21 ms·

Too much efficiency makes everything worse (2022)

• futuramaconarma 2y ago
  Lol. Nothing to do with efficiency, just humans recognizing incentives and acting in self interest.
• curious-tech-12 2y ago
  perfect reminder that when you focus too hard on the proxy, you might win the battle and lose the war
  • HappMacDonald 2y ago
    Sounds like Goodhart's law: "When a measure becomes a target, it ceases to be a good measure"
• failrate 2y ago
  "If you do not build the slack into the system, the system will take the slack out of you."
• rowanG077 2y ago
  I don't think it's unintuitive at all. 100% optimized means 100% without slack. No slack means any hitch at all will destroy you.
  • fallous 2y ago
    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.
    • HappMacDonald 2y ago
      I have heard this same criticism leveled at global supply chains as of the supply shocks of the early 2020s such as COVID, Ever Given, etc.
      • fallous 2y ago
        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.
    • femto 2y ago
      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.)
      • fallous 2y ago
        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."
  • hinkley 2y ago
    “Hidebound”
  • refurb 2y ago
    That would assume your only target measure is efficiency, which would be a silly think to target in exclusivity of everything else.
• hedora 2y ago
  I don’t think the author understands what efficiency measures. All of the examples involve a bad proxy metric, or the flawed assumption that spending less improves the ratio of price to performance.
  • feyman_r 2y ago
    My take was that initially the metric is appropriate, but then with overfitting, it’s not enough. It eventually becomes a bad proxy metric.
  • brilee 2y ago
    Accusing these examples of involving "bad proxy metric" is identical to the no true scotsman fallacy.
  • atoav 2y ago
    > [..] 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.
• dawnofdusk 2y ago
  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?
  • feyman_r 2y ago
    The blog is mainly about ML - I don’t think the author alluded to overfitting having originated in that space; they just said it’s used extensively.
  • HappMacDonald 2y ago
    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.
    • dawnofdusk 2y ago
      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?
  • chefandy 2y ago
    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.
• raister 2y ago
  This reminds me of Eli Goldratt's quote: "Tell me how you measure me, I will tell you how I behave."
  • whack 2y ago
    Corollary: "If you do not measure me, I will not behave"
    • eventuallylive 2y ago
      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."
      • lotsofpulp 2y ago
        Is a contrapositive a corollary? P implies Q is logically equivalent to Not Q implies Not P. 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).
        • moefh 2y ago
          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.
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• dooglius 2y ago
  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.
  • feyman_r 2y ago
    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.
  • dangitman 2y ago
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• bbor 2y ago
  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!
• Animats 2y ago
  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.
  • satyanash 2y ago
    > Optimising for the wrong metric, and what do about it, is an important issue. 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.
  • stoperaticless 2y ago
    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.
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• remram 2y ago
  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.
  • eru 2y ago
    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.
    • hinkley 2y ago
      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.
      • eru 2y ago
        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.
• eru 2y ago
  Just add some measure of robustness to your optimization criterion. That includes having some slack for unforeseen circumstances.
  • paganel 2y ago
    And then you optimize around the slack, and we're back to step 1.
    • eru 2y ago
      The slack is part of your optimisation criteria.
  • sahmeepee 2y ago
    I thought that was the purpose of adding noise (in the mitigations).
    • eru 2y ago
      Noise is one possible way, but not the only one.
• Trasmatta 2y ago
  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.
  • nradov 2y ago
    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.
    • Jerrrrrrry 2y ago
      Everyone wants to be happy, and we can't all be right, right?
    • tananan 2y ago
      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.
• shahules 2y ago
  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.
• usaphp 2y ago
  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...
  • hinkley 2y ago
    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.
• projektfu 2y ago
  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.
  • hinkley 2y ago
    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.
    • eru 2y ago
      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.
      • hinkley 2y ago
        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.
        • eru 2y ago
          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.
          • its_bbq 2y ago
• ocean_moist 2y ago
  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”.
