left an order up for $50”
If they saw 10,000 orders at a lower price rung ...
As usual the answer is transaction costs
Agree and also perceptions. The idea here is to facilitate price discovery and price discrimination. If only we knew people's WTP and could serve them lower prices acceptable to us when volume isn't moving at the current price! We can adjust prices ad hoc, but maybe a little upfront market research would be better and an exchange might be smoother (subject to TCs). The flipside of this has the problem that consumers hate it [Reuters]. Also, hedging (see: futures markets) does happen in B2B, but with more sophisticated owners and larger businesses. The supply chain is constantly to optimize inventory management (again, not mom-and-pops you see on save-my-business shows).
Why is turbulence worse on planes? The headlines blame it on ‘climate change.’ The actual answer is the FAA told airlines to prioritize saving fuel over passenger comfort, despite passengers having a strong revealed preference for spending the extra cost of fuel to have a more pleasant flight. This then became ‘because climate change.’ This kind of thing damages public trust in all such claims, making solving climate change (and everything else) that much harder.
There are benefits to optimized profile descents (fuel, time, reduced air traffic controller instructions, reduced noise over populated areas), which they did studies on to confirm since in high traffic airspace the stepwise approach can be easier for ATC. This change could conceivably increase turbulence on approach but would not explain the increase that "the narrative" is attributing to increased wind shear at higher altitudes.
I'm here to say, this is not some property specific to p-values, just about the credibility of the communicator.
If scientists make a bunch of errors all the time, especially those that change their conclusions, indeed you can't trust them. Turns out (BW11) that scientistspublishedinbetterjournals are more credible than scientistspublishedinworsejournals, the errors they make tend not to change the conclusions of the test (i.e., the chance of drawing a wrong conclusion from their data ("gross error" in BW11) was much lower than the headline rate), and (admittedly I'm going out on a limb here) it is very possible the errors that change the conclusion of a particular test do not change the overall conclusion about the general theory (e.g., if theory says X, Y, and Z should happen, and you find support for X and Y and marginal-support-now-not-significant-support-anymore for Z, the theory is still pretty intact unless you really care about using p-values in a binary fashion. If theory says X, Y, and Z should happen, and you find support for X and Y and now-not-significant-support-anymore for Z, that's more of an issue. But given how many tests are in a paper, it's also possible theory says X, Y, and Z should happen, and you find support for X and Y and Z, but turns out your conclusion about W reverses, which may or may not really have something to say about your theory).
I don't think it is wise to throw the baby out with the bathwater.