Indeed, the more efficient you become the more brittle you will be. You must depend upon the present being static and the future being perfectly predictable based on the events of the past. The present and the future don't merely need to be dependable within your own domain but also in the entire world.
The flexibility necessary to succeed in a real world requires a certain level of inefficiency.
Yes, just-in-time supply chain systems often become over-efficient and brittle... usually because each link in the chain assumes that someone else is taking on the burden of inefficiency by having excess inventory in order to absorb shocks to the system.
Interestingly, the same effect shows up in communications systems. The more efficient an error correction code (ie. the closer it approaches the Shannon Bound), the more catastrophically it fails when the channel capacity is reached. The "perfect" code delivers no errors up until the Shannon bound then meaningless garble (50% error rate) beyond the Shannon Bound.
My point is that error correction codes have a precise mathematical definition and have been deeply studied. Maybe there is a general principle at work in the wider world, and it is amenable to a precise proof and analysis? (My guess is that mileage may be made by applying Information Theory, as used to analyse error correcting codes.)
An interesting idea but I'd imagine you would have to operate within something like the "100 year flood" boundaries that insurance companies do in order to define a constrained domain such as the Shannon Bound. I suspect you would also have to define the scope of this principle within the company and/or deal with the compounding effects of the multiple layers of the system and its "effective inefficiency."
> [..] it signifies the level of performance that uses the least amount of inputs to achieve the highest amount of output. It often specifically comprises the capability of a specific application of effort to produce a specific outcome with a minimum amount or quantity of waste, expense, or unnecessary effort.
to quote wikipedia quoting Sickles, R., and Zelenyuk, V. (2019). "Measurement of Productivity and Efficiency: Theory and Practice". Cambridge: Cambridge University Press.
Offering that criticism without clarifying what efficiency measures in your opinion doesn't allow us to follow your viewpoint without us just taking your word for it. Needless to say this isn't considered good style in a discourse.
A 100 percent "efficient" system can be one that is overfitted to certain metrics and it is the typical death sin of management to confuse metrics with reality and miss that their great numbers hollow out anything that makes a system work well and reliable, because guess what: having 1 critical employee and working them like a mule is good when things work, but bad when they suddenly don't, because that second employee you thought was fat that could be cut, was your fallback. In that case your metric of efficiency was slightly increased while another, less easy to quantify (and therefore often non-existent) metric of resilience went down significantly. This means if your goal was having an efficient and resilient company, but your metric only measured the former, guess what.
Same is true in engineering, where you can optimize your system so much to fit your expected problem, one slight deviation within the problem now stops the whole thing from working alltogether (F1 racing car when part of the track turns out to be a sucky dirtroad). Highly optimized systems are highly optimized towards one particular situation and thus less flexible.
Or in biology, where everybody ought to know that mixed woods are more resilient to storms and other pests, while having great side effects for the health of the ecosystem, yet in pure economic terms it is easy to convince yourself the added effíciency of a monoculture is worth it economically, because all you look at is revenue, while ignoring multiple other metrics that impact reality.
The argument is that regardless of what metic is chosen, it'll create deminishing returns followed by negative returns.
What it means is the objective can't be static - for example once satiated, you need to pick different one to keep improving globally. Or do something else that moves the goalpost.
Yeah, every single example listed looks like gaming of bad metrics. Framing it as overfitting is unproductive, IMHO, and discounts the essentially adversarial context. I also discounts the stupidity of equating "efficiency" with a high score on a simple metric. Reality has a Surprising Amount of Detail, and all that.
The author is a very sharp individual but is there a reason he insists on labelling overfitting as a phenomenon from machine learning instead of from classical statistics?
It might simply be that he didn't trace the etymology back that far.
If it turned out that the term actually started in tailoring before statistics really got it's feet under it (which I absolutely cannot say that it did, just that trying to extrapolate backwards that sounds like a reasonable guess) then it wouldn't speak poorly of you if you hadn't also known that.
The author is an academic, it is important to give proper credit for ideas within reason. Same reason I call F = ma the law of Newton and now the law of my high school physics teacher, even though I learned it first from him.
The reason I have this quibble is because the author says things like
>you should consider building formal (mathematical) bridges between results on overfitting in machine learning, and problems in economics, political science, management science, operations research, and elsewhere
If we are appropriately modest and acknowledge the fact that overfitting is well-studied by statisticians (although, obviously not in the context of deep neural networks), it seems kind of ridiculous to make statements like, economists and political scientists should consider using statistics?
They don't say "classical statistics," but I don't see any implication that the phenomenon was born from machine learning, even if they say it's a common problem within machine learning. Maybe I missed it? They do mention modelling their conception of overfitting around Goodhart's Law, noting its origin in economics.
Strictly speaking this is not the contrapositive and therefore the proof is yet to be seen. A sound corollary: "If I do not behave, it is because you did not measure me."
Yes, a corollary can be just the contrapositive of something you just proved. Sometimes it's even more trivial, like a special case of a general theorem you proved.
A very common use is to re-state something so it's in the exact form of something you said you'd prove. Another common case is to highlight a nice incidental result that's a bit outside the path towards the main result -- for example, it immediately follows (perhaps logically equivalent to) something that's been proven, but it's dressed in a way that catches the attention of someone who's just skimming.
Parallel to Munger’s “Show me the incentives and I will show you the outcome” which I think all of us have or will realize for ourselves at some point in life.
Overfitting may be a special case of Goodhart's Law, but I don't think Goodhart's Law in general is the same as overfitting, so I don't think the conclusion is well-supported supported in general; there may be plenty of instances of proxy measures that do not have issues.
I'll also quibble with the example
of obesity: the proxy isn't nutrient-rich good, but rather the evaluation function of human taste buds (e.g. sugar detection). The problem is the abundance of food that is very nutrient-poor but stimulating to taste buds. If the food that's widely available were nutrient-rich, it's questionable whether we would have an obesity epidemic.
We realize now or at least in recent past, the value of true nutrient-rich food or a balanced diet.
Carbohydrate abundance was likely important in moving people out of hunger and poverty but excesses of the same kind of diet are a reflection on obesity.
My guess is that calorie-per-gram-per-dollar of carbohydrates is still lower than fat and protein.
IMO the theory at the start of the post is well written and almost there, but it needs to more substantively engage with the relevant philosophical concepts. As a result, the title "efficiency is bad!" is incorrect in my opinion.
That said, the post is still valuable and would work much better with a framing closer to "some analogies between statistical analysis and public policy" -- the rest of the post (all the political recommendations) is honestly really solid, even if I don't see a lot of the particular examples' connections to their analogous ML approaches. The creativity is impressive, and overall I think it's a productive, thought-provoking exercise. Thanks for posting OP!
Now, for any fellow pendants, the philosophical critique:
more efficient centralized tracking of student progress by standardized testing
The bad part of standardized testing isn't at all that it's "too efficient", it's that it doesn't measure all the educational outcomes we desire. That's just regular ol' flawed metrics.
This same counterintuitive relationship between efficiency and outcome occurs in machine learning, where it is called overfitting.
Again, overfitting isn't an example of a model being too efficacious, much less too efficient (which IMO is, in technical contexts, a measure of speed/resource consumption and not related to accuracy in the first place).
Overfitting on your dataset just means that you built a (virtual/non-actual) model that doesn't express the underlying (virtual) pattern you're concerned with, but rather a subset of that pattern. That's not even a problem necessarily, if you know what subset you've expressed -- words like "under"/"too close" come into play when it's a random or otherwise meaningless subset.
I'm not allowed to train my model on the test dataset though (that would be cheating), so I instead train the model on a proxy dataset, called the training dataset.
I'd say that both the training and test sets are actualized expressions of your targeted virtual pattern. 100% training accuracy means little if it breaks in online, real-world use.
When a measure becomes a target, if it is effectively optimized, then the thing it is designed to measure will grow worse.
I'd take this as proof that what we're really talking about here is efficacy, not efficiency. This is cute and much better than the opening/title, but my critique above tells me that this is just a wordy rephrasing of "different things have differences". That certainly backs up their claim that the proposed law is universal, at least!
Important subject, so-so blog post. This idea deserves further development.
The author seems to be discussing optimizing for the wrong metric. That's not a problem of too much efficiency.
Excessive efficiency problems are different. They come from optimizing real output at the expense of robustness. Just-in-time systems have that flaw. Price/performance is great until there's some disruption, then it's terrible for a while.
Overfitting is another real problem, but again, a different one. Overfitting is when you try to model something with too complex a model and and up just encoding the original data in the model, which then has no predictive power.
Optimizing for the wrong metric, and what do about it, is an important issue. This note calls out that problem but then goes off in another direction.
Very good characterisation of close, but distinct concepts. (a map of a domain)
If we squint a little, focus on close/far-away instead of same/distinct and s/metric/model/g (because usage of a metric implies a model), we can see how close these things can be.
Optimizing for the wrong metric - becomes “using a wrong model”.
Excessive efficiency - is partially “using a wrong model”, or maybe “good model != perfect model”. We start with good enough model, but after certain threshold we get to experience the difference between “good enough” and “perfect” (aparantly we care about redundancy, but it was not part of our model; so we were using a wrong model)
Overfitting is “finding the wrong model” (I wanted a model for the whole population, got a model only for a sample)
..or if we squint even more and go meta..
overfitting is part of “good model != perfect (meta)model” of modeling.
(using sample data is good enough, but not perfect)
P.S. I liked the article. Choice of the title - not so much.
P.P.S. Simplicity of a model is part of meta-model.
Those are great points! Another related law is from queuing theory: waiting time goes to infinity when utilization approaches 100%. You need your processes/machines/engineers to have some slack otherwise some tasks will wait forever.
You can add a measure of robustness to your optimization criteria. You can explicitly optimise for having enough slack in your utilisation to handle these unforeseen circumstances.
For example, you can assign priorities to the loads on your systems, so that you can shed lower priority loads to create some slack for emergencies, without having to run your system idle under during lulls.
I get what the article is trying to say, but they shouldn't write off optimisation as easily as that.
The problem is that people who agree to a task being low priority still expect it to be done in nine months and all of a sudden they become high priority if that doesn’t happen.
So you’re fixing the micro economics of the queue but not the macro. Queues still suck when they fill up, even if they fill with last minute jobs.
This totally depends on the system in question and what the agreements with your users are.
Eg if you are running video conferencing software, and all of a sudden you are having bandwidth problems, you typically first want to drop some finer details in the video, and then you want to drop the audio feed.
In any case, if you dropped something, you leave it dropped, instead of picking it back up again a few seconds later. People don't care about past frames.
(However, queuing instead of outright dropping can still makes sense in this scenario, for any information that's younger than what human reaction times can perceive.)
Similarly in your scenario, you'd want to explicitly communicate to people what the expectations are. Perhaps you give out deep discounts for tasks that can be dropped (that's what eg some electriticy providers do), or you can give people 'insurance' where they get some monetary compensation if their task gets dropped. (You'd want to be careful how you design such a scheme, to avoid perverse incentives. But it's all doable.)