• LarsDu88 2y ago
  I was trying to remember where I remember where I heard of this author's name before. Invented the first generative diffusion model in 2015. https://arxiv.org/abs/1503.03585 https://arxiv.org/abs/1503.03585
  • Arech 2y ago
    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 https://arxiv.org/abs/1909.04240 Super interesting approach and very well written paper.
• natmaka 2y ago
  https://en.wikipedia.org/wiki/Diminishing_returns https://en.wikipedia.org/wiki/Diminishing_returns
• refibrillator 2y ago
  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 http://proceedings.mlr.press/v80/xiao18a/xiao18a.pdf 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 https://arxiv.org/pdf/2003.04887
  • throw10920 2y ago
    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.
  • LarsDu88 2y ago
    Adding to my reading list!
  • salawat 2y ago
    >> 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.
• tpoacher 2y ago
  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.
• godelski 2y ago
  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.
  • mirekrusin 2y ago
    I think he agrees more with you than you think. 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.
    • godelski 2y ago
      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.
• whatever1 2y ago
  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.
• knodi 2y ago
  Also it leads to a rigid system that is inflexible to deal with unknowns.
• alexashka 2y ago
  Does being a super efficient AI researcher make everything worse?
• t_mann 2y ago
  The argument rides on the well-known Goodhart's law (when a measure becomes a target, it ceases to be a good measure). However, it only puts it down to measurement problems, as in, we can't measure the things we really care about, so we optimize some proxies. That, in my view, is a far too reductionist view of the problem. The problem isn't just about measurement, it's about human behavior. Unlike particles, humans will actively seek to exploit any control system you've set up. This problem goes much deeper than just not being able to measure "peace, love, puppies" well. There's a similar adage called Campbell's law [0] that I think captures this better than the classic formulation of Goodhart's law: The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor. The mitigants proposed (regularization, early stopping) address this indirectly at best and at worst may introduce new quirks that can be exploited through undesired behavior. [0] https://en.wikipedia.org/wiki/Campbell%27s_law https://en.wikipedia.org/wiki/Campbell%27s_law
  • netcan 2y ago
    This is true, these "laws" are approximations and imperfect reductions. Which one is useful or descriptive will depend on the specific example. Optimizing ML VS Optimizing a social media algorithm VS using standardized testing to optimize education systems. There is no perfect abstraction that applies to these different scenarios precisely. We don't need that precision. We just need the subsequent intuition about where these things will go wrong.
    • onethought 2y ago
      I missed the citation on his education point. Has someone proved that “teaching to the test” leads to lower educational outcomes than not having tests?
      • ismailmaj 2y ago
        I saw some professors share the least about their tests to make sure we truly understand the material, sounds to me like a real-life usage of a train/test split. It’s not far fetched to think they employed this technique because teaching to the test didn’t work well by itself.
• bdjsiqoocwk 2y ago
  In practice this is just Goodhart's law itself. It's not distinct. In Goodhart's law > when a measure becomes a target, it ceases to be a good measure If you ask someone "could you give me an example" you will see that in the example the measure that becomes a target is already a proxy. Even the example that the author presents, the good that cares a lot about testing its students... How does the school test its students? With exams. But that's already a proxy for testing students knowledge... But overall excellent article.
• kazinator 2y ago
  Goal: efficient applications Proxy: minimizing execution time of hot loops Strong version Goodhart's: applications get incredibly bloated and unresponsive
• smokel 2y ago
  I was listening to an episode of the "inControl" podcast [1], in which Ben Recht suggested that overfitting is not always well understood. Perhaps it is interesting to read his blogpost "Machine Learning has a validity problem" alongside this article. [1] https://www.incontrolpodcast.com/ https://www.incontrolpodcast.com/ [2] https://archives.argmin.net/2022/03/15/external-validity/ https://archives.argmin.net/2022/03/15/external-validity/
• boredhedgehog 2y ago
  If a citizen recognizes or intuits this to be a deep-seated problem of the political process, and if the only concrete influence this citizen can exert on the political process is choosing one of several proposed representatives, it seems rational to choose the most irrational, volatile, chaotic and unpredictable candidate. The ideal choice would be a random number generator, but lacking that, he would want to inject the greatest dose of entropy available into the system.
• abernard1 2y ago
  The author identifies problems with a system measuring targets, but then all the proposals are about increasing the power and control of the system. Perhaps the answer—as hippy sounding as it is—is to reduce the control of the system outright. Instead of adding more measures, more controls, which are susceptible to the prejudices of control, we let the system fall where it may. This, to me, is a classic post of an academic understanding the failures of a system (and people like themselves in control of said system) but then not allowing the mitigation mechanisms of alternate systems to take its place. This is one of the reasons I come to HN: to view the prime instigators of big-M Modern failure and their inability to recognize their contributions to that problem.
  • CatWChainsaw 2y ago
    Loosening of control is exactly the answer, but the world pathology is currently that money/power/control are all unalloyed virtues to be pursued at any cost, so we'll have to wait for a global implosion before any of the Certified Geniuses on this site, or anywhere else, consider an alternative approach.
  • montefischer 2y ago
    For an example of this, I recall reading a proposal that acts of Congress be strictly limited to be at most (say) 5 pages in length. This would be a natural form of regularization of the legislative power.
• hcfman 2y ago
  Well the use of the phrase “too much” already implies less than optimal. A self fulfilling prophesy by definition ?
• RadiozRadioz 2y ago
  This is more a meta comment about the blog itself (as is customary for HN): I like the blog, there has been a lot of work put into it, so it makes me sad that it's hosted on GitHub pages using a subdomain of GitHub.io. When the day comes that GitHub inevitably kills/ruins Pages, because it _will_ happen, there is no question, the links to this blog will be stuck forever pointing to this dead subdomain that the author has no control over. We just have to hope that the replacement blog is findable via search engines, and hope that comments are enabled wherever the pages link is referenced so that new people can find the blog. An unfortunate mess that is definitely going to happen, entirely Microsoft's fault.
  • mdp2021 2y ago
    Strengthening the importance of the Archive (the Wayback).
  • 0x1ceb00da 2y ago
    If he hosts his site on his own server, it will go down a few months after his death.
    • RadiozRadioz 2y ago
      Assuming he's not 70, I guarantee GitHub pages will turn to shit before he dies. But also fair point. I think we should all have a contingency plan I'm case of death, regardless of where our stuff is hosted. Self-hosted stuff indeed becomes a ticking time bomb after death. Even on 3rd party services, it's apparently a nightmare trying to get access to a deceased person's Google account, where Google Sites may live, etc.
• deleted 2y ago
  [deleted]
• zmmmmm 2y ago
  Maybe I'm misunderstanding this but this doesn't seem like an accurate explanation of overfitting: > In machine learning (ML), overfitting is a pervasive phenomenon. We want to train an ML model to achieve some goal. We can't directly fit the model to the goal, so we instead train the model using some proxy which is similar to the goal One of the pernicious aspects of overfitting is it occurs even if you can perfectly represent your goal via a training metric. In fact it's even worse simetimes as an incorrect training metric can indirectly help regularise the outcome.
  • practal 2y ago
    You might be misunderstanding here what the "goal" is. Your training metric is just another approximation of the goal, and it is almost never perfect. If it is perfect, you cannot overfit, by definition.
  • timClicks 2y ago
    No, because your training data is only an appropriation of the actual workload.
• Angostura 2y ago
  Really interesting article. Got me pondering the extent to which the peacock’s tail is an example of overfitting and Goodhart's The female peacock is using the make peacock’s tail as a proxy for fitness - with beautiful consequences, but the males with the largest, showiest tails are clearly less fit
  • RandomLensman 2y ago
    There is research on costly signalling and evolution.