> So you’re fixing the micro economics of the queue but not the macro. Queues still suck when they fill up, even if they fill with last minute jobs.
I don't know, I had pretty positive experiences so far when eg I got bumped off a flight due to overbooking. The airline offered decent compensation.
Overbooking and bumping people off _improves_ the macro situation: despite the occasional compensation you have to pay, when unexpectedly everyone who booked actually showed up, overbooking still makes the airline extra money, and via competition this is transformed into lower ticket prices. Many people love lower airfares, and have shown a strong revealed preference of putting up with a lot of stuff eg RyanAir pulls as long as they get cheap tickets.
A task "shed" is one delivered with infinite latency. If that's fine for you then the theorem doesn't hurt you, do what's best for your domain. It's just something to be aware of.
Yep, I used to work in a factory. Target utilization at planning time was 80%. If you over-predict your utilization, you waste money. If you under-predict, a giant queue of “not important” stuff starts to develop
For some scenarios that's fine, and you can slash the queue whenever necessary.
Eg at Google (this was ten years ago or so), we could always spend leftover networking capacity on syncing a tiny bit faster and more often between our data centres. And that would improve users' experience slightly, but it also not something that builds up a backlog.
At a factory, you could always have some idle workers swipe the floor a bit more often. (Just a silly example, but there are probably some tasks like that?)
Unlike merchantmen, naval vessels were crewed at a level allowing for substantial attrition (bad attrition would be casualties; good attrition would be prize crews); I believe they traditionally (pace Churchill) had many, many activities which were incidental to force projection (eg polishing the brightwork) but could be used to occupy all hands.
This reminds me of something my mother told me she aimed for when she ran her catering businesses: she always wanted 1 serving of pie leftover at the end of every day.
If she had 0, she ran the risk of turning customers away and losing money. Any more than 1 is excess waste. Having just 1 meant she’d served every possible customer and only “wasted” 1 slice.
Then you just get Little's law, which is not usually what people want. Preemption is usually considered pretty important... Much like preemptory tasks.
I’m remembering reading once that cities are incredibly efficient in how they use resources (compared to the suburbs and rural areas, I guess), and, in light of your comment about waiting time, I’m realizing why now why they’re so unpleasant: constant resource contention.
Sorry to put it so bluntly, but you're basically saying:
"I don't care it the climate's fucked, I want to live away from civilization and drive 100 miles a day everywhere"
Of course we shouldn't hyper-optimize everything, but sooner people realize our environment depends on not everyone getting exactly what they want whenever they want the better. Living in a (walkable) city is just one such concession towards the environment we ought to make, even if we don't "want" to.
Or we can stop acting like there’s only two options: living in wide-open fields with a clear horizon or the fucking walled city of Kowloon.
Also, you have the mindset of a typical anti-social coastal elite who thinks “oh no big deal we can just raise the cost of living for all the poor rural types by sticking on a tax because I want to go LARP as a Victorian manor lord. And people don’t bend to my every whim immediately or live exactly like me so I want to be in total control of the 50 miles around me.”
All I'm saying is that the efficiency arguments are silly unless you are comparing like for like. If we're suggesting that people simply do less because it's more efficient, well, no-one is going to do that without an incentive.
Everyone having 50 sqmi obviously isn't realistic (there actually is not enough space on the globe), but equally, if the idea is that everyone _has_ to live in a metropolitan apartment because each person has to use (1/7billion) of the resources, you're going to see an uprising, that just won't fly with people.
The best outcome is probably to convince as many people as possible to live in a shoebox so that the rest of us can still have a decent life. It seems to be working!
We're already competing under capitalism, and clearly the end-state isn't who'll get the most of what we already have, but who'll get the most of what's yet to get exploited. This competition doesn't have any upper bounds.
That’s not remotely what I’m saying. I live in a city and don’t drive most days because I can walk and take public transit and there’s never any parking. What I’m saying is that in the bigger picture, approaching life as a set of problems to be optimized is the wrong way to approach life.
Right, but if I had 1 hour in NYC versus 1 hour in LA how many clubs could I theoretically go to? Probably a dozen in NYC, provided I leave immediately. Probably about .5 in LA.
So while what you're saying is true, it doesn't disprove anything. LA is much less dense and therefore has much less "stuff" available for its inhabitants. But it's still more than a rural area.
It allows for a greater variety of things (museums, concerts, etc.), but to get that you have to deal with higher contention and, thus, costs across all things (whether in terms of time spent waiting or money spent outbidding others), including, crucially, the things you consume the most of (roads, housing, etc.). So maybe a good way to think about it is: if you have a lifestyle that requires a modest amount of most resources, then the variety provided by density may be worth the increased resource contention, but if you have a lifestyle that requires a lot of certain resources (like space for kids), then the tradeoff may no longer make sense.
On the other hand, in cities people are queueing up and talking at the bakery counter. While people in the suburbs are listening to the radio while driving to the bakery. I guess you choose to live where you feel most comfortable.
Maybe if you are a white male. Ever drive through a rural area a month out from this election? It’s a scary place, especially right now with the rhetoric being used just on the signs people are putting in their yards.
Interesting. My gut reaction is that this is true in reverse: infinite wait time leads to 100% utilization. However, I feel like you can also have 100% utilization with any queue length if input=output. Is that theory just a result of a first order approximation or am I missing something?
The average queue length is still infinity. Whatever the queue length happens to be at the start, it will stay there, and it could be any positive number up to infinity.
That's right, this is true no matter the queue length. If input=output on average, there is no limit on how long your queue will grow, and therefore no limit on how long queued task will wait.
I feel that a 100% efficient system is not resilient. Even minor disruptions in subsystems lead to major breakdowns.
There’s no room to absorb shocks. We saw a drastic version of this during COVID-19 induced supply chain collapse. Car manufacturers had built near 100% just in time manufacturing that they couldn’t absorb chip shortages and it took them years to get back up.
It also leaves no room for experimentation. Whatever experiment can only happen outside a system not from within it.
There is a fundamental tension between efficiency and resilience, you are completely correct. And yea, it’s a systems problem, not limited to tech.
There is an odd corollary, which is that capitalistic systems which reward efficiency gains and put downward pressure to incentivize efficiency, deal with the resilience problem by creating entirely new subsystems rather than having more robust subsystems, which is fundamentally inefficient.
This is exactly the subthread of this conversation I’m interested in.
Is what you’re saying that capitalism breaks down resilience problems into efficiency problems?
I think that’s an extremely motivating line of thinking, but I’ll have to do some head scratching to figure out exactly what to make of it. On one hand, I think capitalism is really good at resilience problems (efficient markets breed resilience, there’s always an incentive to solve a market inefficiency), on the other (or perhaps in light of that) I’m not so sure those two concepts are so dialectically opposed
To understand the effects, we first have to take a step back and recognize that efficiency and resiliency problems are both subsets of optimization problems. Efficiency is concerned with maximizing the ratio of inputs to outputs, and resiliency is concerned with minimizing risk.
The fundamental tension arises because risk mitigation increases input costs. Over a given time horizon, there is an optimal amount of risk mitigation that will result in maximum aggregate profit (output minus input, not necessarily monetary). The longer the time horizon, the more additional risk mitigation is required, to prevent things like ruin risk.
But here’s the rub: competition reduces the time horizon to “very very short” because it drives down the output value. So in a highly competitive market, we see companies ignore resiliency (they cannot afford to invest in it) and instead they get lucky until they don’t (another force at work here is lack of skin in the game). The market deals with this by replacing them with another firm that has not yet been subject to the ruinous risks of the previous firm. This cycle repeats again and again.
Most resilient firms have some amount of monopolistic stickiness that allows them to invest more in resiliency, but it is also easy to look at those firms and see they are highly inefficient.
The point is that the cycle of firms has a cost, and it is not a trivial one: capital gets reallocated, businesses as legal entities are created, sold, and destroyed, contracts have to be figured out again, supply chains are disrupted, etc. Often, the most efficient outcome for the system is if the firms had been more resilient.
So there is an inefficient Nash equilibrium present in those sort of competitive markets.
That’s a good clarification about firms vs. the broader system. I think that’s a pretty good breakdown, overall, and fits well with the general notion that capitalism is resilient, not efficient, by offloading efficiency onto smaller entities which are efficient, not resilient. You could compare to command economies where a firm failure is basically a failure of the state, and can destabilize the future of the entire system.
This is coincides with my headcannon cause of the business cycle.
1. Firms compete
2. Firms either increase their efficiency or die
3. Efficient firms are more susceptible to shocks
4. Firm shutdown and closures are themselves shocks
5. Eventually the system reaches a critical point where the aggregate susceptibility is higher than the aggregate of shocks that will be generated by shutdowns and closures
6. Any external shock will cause a cascade
There's essentially a "commons" where firms trade susceptibility for efficiency. Or in other words, susceptibility is pooled while the rewards for efficiency are separate.
It has inevitability, but you're right, not predictable periodicity. Is predictable periodicity a necessary part of a cycle? I feel like the rise and fall of nations is a cycle, but not necessarily one of predictable periodicity.
There was a sci-fi series I read (I want to say by Alastair Reynolds) which talked about planet-bound civilisations having an innate boom-and-bust cycle where the civilisation would inevitably get more and more efficient at utilising resources, while thereby becoming more fragile and susceptible to system shocks. It would then collapse and eventually the survivors would rebuild.
From a social / emotional / spiritual/ humanistic perspective, this is what I see in the "productivity" and "wellness" spaces.
"Ahh, if only I hyperoptimize all aspects of my existence, then I will achieve inner peace. I just need to be more efficient with my time and goals. Just one more meditation. One more gratitude exercise. If only I could be consistent with my habits, then I would be happy."
I've come to see these things as a hindrance to true emotional processing, which is what I think many of us actually need. Or at least it's what I need - maybe I'm just projecting onto everyone else.
Some of us are trying to optimize for things other than happiness. An occasional bit of happiness can be a nice side effect of certain types of optimization but happiness isn't a reasonable goal to focus on by itself.
Happiness is a valid goal. If one perceives it’s not reasonable to expect it, then you may arrive at this conclusion. But imo that’s because we short-circuit happiness to sources of pleasure that we see aren’t that reliable.
Hell, even this settling for happiness as a side-product is a result of the judgement that this is the best we can do regarding the goal of happiness.
Can't agree with you more my friend. Another point on a philosophical level is efficiency or optimization in life, which always focuses on tangible aspects and ignores the greater intangible aspects of life.
And that's leaving out Jevon's paradox, where increasing efficiency in the use of some scarce resource sometimes/often increases its consumption, by making the unit price of the dependent thing affordable and increasing its demand. For example, gasoline has limited demand if it requires ten liters to go one km, but very high demand at 1 L/10km, even at the same price per liter.
When people know the answer is always “no” they save their energy to plea for stuff they really can’t do without. You start saying yes and they’ll ask for more.
The trick is as always to find out the XY problem. What they really need may be way easier for you to implement than what they actually asked for.
Sometimes you can just embrace it, instead of looking for tricks.
If you are in the business of selling any product or service, then it's great that finding a way to make it cheaper also generates more demand for you.
I’m confused, because the “not trick” I’m talking about is the boondoggle created by giving people exactly what they ask for, making nobody happy and jamming up your throughput in the process.
To be specific: if you can find a way to make fridges for half the previous cost, and you can sell them for three quarters the previous price, you don't want to talk people out of buying more fridges. In fact, them buying vastly more fridges is exactly what you want.
I mean I'm general, I'd rather buy fewer of the same things no matter if it's cheap or expensive if I didn't have to, and it would use less resources. Juicing someone's quarterly sales report is no good reason for me to buy a refrigerator, yet here we are.
Oh, you would already have other reasons to buy a fridge. They are really useful to keep things cool, and thus keep your food and drink from spoiling.
But (in our example) the high price of a fridge has so far kept you from buying one. Luckily, prices have recently been falling, so you can finally afford one.
You look forward to a new era of less spoilage and less food waste in your life; because you'd rather use less resources. (The electricity to be burned by the new fridge comes from your rooftop photovoltaic, of course.)
---
Does this make sense? You need have no regard for anyone's quarterly sales reports.
Yeah, but it is also second-order effects where the efficient use of a resource opens it up for more uses as well as for more exploitation. Perhaps this is most visible with farmland. Efficient use of water (center-pivot sprinkler) causes much more land to be arable, causing more use of that same water as well, depleting aquifers.
Metrics are ambiguous because they are abstractions of success and miss context. If you want a pretty little number, it doesn’t come without cost/missing information.
I don’t know if this phenomenon is aptly characterized as “too much efficiency”.
I think it also applies to when managers try to overoptimize work process, in the end creative people lose interest and work becomes unbearable...little chaos is necessary in a work place/life imo...
I kill my desire to work on a lot of side projects by trying to over optimize the parts I’m not going to like doing. I should just do the yucky parts and get past them. But at least nobody is paying me to spiral.
And for me it was this ingenious 2019 paper co-authored by Stephan Hoyer and Sam Greydanus on doing structural optimization by employing a (constrained) neural network as a storage/modifier/tuner of the physical model describing the structure to optimize: https://arxiv.org/abs/1909.04240
Super interesting approach and very well written paper.
I recognize the author Jascha as an incredibly brilliant ML researcher, formerly at Google Brain and now at Anthropic.
Among his notable accomplishments, he and coauthors mathematically characterized the propagation of signals through deep neural networks via techniques from physics and statistics (mean field and free probability theory). Leading to arguably some of the most profound yet under-appreciated theoretical and experimental results in ML in the past decade. For example see “dynamical isometry” [1] and the evolution of those ideas which were instrumental in achieving convergence in very deep transformer models [2].
After reading this post and the examples given, in my eyes there is no question that this guy has an extraordinary intuition for optimization, spanning beyond the boundaries of ML and across the fabric of modern society.
We ought to recognize his technical background and raise this discussion above quibbles about semantics and definitions.
Let’s address the heart of his message, the very human and empathetic call to action that stands in the shadow of rapid technological progress:
> If you are a scientist looking for research ideas which are pro-social, and have the potential to create a whole new field, you should consider building formal (mathematical) bridges between results on overfitting in machine learning, and problems in economics, political science, management science, operations research, and elsewhere.
[1] Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
>> If you are a scientist looking for research ideas which are pro-social, and have the potential to create a whole new field, you should consider building formal (mathematical) bridges between results on overfitting in machine learning, and problems in economics, political science, management science, operations research, and elsewhere.
Translation to laymen: ML is being analogized to the mathematical structure of signaling between entities and institutions in society.
Mathematician proposes problem that plagues one (overfitting in ML, the phenomena by which a neural network's ability to generalize is negatively impacted by overtraining so the functions it can emulate are tightly coupled to the training data), must plague the other.
In short, there must be a breakdown point at which overdevelopment of societal systems or signaling between them makes things simply worse.
I personally think all one need do is look at what would happen if every system were perfectly complied with to see we may already be well beyond that breakpoint in several industrial verticals.
The exciting thing about this idea is if you can correlate, say, economics with the works of ML, that means a computer program which you can run, revise and alter can directly give you measurable data about these complex system interactions that mostly have existed as a platonic idea since reality is too nuanced and multiple to validate concepts formally.
With the idea that there is some subset of logic that sits below economics that is provable and exact. That is a powerful idea worth pursuing!
This idea has been pursued several times in the past, and it always ends up producing lots of interesting academic results and no practical conclusions.
It's certainly an interesting perspective on the development of complex systems. The idea that an economy can be somehow overfitted to its own incentives and constraints I don't think is entirely new, cf the Beer Game. But as a general concept, it's certainly not something that usually finds its way into policy discussion, beyond some very specific talk about reshoring of certain critical industries.
However, I think the most important benefit of this perspective is going to be providing yet another counterargument against the Austrian economics death cult.
It seems to me that something similar to Adam Smith happened to the Austrians: their ideas have been cherry-picked. According to German Wikipedia, their main things were / are a focus on individual preferences, marginal utility, and a rejection of mathematical modeling(!)
There was also something about lower state expenditures (...taxes...) giving better results for the people - that's the one that seems to be very popular with rich people for some reason. Go figure.
Austrian economics also rejects empirical assesment of its claims. Instead, universal thruths are derived "logically" (formal logic banned though) from "obviously true" axioms using a method called praxeology.
It seems a lot like Scientology: the more you learn about it, the more bizarre it gets. And of course it's used to extract a lot of money for few benefactors.
Well if you can turn chatgpt into an intelligent actor in a simulated economy, and are able to run it at scale, I bet you can get some valuable insights.
Interesting timing for me! Just a couple of days ago I discovered the work of biologist Olivier Hamant who has been raising exactly this issue. His main thesis is that very high performance (which he defines as efficacy towards a known goal plus efficiency) and very high robustness (the ability to withstand large fluctuations in the system) are physically incompatible. Examples abound in nature. Contrary to common perception evolution does not optimise for high performance but high robustness. Giving priority to performance may have made sense in a world of abundant resources, but we are now facing a very different period where instability is the norm. We must (and will be forced to) backtrack on performance in order to become robust. It’s the freshest and most interesting take on the poly-crisis that I’ve seen in a long time.
We've seen this during the COVID pandemic supply chain disruptions as well, where just in time supply chain management doesn't work as expected when operating in an abnormal environment.
I'd always thought this conclusion was just a given.
Highly optimized systems take full advantage of their environment and rely on a high degree of predictability in order to avoid redundant operations.
These systems minimize the free energy in the system, and so very little free energy is available to counteract new forces introduced to the environment which act on the system.
You'll find parallels in countless domains, since the very basis for learning and stabilization of a system revolves around becoming more or less sensitive to a given stimulus. Examples could be attention, supply chain economics, institutions, etc.
I was gonna come here to say that, especially how there was a shortage on toilet paper. I remember reading it was becuase factories were so efficient that when people started using the toilet at home instead of the office, it was hard to switch the factories from making commercial to residential toilet paper. I think someone even made the pun of paper-thin margins.
It's not just Covid. Look at the medical world. Generic products compete on price and there is little profit margin--not enough to warrant overprovisioning against problems. And meeting FDA requirements for new activities means new players can't just jump in the game. (And we sometimes see this done maliciously--control all active production of something and shove the price through the roof.) One factory has a problem and there can be huge problems downstream as a result.
The only solution I see is for the FDA to include supply reliability in it's determination of whether a system is acceptable.
Giving priority to performance may have made sense in a world of abundant resources, but we are now facing a very different period where instability is the norm.
I don’t think it’s smart to proactively back track without being very careful. One thing that’s needed is for corporate death to be allowed to occur. Right now the downsides of risky behavior is bailed out for large enough risk. Then the companies that fail aren’t robust and the ones that don’t are but bailouts let non robust companies keep going. Otherwise “robustness” is a property without a measure which means that you’ll get robustness theater where actions are being taken in the name of being robust but it’s not actually making a difference at best and could be making things worse.
As for society itself being robust, it’s a much harder property. Being robust is nice but no one actually wants to live in a metered society where there’s insufficient resources - they’d generally rather kill for resources greedily and let others fail without helping them. That’s why socialized healthcare struggles - while it guarantees a minimum of care for everybody, the care provided has longer wait times and most people are not willing to wait their turn.
The usual cycle for business in a free market is it appears young and fresh, lacking any parasites. It grows rapidly, displacing existing mature businesses. Then, it accumulates bureaucracy and parasites, becoming less and less efficient, strangled by bloat and inability to adapt, and slides into bankruptcy, replaced by the next generation of new businesses. The remains of the business are then reallocated to the next generation of businesses.
(This is quite unlike the common view that businesses inevitably grow to take over the world.)
I.e. business is much like a living organism.
Problems set in when the government bails out failing businesses.
Even worse are government "businesses". They are not allowed to fail, and the inefficiencies, parasites, corruption, grow and grow. When can you remember a government agency being abolished? Eventually, the government will collapse.
> Even worse are government "businesses". They are not allowed to fail, and the inefficiencies, parasites, corruption, grow and grow. When can you remember a government agency being abolished?
In Commonwealth countries and the UK itself there are plenty of businesses called “crown corporations” which are owned by the government. Change in attitudes towards more liberalism led governments to deregulation and selling off bits and pieces or the entire corporation. Here are some Canadian examples:
America is a relatively young country and has very peculiar philosophies sometimes not found in the rest of the world. Be very careful extrapolating an American perspective abroad or as capturing some elemental truth of the universe.
Not just government agencies. Everybody wants their finger in the pie to justify their job. And every politician wants to do things their voters like.
I'm thinking of a reasonably recent article I saw that was talking about helping people navigate the 30+ assistance programs they might be eligible for. There's your problem right there--there should not be 30+ programs doing approximately the same thing! That's an awful lot of duplication of effort.
Or look at what happens with business licenses. Two things I see:
1) They want their $ from entities that shouldn't really be "businesses" in the first place. Around here an awful lot of licensed professionals have to have a "business" license--never mind that the nature of their work means they're inside some other entity that actually is reasonable to license. And that means a sales tax registration which has an annual minimum that such people almost certainly will never reach. (Sales tax includes use tax--but it's their office that actually engages in such transactions.)
2) Businesses that perform their work on-site have to have business licenses for every license area of the metropolitan area they work in. Hey, guys, get together and define the superset of the rules of your area and allow someone to get a license that covers the whole area based on that superset.