• Angostura 2y ago
  Really interesting article. Got me pondering the extent to which the peacock’s tail is an example of overfitting and Goodhart's The female peacock is using the make peacock’s tail as a proxy for fitness - with beautiful consequences, but the males with the largest, showiest tails are clearly less fit, and more prone to predation.
  • llm_trw 2y ago
    That's the point. That they are alive shows that their innate fitness minus the tail is higher than that of another alive peacock who doesn't have a tail. Or put another way: someone who wins the Olympic 100m sprint while hopping on one leg is a better runner that everyone else in the race by a wide margin.
• inglor_cz 2y ago
  When it comes to his Mitigation: Inject noise into the system. proposal: I would be happy to experiment with some sortition in our political systems. Citizens' assemblies et cetera. Randomly chosen deliberative bodies could keep some of the stupid polarization in check, especially if your chances to be chosen twice into the same body are infinetesimal. https://en.wikipedia.org/wiki/Sortition https://en.wikipedia.org/wiki/Sortition We tend to consider "democracy" as fundamentally equivalent to "free and fair elections", but sortition would be another democratic mechanism that could complement our voting systems. Arguably more democratic, as you need money and a support structure to have a chance to win an election.
• ansc 2y ago
  Kinda surprised to not see anyone mention Jacques Ellul and his The Technological Society which highly revolve around this. Technological here does not refer to technology.
  • montefischer 2y ago
    What do you think about Ellul's arguments in that book?
• naitgacem 2y ago
  Upon reading the title at first glance, I thought this was going to be how "effecient" computers nowadays. Such as MacBooks and such, who started this efficient computers thing in the recent times. And they are, but as a result computers are all worse off for it. I mean soldered RAMs and everything is a system on a chip.
  • throwuxiytayq 2y ago
    The existence of the Framework Laptop proves that this is largely an imaginary tradeoff, or at least one badly taken by Apple.
    • naitgacem 2y ago
      Unfortunately once one company does something and gets away with it, or makes even more money, everyone will follow. I was looking at Thinkpads and was somewhat shocked to see they started doing that too!
      • CooCooCaCha 2y ago
        This is also why the libertarian solution to bad work environments “just leave” doesn’t work at scale.
  • appendix-rock 2y ago
    That wouldn’t be worthy of the front page of HN. “I don’t like the current tradeoffs that laptop manufacturers make” has been talked through to absolute death. It’s the opposite of interesting.
    • throwuxiytayq 2y ago
      If anything, there should be more space for this topic in our collective consciousness.
• mooktakim 2y ago
  Teaching is a terrible example. Teaching is actually more efficient when it is decentralised as the teachers can adapt to local environment and changes. With centralisation you have bad feedback loop.
• RandomLensman 2y ago
  Efficiency is usually easier than effectiveness so it is optimised for much more and that spills over in the results and outcomes, of course.
• nobrains 2y ago
  It would be nice, if us on HN, can crowdsource, some good KPIs/Proxies for the goals mentioned in the article. These ones: Goal: Educate children well Proxy: Measure student and school performance on standardized tests Strong version of Goodhart's law leads to: Schools narrowly focus on teaching students to answer questions like those on the test, at the expense of the underlying skills the test is intended to measure --- Goal: Rapid progress in science Proxy: Pay researchers a cash bonus for every publication Strong version of Goodhart's law leads to: Publication of incorrect or incremental results, collusion between reviewers and authors, research paper mills --- Goal: A well-lived life Proxy: Maximize the reward pathway in the brain Strong version of Goodhart's law leads to: Substance addiction, gambling addiction, days lost to doomscrolling Twitter --- Goal: Healthy population Proxy: Access to nutrient-rich food Strong version of Goodhart's law leads to: Obesity epidemic --- Goal: Leaders that act in the best interests of the population Proxy: Leaders that have the most support in the population Strong version of Goodhart's law leads to: Leaders whose expertise and passions center narrowly around manipulating public opinion at the expense of social outcomes --- Goal: An informed, thoughtful, and involved populace Proxy: The ease with which people can share and find ideas Strong version of Goodhart's law leads to: Filter bubbles, conspiracy theories, parasitic memes, escalated tribalism --- Goal: Distribution of labor and resources based upon the needs of society Proxy: Capitalism Strong version of Goodhart's law leads to: Massive wealth disparities (with incomes ranging from hundreds of dollars per year to hundreds of dollars per second), with more than a billion people living in poverty --- I will start: Goal: Leaders that act in the best interests of the population Good proxy: Mandate that local leaders can only send their kids to the schools in their precinct. They can only take their families to the hospitals in their precincts.