The Republicans are "right" in that we have far too many regulations. But they are very wrong in wanting to take an axe to them--most of the rules are individually sensible (and when they produce nonsense it's often situations where it's not worthwhile to special case), there is a horrible problem of duplication of effort and fingers in the pie. It's not chopping that's needed, it's organization.
One of the primary reasons people bail out companies are the knock-on effects. People losing jobs, etc. If society itself is robust enough to cover for people in those situations, we could let companies fail far more.
There's a sentiment on here often that, even if a company has been essentially blown up by technology or market change, they should have transformed themselves to adapt. But that implies they probably needed to rototill their workforce in any case. At some point, you're probably better off just declaring bankruptcy and starting fresh or letting someone else do so.
True, but for some companies there are also national security concerns. If we lose the domestic supply chain for certain items then that limits our freedom of action and leaves us vulnerable to supply disruptions.
If you depend on a single company to supply certain items, you have a big problem already. Pouring money in that company will mostly help the executive bonuses, not the national security.
In a free market economy we shouldn't demand robustness, we should create a system that promotes and rewards robustness. A strict commitment against bail-outs would certainly be part of that. Companies (and private people) can decide to lower their risk exposure (at the cost of efficiency/profit) or take out insurance against risks. And if they go the insurance route they have to assess how likely their insurance is to go insolvent at the next insurance event. That's how you reward those that are actually resilient.
Healthcare is more complicated. It can never work as an efficient free market since nobody goes comparison shopping for the hospital with the best value-for-money when they have a car crash. That's why socialized healthcare achieves much better results per dollar spent. But it's often hamstrung by attempts at efficiency.
I think a better societal example is disaster relief: helping people back up after they have been hit by a hurricane is the humane thing to do, but how much is that encouraging people to settle in high risk areas with insufficient precautions?
I don't see why people can't comparisons shop for hospitals before they get in a car crash. Unless I am literally unconscious I would go to the hospital in my area that I trust the most, and I have plans for which urgent care, clinics and hospitals I would take someone else to if they needed a driver.
In fact I think a pretty small fraction of patients arrive at the ER unconscious.
How would develop the "trust" and why would it be correct? How would you diagnose yourself or others before selecting a hospital if those have different trust for different things? How do you balance urgency vs different trust levels if the hospitals are not all the same distance?
It also ignores that huge swaths of the country have no choice at all and the only hospital within a hundred miles is only viable due to huge Federal subsidies. We’ve been helping a close family member navigate that scenario and sure, he could vote with his dollars but it would involve a three hour drive to a neighboring state for an 80-yr old. I’d rather just enforce minimum quality standards on everyone like most other civilized countries rather then relying on “the free market” which so far in my experience has just led to PE goliaths swallowing entire health systems to focus on bill collection and union busting.
edit: didn't realize I was feeding a troll. Feel free to ignore.
I expect the objections are in how quality is measured and enforced.
It reminds me of education system in the US - most people (project 2025 aside) think it's good to have a public education system; having a pipeline of skilled workers makes it easier to build an economy filled with a diverse set of businesses.
However, the attacks start to fly when there is disagreement about who should be allowed to teach, how they should be measured, and how they should be paid.
CMS does enforce minimum clinical quality standards on hospitals (at least those that accept Medicare). The problems in areas without meaningful competition tend to be more around shortages of qualified practitioners, high prices, and abusive billing policies.
I've never needed a medical diagnosis to decide between calling my GP and going to an urgent care. It's just a bit surreal to hear everyone else say my ordinary survival skills are impossible and more than could be asked of anybody!
You said you have a hospital selected you trust (by whatever your metric is). Hospitals tend not be all equal for all things, so trust should probably be differential - how do you assess yourself as a patient then to decide on where to go? And if you do not differentiate the trust any further than to a single hospital regardless of what the issue is: why is that sufficient?
I think it is fine to have some preferences for a hospital, but not sure how much benefit that confers outside of some narrow situations.
Sinply replace hospital with any other service, take your own answers and then translate it back. In economic terms I researched medical facilities until the expected marginal benefit of the information fell below the marginal cost. There are a lot of reasons to reform the US healthcare system but you can't argue that consumer choice is too complex to be realized.
Just to be clear: You're asserting that the average citizen
* has the same capacity to research an unknown number of medical procedures and the doctors performing them as they do researching onion prices or CPU specs
* faces a similar scale of consequences when failing to properly analyze medical procedures as they do when they fail to properly price-compare onions or PC services
* has the same freedom of choice to "purchase their preference" in an emergency, life-threatening situation as they have when shopping for PCs or groceries
Dietary and metabolic problems are an epidemic that outweights malpractice in terms of quality and quantity of life by more than two orders of magnitude - so yes, I am saying people face "shopping problems" of life or death magnitude every day.
>private people) can decide to lower their risk exposure
I think the complexities of modern societies make it too difficult to measure this risk adequately. We just don’t have the bandwidth to think about the second-and-third order effects for every social/financial interaction we encounter. And people are generally very poor at estimating high-consequence/low-probability events. This means people will often take very outsized risks without realizing it; when bad things happen it creates an unstable society. I don’t think we’ve evolved to personally manage all the c risks in a large complex society and farming those risks out to institutions seems to be the current way most societies have decided to mitigate those risks.
>...farming those risks out to institutions seems to be the current way most societies have decided to mitigate those risks
Unfortunately, those institutions --be they governments, insurance companies, UL Labs, banks, venture capitalists, etc.--also need to be vetted.
Even when staffed with impeccably well credentialed and otherwise highly capable people, their conclusions may be drawn using a different risk framework than your own.
The risk that they mitigate may even be the risk that you won't vote for them, give them money, etc.
There is also the risk of having too little risk, a catastrophe no worse than too much risk. The balloon may not pop, but it may never be filled.
I don’t think anyone reasonable is advocating believing institutions on blind faith (possibly with the exception of religious institutions). They need to be transparent and also strive to reflect the values (risk and otherwise) of their constituents.
It also doesn't strike me as very fair. If you smoke, should you not receive cancer care because you took unnecessary risk?
I can see how you could arrive at similar conclusions from a risk management perspective, but it's not a morally just system. Within the system risk taking must be accepted.
Depending on your definition of “fair” this presupposes people are good at estimating risk. The above premise was is based on the fact that they are not. I think there’s a lot of behavioral research that backs that up.
That solution is never going to work when black swan events occur on the order of every 5-10 years and executive vision is focused on the next quarter with little concern paid to anything outside the next 2-3 years. Nobody is going to want to give up short term performance to mitigate risks that probably won't manifest until after they've left for a better job.
5-10 years is a perfectly normal investment horizon, and in the end investors are the ones electing the CEO and setting goals and rewards for the executive. If betting on the long term is a winning strategy companies absolutely have the means to do that. But right now it usually isn't.
That solution is how it already works for the vast majority of companies in the US.
“Too big to fail” is a meme that only applied to a tiny handful of companies during the financial crisis. Take a look at SVB for how fast a stalwart huge bank can implode with zero fucks given by the government.
By "zero fucks given by the government" do you mean the government got involved, effectively bought the bank, and took responsibility for 100% of deposits (most of which were the balances of startups, ie venture capital investments)?
Nope, shareholders got wiped out and the bank was done as a bank.
What you’re thinking of is FDIC which is completely the opposite of a bailout for the bank. It’s a bailout for depositors (a huge portion of which were normal people). Arguments for the FDIC protecting people from keeping money in bad banks is a different argument, but it most certainly isn’t a bailout.
If you think going bankrupt and the FDIC seizing your company and wiping out shareholders is a bailout, you don’t know what a bailout is at all. That’s standard bankruptcy with an extra heavy boot on the throat from the government because they are ruthless about maintaining consumer confidence in the banking system.
I didn't say bailout. You said the government gave zero fucks but I think it actually went way above and beyond the normal FDIC insurance to make sure ALL depositors were made whole not just up to the normal 250k.
Businesses won't plan for black swan events when the people operating them have other sufficient wealth that the death of the company doesn't pose a serious problem for them. When CEOs make enough in a year to retire, there's no need to to worry about a potential catastrophic failure next year.
Yeah, that's the real problem. Too much efficiency in the short term.
My idea on working around this: for any business with actively traded stock there is a salary cap, say $1m/yr *per year*. You want to pay that guy $10m/yr? No, you pay him $1m and he gets 9 sets of shares that are worth $1m now, but they will be delivered one a year. Next year, same thing, you give him $1m, one set of shares from the previous year is delivered to him, he's got 9 new sets coming. So long as you have such shares forthcoming you are not permitted to engage in any trade where you would gain from the stock going down. If you do so inadvertently (say, investing in a fund that shorts the stock) any income from that is taxed at 100%.
The idea is to make your top people care about the long term prospects of the company, not merely the prospects of their area for whatever time they're in charge of it.
Patients have time to shop for most healthcare services. Only a small fraction of healthcare spending is for emergencies. The highest cost stuff is mostly elective procedures. If you need a colonoscopy or hip replacement then you have time to shop around.
Socialized healthcare has its advantages and is probably more cost effective on average. But we also see affluent Canadians coming to the USA as medical tourists and paying cash for MRI scans in order to avoid the queues back home.
> Patients have time to shop for most healthcare services.
Patients have the time but rarely have the actual ability to shop around outside asking "is this provider in my coverage plan?" They demand me to sign a document stating I'm willing and able to pay while often never being able to actually tell me what the procedure will actually cost. Often, they won't even know that same day the procedure is done, it'll be weeks before I'm actually invoiced. And don't even get me started when you've chosen the surgeon in your plan, the facility in your plan but it turns out the anesthesiologist they scheduled wasn't in your plan. Oops. That's an expensive mistake you made, should have shopped around!
My knee kept locking up and I'd experience tremendous pain. Only once every few weeks though, so I had time to "shop around". I called up several places and tried to get an estimate of what it would cost ahead of actually seeing the doctor. Nobody would actually offer that, they could only make an appointment to see the doctor. No idea what the doctor would actually want to do during that appointment, so who knows what things will cost. Will they want x-rays? Will they want an MRI? Can they do the MRI there? Won't know until you commit to paying!
And out of the few dozen choices of kinesiologists around me which were covered by my insurance few had any appointments available within the next several weeks. Many weren't seeing new patients. So really it was deal with my knee randomly causing me immense pain for several more months or take whoever had the first appointment. And this is in one of the top five largest metro areas in the country, not some small town in the middle of nowhere.
Shopping for which hospital to do the delivery of my children, the estimates for our costs after insurance had a massive amount of uncertainty to the point of being useless. Could be $4k, could be $20k, who knows. Imagine going to a burger joint and the menu says a burger could be anywhere from $1 to $50, we'll invoice you in a month. Go down the street, menu says it could be $3 to $48, we'll invoice you in a few weeks. What an ability to shop around! Free market at work!
> avoid the queues back home.
I already mentioned, most kinesiologists around me were fully booked for months. Very few had anything within several weeks. That's queueing.
I tried to book an appointment with a new dermatologist a few months ago. Once again, in this very large metro area. For dermatologists in my area covered by my insurance, the earliest appointment was six months out. It took several months to get a family member's hip replacement scheduled. We have queues in this country as well.
Getting medical imaging is generally pretty quick and easy though, and places like MRI imaging centers just want to keep moving people through so if they have an empty spot in an afternoon having someone in the machine constantly is important. It's also generally the easiest thing to automate in healthcare; mostly just a matter of getting enough machines and lightly trained techs to rotate people through. Radiologists are often off-site contractors getting paid for every scan they review.
It's unreasonable for you to expect a cost estimate on knee pain. You can ask the provider organization how much they charge for a regular office visit, and then check your coinsurance or copay amount. During the initial office visit the doctor is likely to recommend follow-up tests, imaging scans, medications, and/or physical therapy; you can then ask for price estimates on those additional services.
The No Surprises Act does give patients some protection against high charges for out-of-network services.
> It's unreasonable for you to expect a cost estimate on knee pain
Ok, but then it goes back to this idea of queueing and the "free market". Ok, so it'll take me a few weeks to go to the first doctor just to get his paid estimate. If I don't like whatever estimate he gives (which once again he probably won't directly) what am I to do? Start calling around, make another appointment with someone else several weeks later and pay yet another appointment fee? Hope his prices are better? Rinse and repeat to get a few quotes?
And so continuing the burger example, you call ahead to book a time well in advance and pay $2 just to be able to look at the menu. What a free market.
One of my kids had to get tubes in his ears. I didn't actually get a good faith estimate of cost until that morning in the hospital despite calling several times. I had to schedule, wait weeks, show up early in the morning, and refuse to sign for the financial liability until I got an actual estimate. Free markets at work here guys, totally not a broken system, easily just shop around. And yeah, sure, I could have gone to see a different audiologist and then gone through scheduling it all again and waiting another few months and a couple hundred dollars. After all its just my kid's speech development, no big deal delaying that another month or two (or three).
At least it's good to see this No Surprises Bill solved at least that one example of the healthcare industry screwing people over. Thanks for sharing that.
I would love to see a good proof that it works, all the discussions, rumors and anecdotal evidence suggest the contrary. I am open to learn the truth, with hard numbers.
Very long waiting times are the first thing that comes to mind regarding such failures, with UK and Canada at the top spot. It is not uncommon to die waiting for a consultation to be diagnosed in 1-2 years.
What do you wanna good proof of?
Life expectancy? What population health metrics do you want?
Almost all EU countries with socialized healthcare beat US and even UK. Canada is more near EU level.
Do you also want US anedoctal evidence?
What’s the consultation time for diagnose in the US for people without insurance/ insurance doesn’t cover/insurance says it doesn’t cover/no money??
Never?
How does that affect the average/mean?
Life Expectancy:
Italy 83.7;
Spain 83.7;
France 83.3;
Sweden 83.3;
Canada 82.6;
Portugal 82.4;
Germany 81.4;
United Kingdom 81.3;
United States 79.3;
Poland 78.6;
Sometimes insufficient capacity and long wait times can be the result of a government's agenda.
Here in BC, we have (mandatory) auto insurance provided by a crown corporation with a monopoly. The neo-liberal government before the current more socialist leadership hated it and wanted desperately to privatize car insurance. The problem is that ICBC is dearly loved by most residents here. There is a playbook for this problem.
They appointed a fairly incompetent civil servant to run things, and also started raiding the fund, to the tune of billions of dollars.
After about a decade of this, the company was a mess and nearly broke. They were forced to raise rates. The premier characterized the situation as a "dumpster fire" and editorials started popping up arguing for privatization.
That government was defeated, and the new leadership sorted it all out. Within a couple of years, drivers in BC were getting cheques in the mail because ICBC was making too much profit.
There are powerful interests very interested in getting a piece of healthcare in Canada, and some of the shenanigans you see here smell a lot like a set-up to make things become broken enough that the voters will demand privatization.
Just for one example, in the last few years staffing agencies have been hiring away nurses by offering higher wages and then contracting them back to the health authorities at ~$130/hr. This has cost billions to taxpayers and lead to great resentment within the regular staff.
The problem with this is principal-agent problems. The owners of the business don't want it to fail. The people working there want to make money. They generally live their life and enjoy what money they make before the chickens come home to roost. It can be hard for the owners to realize the business is fragile before that fragility becomes apparent. In the mean time the people running the business made a bunch of money, potentially jumped to other jobs or retired or died.
And the owners could have sold when the business was propped up by unknown fragility.
Human lives are too short for these kinds of feedback loops to be all that effective.
There are also a lot of engineering examples where the goal is to optimize for reliability. I think the most common domain is marine platforms where it is prohibitively expensive to induct and repair (you have to send a team out by helicopter, for example).
And yet most large merchant ships are designed with a single engine, propeller, and rudder to optimize for cost instead of reliability. We have seen some spectacular failures of that approach recently, although it probably still makes sense in aggregate.
A major mechanical casualty beyond what the crew can repair usually means a tow to a shipyard. Flying more engineers in by helicopter would seldom help, and often isn't feasible.
This is true, but different than the maritime platforms I was talking about. The ones that tend to focus on reliability-centered optimization are platforms used for drilling, not transport. Even then, you will see instances where they decide to optimize for cost/schedule (eg Deepwater Horizon). IMO, that is a company-cultural issue.
Btw- reliability optimization doesn’t necessarily mean it is optimized to not fail. They are optimized to fail within some predetermined risk level. What that risk level should be is an entirely different discussion.
The bridge that collapsed wasn't due to a single engine, propeller or rudder. It was due to a single electrical system. One intermittent electrical issue left the ship basically helpless even though all propulsion and steering was undamaged.
His main thesis is that very high performance (which he defines as efficacy towards a known goal plus efficiency) and very high robustness (the ability to withstand large fluctuations in the system) are physically incompatible.
…what about humans? We’re far more efficacious than any other animal, and far more capable of behavioral adaptation.
Plus, isn’t “physically impossible” a computer science argument, not a biological one? Unless we’re using the OG “physis”==“nature”, I guess
To a first approximation, humans have never lived in a world of abundant resources. That has mostly only applied to a minority of affluent people in developed countries. But resource abundance continues to improve on average worldwide.
I don't know that the poly-crisis is bit this does feel timely.
I know I'd tolerate a digital experience of far lower fidelity (fewer pixels, for instance, or even giving up GUIs altogether) if I could get it in a way that doesn't break every time some far away person farts near a cloud console: A trade of performance for robustness.
Unedited bullet points on a related topic (same prefixes are linear, different prefixes connect to the others, but I haven't decided where yet):
>capital concentration increases
>expectations for what capital owners can do with money increases
>expectations exceed available capital
>investment returns must increase (race to the top)
>cooperation among capital owners must increase to get better returns
>capital owning group begins to self-select and become less diverse, if this wasn't already caused by the background/personality required to accrue capital
>investment theory converges on a handful of "winning" ventures
>because this is where capital is flowing, workers are forced to divert to these ventures
Your sound system has a volume dial to turn up and down the gain of the track even though you could get the same effect by re-recording the track at a higher volume; isn’t that curious?
Brilliant enough to know he's helping build another atom bomb (presumably for peanuts)? And the nuclear briefcase is gonna be controlled by the ultrarich.
This is a really manipulative way to categorically hand-wave away objections without actually responding to their content, in addition to having several logical fallacies (such as the appeal to emotion and the argument from authority). This is not in the spirit of intellectual curiosity that HN is for.
There was no need to invent a new law named "strong version", it already exists: Campbell's law.
The subtle difference between the two being exactly what the author describes: Goodhart's law states that metrics eventually don't work, Campbell's law states that, worse still, eventually they tend to backfire.
I find this article a bit odd, considering what the author is an expert in: generative imagery. It's the exact problem he discusses, the lack of a target that is measurable. Defining art is well known to be ineffable, yet it is often agreed upon. For thousands of years we've been trying to define what good art means.
But you do not get good art by early stopping, you do not get it by injecting noise, you do not get it by regularization. All these do help and are essential to our modeling processes, but we are still quite far. We have better proxies than FID but they all have major problems and none even come close (even when combined).
We've gotten very good at AI art but we've still got a long way to go. Everyone can take a photo, but not everyone is a photographer and it takes great skill and expertise to take such masterpieces. Yet there are masters of the craft. Sure, AI might be better than you at art but that doesn't mean it's close to a master. As unintuitive as this sounds. This is because skill isn't linear. The details start to dominate as you become an expert. A few things might be necessary to be good, but a million things need be considered in mastery. Because mastery is the art of subtly. But this article, it sounds like everything is a nail. We don't have the methods yet and my fear is that we don't want to look (there are of course many pursuing this course, but it is very unpopular and not well received. Scale is all you need is quite exciting, but lacking sufficient complexity, which even Sutton admits to be necessary). It's my fear that we get too caught up in excitement that we become blind to our limitations. Because it's knowing those limitations that is what gives us direction to improve upon. When every critique is seen as spoiling the fun of the party, we'll never be able to have anything better. I'm not trying to stop the party, in fact, I'm worried it'll stop.
I think you're probably right tbh. But I do think this point could be stressed a bit more. Especially when we're talking about how easy it is to trick ourselves into thinking we're doing what's good enough.
> But this article, it sounds like everything is a nail
In the process, acting somewhat like a generalization of the problem it describes: overly precise and narrow approaches to "improve" ineffable qualities. But the author seems to understand that - he comments on the absurdity of some direct transfers of ML methods to real world problems. I think he just added a bunch of not necessarily well solvable, but particularly suffering from "overfitting", example problems. It's a food for thought article, not a grand proposal.
When we optimize we typically have a specific scenario in our head. With the proper tools one can probably make the mathematically optimal decisions to deal with this exact scenario.
However: 1) This exact scenario will likely never materialize 2) You have not good quantification of the scenario anyway due to noise/biases in measurements.
So now you optimized for something very specific, and the nature throws you something slightly different and you are completely screwed because your optimized solve is not flexible at all.
That is why a more “suboptimal” approach is typically better and why our stupid brains outperform super fancy computers and algorithms in planning.
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[ 2.5 ms ] story [ 291 ms ] threadThe flexibility necessary to succeed in a real world requires a certain level of inefficiency.
My point is that error correction codes have a precise mathematical definition and have been deeply studied. Maybe there is a general principle at work in the wider world, and it is amenable to a precise proof and analysis? (My guess is that mileage may be made by applying Information Theory, as used to analyse error correcting codes.)
All of the examples involve a bad proxy metric, or the flawed assumption that spending less improves the ratio of price to performance.
It eventually becomes a bad proxy metric.
to quote wikipedia quoting Sickles, R., and Zelenyuk, V. (2019). "Measurement of Productivity and Efficiency: Theory and Practice". Cambridge: Cambridge University Press.