• gond 2y ago
  “Though this pheonomenon is often discussed, it doesn't seem to be named. Let's call it the strong version of Goodhart's law“ I wonder why the author called it that way when this seems to me clearly derived from Ross Ashby‘s law of Requisite Variet[1], predating Goodhard by 20 years. As I see it, it is not even necessary to put more meaning it Goodhard as there actually is. Requisite Variety is sufficient. Going by his resume, I strongly assume the author knows this. Russel Ackoff, building on countless others, put into two sentences for which others needed two volumes: “The behaviour of a system is never equal to the behaviour of its parts. - It is the product of their interactions.“ [1] https://en.m.wikipedia.org/wiki/Variety_(cybernetics) https://en.m.wikipedia.org/wiki/Variety_(cybernetics)
  • appendix-rock 2y ago
    Love myself some cybernetics! All engineers are doing themselves a disservice by sitting here writing smooth-brained rants about “dumb MBAs making my job hard” instead of reading up on this field and understanding the true complexities that are inherent in people working together.
    • gond 2y ago
      Agreed. I wonder if this is still the aftermath of the chasm which resulted when ‘Marvin Minsky et al‚ disowned Cybernetics, took some parts out of it and gave it a shiny new name. Especially Systems Theory in its second manifestation (Maturana, Luhmann, von Förster, Glasersfeld - and Ackoff) is extremely powerful, deep and, reasons beyond me, totally overlooked. Have to say tho, most MBA‘s I encountered sadly never ever heard of Cybernetics or Systems Theory. :-(
      • pradn 2y ago
        How has Systems Theory changed how you think? Is there a good book you recommend on the topic? Thank you!
        • gond 2y ago
• lynguist 2y ago
  I would claim in a completely informal way that the optimal degree of utilization is ln(2)=0.693, around 70%. This stems from the optimal load of self-balancing trees. A little bit of slack is always useful to deal with the unforeseen. And even a lot of slack is useful (though not always as it is costly) as it enables to do things that a dedicated resource cannot do. On the other hand, no slack at all (so running at above 70%) makes a system inflexible and unresilient. I would argue for this in any circumstance, be it military, be it public transit, be it funding, be it allocation of resources for a particular task.
  • Tade0 2y ago
    I would put it at 1 - e^(-1) ~= 0.6321 As e^x is a commonly occurring curve and at that point its derivative goes below 1, meaning from that point on it's diminishing returns.
    • LUmBULtERA 2y ago
      I was thinking how this string of thought could connect to our daily lives. As a family with a toddler, if we fill up too much of our schedule/time in a day, a perturbation to the schedule can break everything. If instead we fill up 63-70% of the schedule and build in Flex Time, we're good!
  • pictureofabear 2y ago
    Where e^-X and 1-e^-X intersect is 0.69. Mumbo? perhaps. Jumbo? perhaps not.
• juanani 2y ago
  [dead]
• submeta 2y ago
  Remind‘s me of a quote from Donald Knuth: „Premature optimisation is the source of all evil.“
• skramzy 2y ago
  What gets measured gets optimized
• baq 2y ago
  See also antifragility: https://en.wikipedia.org/wiki/Antifragility https://en.wikipedia.org/wiki/Antifragility In short, efficiency is fragile. If you want your thing to be be stronger after a shock (instead of falling apart), you must design it to be antifragile. Note: it's hard to build antifragile physical things or software, but processes and organizations are easier. ML models can be antifragile if they're constantly updating.