Offering that criticism without clarifying what efficiency measures in your opinion doesn't allow us to follow your viewpoint without us just taking your word for it. Needless to say this isn't considered good style in a discourse.
A 100 percent "efficient" system can be one that is overfitted to certain metrics and it is the typical death sin of management to confuse metrics with reality and miss that their great numbers hollow out anything that makes a system work well and reliable, because guess what: having 1 critical employee and working them like a mule is good when things work, but bad when they suddenly don't, because that second employee you thought was fat that could be cut, was your fallback. In that case your metric of efficiency was slightly increased while another, less easy to quantify (and therefore often non-existent) metric of resilience went down significantly. This means if your goal was having an efficient and resilient company, but your metric only measured the former, guess what.
Same is true in engineering, where you can optimize your system so much to fit your expected problem, one slight deviation within the problem now stops the whole thing from working alltogether (F1 racing car when part of the track turns out to be a sucky dirtroad). Highly optimized systems are highly optimized towards one particular situation and thus less flexible.
Or in biology, where everybody ought to know that mixed woods are more resilient to storms and other pests, while having great side effects for the health of the ecosystem, yet in pure economic terms it is easy to convince yourself the added effíciency of a monoculture is worth it economically, because all you look at is revenue, while ignoring multiple other metrics that impact reality.
What it means is the objective can't be static - for example once satiated, you need to pick different one to keep improving globally. Or do something else that moves the goalpost.
If it turned out that the term actually started in tailoring before statistics really got it's feet under it (which I absolutely cannot say that it did, just that trying to extrapolate backwards that sounds like a reasonable guess) then it wouldn't speak poorly of you if you hadn't also known that.
The reason I have this quibble is because the author says things like
>you should consider building formal (mathematical) bridges between results on overfitting in machine learning, and problems in economics, political science, management science, operations research, and elsewhere
If we are appropriately modest and acknowledge the fact that overfitting is well-studied by statisticians (although, obviously not in the context of deep neural networks), it seems kind of ridiculous to make statements like, economists and political scientists should consider using statistics?
A corollary would be some other relation that can be deduced as a result of P implies Q, not simply a restatement of P implies Q.
(Using the discrete math definition of imply, not the colloquial definition of imply).
A very common use is to re-state something so it's in the exact form of something you said you'd prove. Another common case is to highlight a nice incidental result that's a bit outside the path towards the main result -- for example, it immediately follows (perhaps logically equivalent to) something that's been proven, but it's dressed in a way that catches the attention of someone who's just skimming.
I'll also quibble with the example of obesity: the proxy isn't nutrient-rich good, but rather the evaluation function of human taste buds (e.g. sugar detection). The problem is the abundance of food that is very nutrient-poor but stimulating to taste buds. If the food that's widely available were nutrient-rich, it's questionable whether we would have an obesity epidemic.
Carbohydrate abundance was likely important in moving people out of hunger and poverty but excesses of the same kind of diet are a reflection on obesity.
My guess is that calorie-per-gram-per-dollar of carbohydrates is still lower than fat and protein.
That said, the post is still valuable and would work much better with a framing closer to "some analogies between statistical analysis and public policy" -- the rest of the post (all the political recommendations) is honestly really solid, even if I don't see a lot of the particular examples' connections to their analogous ML approaches. The creativity is impressive, and overall I think it's a productive, thought-provoking exercise. Thanks for posting OP!
Now, for any fellow pendants, the philosophical critique:
The bad part of standardized testing isn't at all that it's "too efficient", it's that it doesn't measure all the educational outcomes we desire. That's just regular ol' flawed metrics. Again, overfitting isn't an example of a model being too efficacious, much less too efficient (which IMO is, in technical contexts, a measure of speed/resource consumption and not related to accuracy in the first place).Overfitting on your dataset just means that you built a (virtual/non-actual) model that doesn't express the underlying (virtual) pattern you're concerned with, but rather a subset of that pattern. That's not even a problem necessarily, if you know what subset you've expressed -- words like "under"/"too close" come into play when it's a random or otherwise meaningless subset.
I'd say that both the training and test sets are actualized expressions of your targeted virtual pattern. 100% training accuracy means little if it breaks in online, real-world use. I'd take this as proof that what we're really talking about here is efficacy, not efficiency. This is cute and much better than the opening/title, but my critique above tells me that this is just a wordy rephrasing of "different things have differences". That certainly backs up their claim that the proposed law is universal, at least!The author seems to be discussing optimizing for the wrong metric. That's not a problem of too much efficiency.
Excessive efficiency problems are different. They come from optimizing real output at the expense of robustness. Just-in-time systems have that flaw. Price/performance is great until there's some disruption, then it's terrible for a while.
Overfitting is another real problem, but again, a different one. Overfitting is when you try to model something with too complex a model and and up just encoding the original data in the model, which then has no predictive power.
Optimizing for the wrong metric, and what do about it, is an important issue. This note calls out that problem but then goes off in another direction.
All metrics are wrong, some metrics are useful. Finding the useful one and then recognising when it ceases to become useful is the hard problem.
If we squint a little, focus on close/far-away instead of same/distinct and s/metric/model/g (because usage of a metric implies a model), we can see how close these things can be.
Optimizing for the wrong metric - becomes “using a wrong model”.
Excessive efficiency - is partially “using a wrong model”, or maybe “good model != perfect model”. We start with good enough model, but after certain threshold we get to experience the difference between “good enough” and “perfect” (aparantly we care about redundancy, but it was not part of our model; so we were using a wrong model)
Overfitting is “finding the wrong model” (I wanted a model for the whole population, got a model only for a sample)
..or if we squint even more and go meta.. overfitting is part of “good model != perfect (meta)model” of modeling. (using sample data is good enough, but not perfect)
P.S. I liked the article. Choice of the title - not so much.
P.P.S. Simplicity of a model is part of meta-model.
For example, you can assign priorities to the loads on your systems, so that you can shed lower priority loads to create some slack for emergencies, without having to run your system idle under during lulls.
I get what the article is trying to say, but they shouldn't write off optimisation as easily as that.
So you’re fixing the micro economics of the queue but not the macro. Queues still suck when they fill up, even if they fill with last minute jobs.
Eg if you are running video conferencing software, and all of a sudden you are having bandwidth problems, you typically first want to drop some finer details in the video, and then you want to drop the audio feed.
In any case, if you dropped something, you leave it dropped, instead of picking it back up again a few seconds later. People don't care about past frames.
(However, queuing instead of outright dropping can still makes sense in this scenario, for any information that's younger than what human reaction times can perceive.)
Similarly in your scenario, you'd want to explicitly communicate to people what the expectations are. Perhaps you give out deep discounts for tasks that can be dropped (that's what eg some electriticy providers do), or you can give people 'insurance' where they get some monetary compensation if their task gets dropped. (You'd want to be careful how you design such a scheme, to avoid perverse incentives. But it's all doable.)
> So you’re fixing the micro economics of the queue but not the macro. Queues still suck when they fill up, even if they fill with last minute jobs.
I don't know, I had pretty positive experiences so far when eg I got bumped off a flight due to overbooking. The airline offered decent compensation.
Overbooking and bumping people off _improves_ the macro situation: despite the occasional compensation you have to pay, when unexpectedly everyone who booked actually showed up, overbooking still makes the airline extra money, and via competition this is transformed into lower ticket prices. Many people love lower airfares, and have shown a strong revealed preference of putting up with a lot of stuff eg RyanAir pulls as long as they get cheap tickets.
Eg at Google (this was ten years ago or so), we could always spend leftover networking capacity on syncing a tiny bit faster and more often between our data centres. And that would improve users' experience slightly, but it also not something that builds up a backlog.
At a factory, you could always have some idle workers swipe the floor a bit more often. (Just a silly example, but there are probably some tasks like that?)
If she had 0, she ran the risk of turning customers away and losing money. Any more than 1 is excess waste. Having just 1 meant she’d served every possible customer and only “wasted” 1 slice.
Yes, it’s inefficient. Yes, some people want that!
"I don't care it the climate's fucked, I want to live away from civilization and drive 100 miles a day everywhere"
Of course we shouldn't hyper-optimize everything, but sooner people realize our environment depends on not everyone getting exactly what they want whenever they want the better. Living in a (walkable) city is just one such concession towards the environment we ought to make, even if we don't "want" to.
Just whack an externality tax on fossil fuels and things like cutting down wilderness, job done.
Also, you have the mindset of a typical anti-social coastal elite who thinks “oh no big deal we can just raise the cost of living for all the poor rural types by sticking on a tax because I want to go LARP as a Victorian manor lord. And people don’t bend to my every whim immediately or live exactly like me so I want to be in total control of the 50 miles around me.”
All I'm saying is that the efficiency arguments are silly unless you are comparing like for like. If we're suggesting that people simply do less because it's more efficient, well, no-one is going to do that without an incentive.
Everyone having 50 sqmi obviously isn't realistic (there actually is not enough space on the globe), but equally, if the idea is that everyone _has_ to live in a metropolitan apartment because each person has to use (1/7billion) of the resources, you're going to see an uprising, that just won't fly with people.
The best outcome is probably to convince as many people as possible to live in a shoebox so that the rest of us can still have a decent life. It seems to be working!
https://usa.streetsblog.org/2015/03/05/sprawl-costs-the-publ...
https://news.berkeley.edu/2014/01/06/suburban-sprawl-cancels...
So while what you're saying is true, it doesn't disprove anything. LA is much less dense and therefore has much less "stuff" available for its inhabitants. But it's still more than a rural area.
Besides, angels can't really balance on pinheads.
I don't know what you mean by reverse.
There’s no room to absorb shocks. We saw a drastic version of this during COVID-19 induced supply chain collapse. Car manufacturers had built near 100% just in time manufacturing that they couldn’t absorb chip shortages and it took them years to get back up.
It also leaves no room for experimentation. Whatever experiment can only happen outside a system not from within it.
There is an odd corollary, which is that capitalistic systems which reward efficiency gains and put downward pressure to incentivize efficiency, deal with the resilience problem by creating entirely new subsystems rather than having more robust subsystems, which is fundamentally inefficient.
Is what you’re saying that capitalism breaks down resilience problems into efficiency problems?
I think that’s an extremely motivating line of thinking, but I’ll have to do some head scratching to figure out exactly what to make of it. On one hand, I think capitalism is really good at resilience problems (efficient markets breed resilience, there’s always an incentive to solve a market inefficiency), on the other (or perhaps in light of that) I’m not so sure those two concepts are so dialectically opposed
The fundamental tension arises because risk mitigation increases input costs. Over a given time horizon, there is an optimal amount of risk mitigation that will result in maximum aggregate profit (output minus input, not necessarily monetary). The longer the time horizon, the more additional risk mitigation is required, to prevent things like ruin risk.
But here’s the rub: competition reduces the time horizon to “very very short” because it drives down the output value. So in a highly competitive market, we see companies ignore resiliency (they cannot afford to invest in it) and instead they get lucky until they don’t (another force at work here is lack of skin in the game). The market deals with this by replacing them with another firm that has not yet been subject to the ruinous risks of the previous firm. This cycle repeats again and again.
Most resilient firms have some amount of monopolistic stickiness that allows them to invest more in resiliency, but it is also easy to look at those firms and see they are highly inefficient.
The point is that the cycle of firms has a cost, and it is not a trivial one: capital gets reallocated, businesses as legal entities are created, sold, and destroyed, contracts have to be figured out again, supply chains are disrupted, etc. Often, the most efficient outcome for the system is if the firms had been more resilient.
So there is an inefficient Nash equilibrium present in those sort of competitive markets.
1. Firms compete
2. Firms either increase their efficiency or die
3. Efficient firms are more susceptible to shocks
4. Firm shutdown and closures are themselves shocks
5. Eventually the system reaches a critical point where the aggregate susceptibility is higher than the aggregate of shocks that will be generated by shutdowns and closures
6. Any external shock will cause a cascade
There's essentially a "commons" where firms trade susceptibility for efficiency. Or in other words, susceptibility is pooled while the rewards for efficiency are separate.
A species will specialise for a niche, and outcompete a generalist. But when conditions change, the generalist can adapt and the specialist suffers.
1. Firms compete
2. Some firms get ahead
3. Accrued advantages to being ahead amplify
4. A small number of firms dominate
5. New competition is bought or crushed
6. Dominate firms become less efficient in competition-free environment
Something you personally (in your head) believe to be a general law, or rule, or truth (canon). It's roughly synonymous with "mental model".
A cannon is a weapon.
"Ahh, if only I hyperoptimize all aspects of my existence, then I will achieve inner peace. I just need to be more efficient with my time and goals. Just one more meditation. One more gratitude exercise. If only I could be consistent with my habits, then I would be happy."
I've come to see these things as a hindrance to true emotional processing, which is what I think many of us actually need. Or at least it's what I need - maybe I'm just projecting onto everyone else.
Hell, even this settling for happiness as a side-product is a result of the judgement that this is the best we can do regarding the goal of happiness.
The trick is as always to find out the XY problem. What they really need may be way easier for you to implement than what they actually asked for.
If you are in the business of selling any product or service, then it's great that finding a way to make it cheaper also generates more demand for you.
It's pretty normal that people want less of stuff when it's expensive, and more when it's cheap.
But (in our example) the high price of a fridge has so far kept you from buying one. Luckily, prices have recently been falling, so you can finally afford one.
You look forward to a new era of less spoilage and less food waste in your life; because you'd rather use less resources. (The electricity to be burned by the new fridge comes from your rooftop photovoltaic, of course.)
---
Does this make sense? You need have no regard for anyone's quarterly sales reports.
I don’t know if this phenomenon is aptly characterized as “too much efficiency”.
Invented the first generative diffusion model in 2015. https://arxiv.org/abs/1503.03585
Among his notable accomplishments, he and coauthors mathematically characterized the propagation of signals through deep neural networks via techniques from physics and statistics (mean field and free probability theory). Leading to arguably some of the most profound yet under-appreciated theoretical and experimental results in ML in the past decade. For example see “dynamical isometry” [1] and the evolution of those ideas which were instrumental in achieving convergence in very deep transformer models [2].
After reading this post and the examples given, in my eyes there is no question that this guy has an extraordinary intuition for optimization, spanning beyond the boundaries of ML and across the fabric of modern society.
We ought to recognize his technical background and raise this discussion above quibbles about semantics and definitions.
Let’s address the heart of his message, the very human and empathetic call to action that stands in the shadow of rapid technological progress:
> If you are a scientist looking for research ideas which are pro-social, and have the potential to create a whole new field, you should consider building formal (mathematical) bridges between results on overfitting in machine learning, and problems in economics, political science, management science, operations research, and elsewhere.
[1] Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
http://proceedings.mlr.press/v80/xiao18a/xiao18a.pdf
[2] ReZero is All You Need: Fast Convergence at Large Depth
https://arxiv.org/pdf/2003.04887
Translation to laymen: ML is being analogized to the mathematical structure of signaling between entities and institutions in society.
Mathematician proposes problem that plagues one (overfitting in ML, the phenomena by which a neural network's ability to generalize is negatively impacted by overtraining so the functions it can emulate are tightly coupled to the training data), must plague the other.
In short, there must be a breakdown point at which overdevelopment of societal systems or signaling between them makes things simply worse.
I personally think all one need do is look at what would happen if every system were perfectly complied with to see we may already be well beyond that breakpoint in several industrial verticals.
With the idea that there is some subset of logic that sits below economics that is provable and exact. That is a powerful idea worth pursuing!
It's certainly an interesting perspective on the development of complex systems. The idea that an economy can be somehow overfitted to its own incentives and constraints I don't think is entirely new, cf the Beer Game. But as a general concept, it's certainly not something that usually finds its way into policy discussion, beyond some very specific talk about reshoring of certain critical industries.
However, I think the most important benefit of this perspective is going to be providing yet another counterargument against the Austrian economics death cult.
There was also something about lower state expenditures (...taxes...) giving better results for the people - that's the one that seems to be very popular with rich people for some reason. Go figure.
It seems a lot like Scientology: the more you learn about it, the more bizarre it gets. And of course it's used to extract a lot of money for few benefactors.
https://books.google.co.uk/books/about/Tracts_N_50_Antidote_...
Highly optimized systems take full advantage of their environment and rely on a high degree of predictability in order to avoid redundant operations.
These systems minimize the free energy in the system, and so very little free energy is available to counteract new forces introduced to the environment which act on the system.
You'll find parallels in countless domains, since the very basis for learning and stabilization of a system revolves around becoming more or less sensitive to a given stimulus. Examples could be attention, supply chain economics, institutions, etc.
The only solution I see is for the FDA to include supply reliability in it's determination of whether a system is acceptable.
This is something that Nassim Taleb and the people working on https://realworldrisk.com/ have been saying for decades already.
Maximizing efficiency in the short-term is not the same as maximizing survival in the long term.
[0] https://www.sciencedaily.com/releases/2017/09/170908205356.h...
Why do you think this?
As for society itself being robust, it’s a much harder property. Being robust is nice but no one actually wants to live in a metered society where there’s insufficient resources - they’d generally rather kill for resources greedily and let others fail without helping them. That’s why socialized healthcare struggles - while it guarantees a minimum of care for everybody, the care provided has longer wait times and most people are not willing to wait their turn.
(This is quite unlike the common view that businesses inevitably grow to take over the world.)
I.e. business is much like a living organism.
Problems set in when the government bails out failing businesses.
Even worse are government "businesses". They are not allowed to fail, and the inefficiencies, parasites, corruption, grow and grow. When can you remember a government agency being abolished? Eventually, the government will collapse.
In the UK the last I specifically remember is DFID, which shut down in 2020.
In Commonwealth countries and the UK itself there are plenty of businesses called “crown corporations” which are owned by the government. Change in attitudes towards more liberalism led governments to deregulation and selling off bits and pieces or the entire corporation. Here are some Canadian examples:
https://www.cbc.ca/news/politics/canada-post-it-innovapost-s...
https://policyoptions.irpp.org/magazines/march-2024/mulroney...
America is a relatively young country and has very peculiar philosophies sometimes not found in the rest of the world. Be very careful extrapolating an American perspective abroad or as capturing some elemental truth of the universe.
I'm thinking of a reasonably recent article I saw that was talking about helping people navigate the 30+ assistance programs they might be eligible for. There's your problem right there--there should not be 30+ programs doing approximately the same thing! That's an awful lot of duplication of effort.
Or look at what happens with business licenses. Two things I see:
1) They want their $ from entities that shouldn't really be "businesses" in the first place. Around here an awful lot of licensed professionals have to have a "business" license--never mind that the nature of their work means they're inside some other entity that actually is reasonable to license. And that means a sales tax registration which has an annual minimum that such people almost certainly will never reach. (Sales tax includes use tax--but it's their office that actually engages in such transactions.)
2) Businesses that perform their work on-site have to have business licenses for every license area of the metropolitan area they work in. Hey, guys, get together and define the superset of the rules of your area and allow someone to get a license that covers the whole area based on that superset.
The Republicans are "right" in that we have far too many regulations. But they are very wrong in wanting to take an axe to them--most of the rules are individually sensible (and when they produce nonsense it's often situations where it's not worthwhile to special case), there is a horrible problem of duplication of effort and fingers in the pie. It's not chopping that's needed, it's organization.
Healthcare is more complicated. It can never work as an efficient free market since nobody goes comparison shopping for the hospital with the best value-for-money when they have a car crash. That's why socialized healthcare achieves much better results per dollar spent. But it's often hamstrung by attempts at efficiency.
I think a better societal example is disaster relief: helping people back up after they have been hit by a hurricane is the humane thing to do, but how much is that encouraging people to settle in high risk areas with insufficient precautions?
In fact I think a pretty small fraction of patients arrive at the ER unconscious.
I expect the objections are in how quality is measured and enforced.
It reminds me of education system in the US - most people (project 2025 aside) think it's good to have a public education system; having a pipeline of skilled workers makes it easier to build an economy filled with a diverse set of businesses.
However, the attacks start to fly when there is disagreement about who should be allowed to teach, how they should be measured, and how they should be paid.
I think it is fine to have some preferences for a hospital, but not sure how much benefit that confers outside of some narrow situations.
I think the complexities of modern societies make it too difficult to measure this risk adequately. We just don’t have the bandwidth to think about the second-and-third order effects for every social/financial interaction we encounter. And people are generally very poor at estimating high-consequence/low-probability events. This means people will often take very outsized risks without realizing it; when bad things happen it creates an unstable society. I don’t think we’ve evolved to personally manage all the c risks in a large complex society and farming those risks out to institutions seems to be the current way most societies have decided to mitigate those risks.
Unfortunately, those institutions --be they governments, insurance companies, UL Labs, banks, venture capitalists, etc.--also need to be vetted.
Even when staffed with impeccably well credentialed and otherwise highly capable people, their conclusions may be drawn using a different risk framework than your own.
The risk that they mitigate may even be the risk that you won't vote for them, give them money, etc.
There is also the risk of having too little risk, a catastrophe no worse than too much risk. The balloon may not pop, but it may never be filled.
I can see how you could arrive at similar conclusions from a risk management perspective, but it's not a morally just system. Within the system risk taking must be accepted.
“Too big to fail” is a meme that only applied to a tiny handful of companies during the financial crisis. Take a look at SVB for how fast a stalwart huge bank can implode with zero fucks given by the government.
What you’re thinking of is FDIC which is completely the opposite of a bailout for the bank. It’s a bailout for depositors (a huge portion of which were normal people). Arguments for the FDIC protecting people from keeping money in bad banks is a different argument, but it most certainly isn’t a bailout.
If you think going bankrupt and the FDIC seizing your company and wiping out shareholders is a bailout, you don’t know what a bailout is at all. That’s standard bankruptcy with an extra heavy boot on the throat from the government because they are ruthless about maintaining consumer confidence in the banking system.
My idea on working around this: for any business with actively traded stock there is a salary cap, say $1m/yr *per year*. You want to pay that guy $10m/yr? No, you pay him $1m and he gets 9 sets of shares that are worth $1m now, but they will be delivered one a year. Next year, same thing, you give him $1m, one set of shares from the previous year is delivered to him, he's got 9 new sets coming. So long as you have such shares forthcoming you are not permitted to engage in any trade where you would gain from the stock going down. If you do so inadvertently (say, investing in a fund that shorts the stock) any income from that is taxed at 100%.
The idea is to make your top people care about the long term prospects of the company, not merely the prospects of their area for whatever time they're in charge of it.
Socialized healthcare has its advantages and is probably more cost effective on average. But we also see affluent Canadians coming to the USA as medical tourists and paying cash for MRI scans in order to avoid the queues back home.
Patients have the time but rarely have the actual ability to shop around outside asking "is this provider in my coverage plan?" They demand me to sign a document stating I'm willing and able to pay while often never being able to actually tell me what the procedure will actually cost. Often, they won't even know that same day the procedure is done, it'll be weeks before I'm actually invoiced. And don't even get me started when you've chosen the surgeon in your plan, the facility in your plan but it turns out the anesthesiologist they scheduled wasn't in your plan. Oops. That's an expensive mistake you made, should have shopped around!
My knee kept locking up and I'd experience tremendous pain. Only once every few weeks though, so I had time to "shop around". I called up several places and tried to get an estimate of what it would cost ahead of actually seeing the doctor. Nobody would actually offer that, they could only make an appointment to see the doctor. No idea what the doctor would actually want to do during that appointment, so who knows what things will cost. Will they want x-rays? Will they want an MRI? Can they do the MRI there? Won't know until you commit to paying!
And out of the few dozen choices of kinesiologists around me which were covered by my insurance few had any appointments available within the next several weeks. Many weren't seeing new patients. So really it was deal with my knee randomly causing me immense pain for several more months or take whoever had the first appointment. And this is in one of the top five largest metro areas in the country, not some small town in the middle of nowhere.
Shopping for which hospital to do the delivery of my children, the estimates for our costs after insurance had a massive amount of uncertainty to the point of being useless. Could be $4k, could be $20k, who knows. Imagine going to a burger joint and the menu says a burger could be anywhere from $1 to $50, we'll invoice you in a month. Go down the street, menu says it could be $3 to $48, we'll invoice you in a few weeks. What an ability to shop around! Free market at work!
> avoid the queues back home.
I already mentioned, most kinesiologists around me were fully booked for months. Very few had anything within several weeks. That's queueing.
I tried to book an appointment with a new dermatologist a few months ago. Once again, in this very large metro area. For dermatologists in my area covered by my insurance, the earliest appointment was six months out. It took several months to get a family member's hip replacement scheduled. We have queues in this country as well.
Getting medical imaging is generally pretty quick and easy though, and places like MRI imaging centers just want to keep moving people through so if they have an empty spot in an afternoon having someone in the machine constantly is important. It's also generally the easiest thing to automate in healthcare; mostly just a matter of getting enough machines and lightly trained techs to rotate people through. Radiologists are often off-site contractors getting paid for every scan they review.
The No Surprises Act does give patients some protection against high charges for out-of-network services.
https://www.cms.gov/newsroom/fact-sheets/no-surprises-unders...
Ok, but then it goes back to this idea of queueing and the "free market". Ok, so it'll take me a few weeks to go to the first doctor just to get his paid estimate. If I don't like whatever estimate he gives (which once again he probably won't directly) what am I to do? Start calling around, make another appointment with someone else several weeks later and pay yet another appointment fee? Hope his prices are better? Rinse and repeat to get a few quotes?
And so continuing the burger example, you call ahead to book a time well in advance and pay $2 just to be able to look at the menu. What a free market.
One of my kids had to get tubes in his ears. I didn't actually get a good faith estimate of cost until that morning in the hospital despite calling several times. I had to schedule, wait weeks, show up early in the morning, and refuse to sign for the financial liability until I got an actual estimate. Free markets at work here guys, totally not a broken system, easily just shop around. And yeah, sure, I could have gone to see a different audiologist and then gone through scheduling it all again and waiting another few months and a couple hundred dollars. After all its just my kid's speech development, no big deal delaying that another month or two (or three).
At least it's good to see this No Surprises Bill solved at least that one example of the healthcare industry screwing people over. Thanks for sharing that.
Very long waiting times are the first thing that comes to mind regarding such failures, with UK and Canada at the top spot. It is not uncommon to die waiting for a consultation to be diagnosed in 1-2 years.
Edit: I would also add that it is probably better to look at health outcomes, e.g., survival rates for cancer.
Almost all EU countries with socialized healthcare beat US and even UK. Canada is more near EU level.
Do you also want US anedoctal evidence? What’s the consultation time for diagnose in the US for people without insurance/ insurance doesn’t cover/insurance says it doesn’t cover/no money?? Never? How does that affect the average/mean?
Life Expectancy:
Italy 83.7; Spain 83.7; France 83.3; Sweden 83.3; Canada 82.6; Portugal 82.4; Germany 81.4; United Kingdom 81.3; United States 79.3; Poland 78.6;
https://ourworldindata.org/grapher/life-expectancy-unwpp?tab...
Here in BC, we have (mandatory) auto insurance provided by a crown corporation with a monopoly. The neo-liberal government before the current more socialist leadership hated it and wanted desperately to privatize car insurance. The problem is that ICBC is dearly loved by most residents here. There is a playbook for this problem.
They appointed a fairly incompetent civil servant to run things, and also started raiding the fund, to the tune of billions of dollars.
After about a decade of this, the company was a mess and nearly broke. They were forced to raise rates. The premier characterized the situation as a "dumpster fire" and editorials started popping up arguing for privatization.
That government was defeated, and the new leadership sorted it all out. Within a couple of years, drivers in BC were getting cheques in the mail because ICBC was making too much profit.
There are powerful interests very interested in getting a piece of healthcare in Canada, and some of the shenanigans you see here smell a lot like a set-up to make things become broken enough that the voters will demand privatization.
Just for one example, in the last few years staffing agencies have been hiring away nurses by offering higher wages and then contracting them back to the health authorities at ~$130/hr. This has cost billions to taxpayers and lead to great resentment within the regular staff.
Some folks somewhere allowed this to happen. Why?
And the owners could have sold when the business was propped up by unknown fragility.
Human lives are too short for these kinds of feedback loops to be all that effective.
A major mechanical casualty beyond what the crew can repair usually means a tow to a shipyard. Flying more engineers in by helicopter would seldom help, and often isn't feasible.
Btw- reliability optimization doesn’t necessarily mean it is optimized to not fail. They are optimized to fail within some predetermined risk level. What that risk level should be is an entirely different discussion.
Plus, isn’t “physically impossible” a computer science argument, not a biological one? Unless we’re using the OG “physis”==“nature”, I guess
I know I'd tolerate a digital experience of far lower fidelity (fewer pixels, for instance, or even giving up GUIs altogether) if I could get it in a way that doesn't break every time some far away person farts near a cloud console: A trade of performance for robustness.
>capital concentration increases
>expectations for what capital owners can do with money increases
>expectations exceed available capital
>investment returns must increase (race to the top)
>cooperation among capital owners must increase to get better returns
>capital owning group begins to self-select and become less diverse, if this wasn't already caused by the background/personality required to accrue capital
>investment theory converges on a handful of "winning" ventures
>because this is where capital is flowing, workers are forced to divert to these ventures
>competition increases, hyperspecialization increases
>expertise in and sophistication of other areas begins to decline, causing quality decline, garnering less investment; feedback loop
-----
*debt cannibalizes future productivity
-----
)diversity in capital ownership and management increases likelihood of diversity in investment venture target
)increased competition, increased likelihood that ventures will cover needs, decreased likelihood of overweighting in one area/overproduction
)solution: capital redistribution. Perhaps globally
It's tempting to minimize waste, but excess capacity is required to adapt if things are evolving quickly.
It does both, eg. if the environment is stable then fitness is correlated with efficiency, if the environment is unstable then it's robustness.
Am I missing something?
http://proceedings.mlr.press/v37/sohl-dickstein15.pdf
The subtle difference between the two being exactly what the author describes: Goodhart's law states that metrics eventually don't work, Campbell's law states that, worse still, eventually they tend to backfire.
But you do not get good art by early stopping, you do not get it by injecting noise, you do not get it by regularization. All these do help and are essential to our modeling processes, but we are still quite far. We have better proxies than FID but they all have major problems and none even come close (even when combined).
We've gotten very good at AI art but we've still got a long way to go. Everyone can take a photo, but not everyone is a photographer and it takes great skill and expertise to take such masterpieces. Yet there are masters of the craft. Sure, AI might be better than you at art but that doesn't mean it's close to a master. As unintuitive as this sounds. This is because skill isn't linear. The details start to dominate as you become an expert. A few things might be necessary to be good, but a million things need be considered in mastery. Because mastery is the art of subtly. But this article, it sounds like everything is a nail. We don't have the methods yet and my fear is that we don't want to look (there are of course many pursuing this course, but it is very unpopular and not well received. Scale is all you need is quite exciting, but lacking sufficient complexity, which even Sutton admits to be necessary). It's my fear that we get too caught up in excitement that we become blind to our limitations. Because it's knowing those limitations that is what gives us direction to improve upon. When every critique is seen as spoiling the fun of the party, we'll never be able to have anything better. I'm not trying to stop the party, in fact, I'm worried it'll stop.
Evolution also picked it up as "satiation" - eating icecream feels good however you can't keep eating 1 per minute, same with pretty much everything.
In art it means not hijacking everything for some local maximum.
In the process, acting somewhat like a generalization of the problem it describes: overly precise and narrow approaches to "improve" ineffable qualities. But the author seems to understand that - he comments on the absurdity of some direct transfers of ML methods to real world problems. I think he just added a bunch of not necessarily well solvable, but particularly suffering from "overfitting", example problems. It's a food for thought article, not a grand proposal.
However: 1) This exact scenario will likely never materialize 2) You have not good quantification of the scenario anyway due to noise/biases in measurements.
So now you optimized for something very specific, and the nature throws you something slightly different and you are completely screwed because your optimized solve is not flexible at all.
That is why a more “suboptimal” approach is typically better and why our stupid brains outperform super fancy computers and algorithms in planning.