  • throw_pm23 2y ago
    Yes, extending the idea more broadly to society, health, politics, etc. sounds pretty much was Taleb has been doing.
• js8 2y ago
  I have a pet theory that the state-planned economies failed not because they were inefficient (as neoclassical economics claims), but rather because they attempted to be too efficient. They tried to exactly calculate which producer needs what inputs, what they should produce and when, and a little deviation from the plan caused big cascading failures. Free market is actually less efficient than direct control, but it is correspondingly more robust. This is evidenced in the big companies, which also sometimes try to control things in the name of "efficiency" and end up being quite inefficient. And also small companies, which are often competing and duplicating efforts. The optimum (I hesitate it call that because it's not well-defined, it is in some sense a society's choice) seems to be somewhere in the middle - you need decent amount of central direction (almost all private companies have that) and redundancy (provided by investment funds on the free market). (As aside, despite me being democratic socialist, I don't believe the democracy matters that much for economic development, but is desirable from a moral perspective. You can have a lot of economic development under authoritarian rule, there are examples on both sides, as most private companies are also actually small authoritarian fiefdoms.)
  • mglz 2y ago
    > I have a pet theory that the state-planned economies failed not because they were inefficient Well, a lot of it was corruption. A sufficient level of corruption can destroy almost any system, even if it had a well-meaning leader at the top.
    • aebtebeten 2y ago
      That suggests a plausible mechanism for why the dissolution of the Union was so peaceful: faced with a call for glasnost' from Moscow, local leaders preferred to quietly leave? Do we have any reasonable datasets for before-and-after corruption levels in the FSU, or would that be a project which would* need another 3-5 decades to be viable? * in the absence of sufficient well-placed cabbage? EDIT: circumstantial, but chin-scratch-worthy: https://en.wikipedia.org/wiki/Glasnost#Opposition https://en.wikipedia.org/wiki/Glasnost#Opposition (romania's post-communist transition was exceptionally** violent) ** here I count the stans as having suffered from preexisting violence
• numbol 2y ago
  There is a book on this topic, "Why Greatness Cannot Be Planned" https://link.springer.com/book/10.1007/978-3-319-15524-1 https://link.springer.com/book/10.1007/978-3-319-15524-1 There are many youtube videos where Ken explain those ideas, this one for example https://www.youtube.com/watch?v=y2I4E_UINRo https://www.youtube.com/watch?v=y2I4E_UINRo
• AnimeLife 2y ago
  Very interesting article. I don't get though for hotspot partitions they didn't use a cache like Redis.
• tikkun 2y ago
  Makes me think of: some of Taleb's ideas about just-in-time manufacturing (no slack eg covid supply shocks) https://www.lesswrong.com/posts/yLLkWMDbC9ZNKbjDG/slack https://www.lesswrong.com/posts/yLLkWMDbC9ZNKbjDG/slack Also, can't recall it but a long time ago I read a piece about how scheduling a system to 60% of its max capacity is generally about right, to allow for expected but unexpected variations (also makes me think of the concept of stochastic process control and how we can figure out the level of expected unexpected variations, which could give us an even better sense of what %-of-capacity to run a system at)
• ponow 2y ago
  > Distribution of labor and resources based upon the needs of society Not a goal for me, and not for evolution. Survival, health, prosperity, thriving and complexity rate higher. Not everyone makes it.
  • immibis 2y ago
    Thank you for acknowledging that your goal is to kill people. Now that that has been acknowledged, we can all ignore whatever else you have to say.
• zug_zug 2y ago
  I like the connection between Goodheart's law and overfitting. However these examples are a reach: --- Goal: Healthy population Proxy: Access to nutrient-rich food Strong version of Goodhart's law leads to: Obesity epidemic I'm not sure I believe this one. Exactly who's target is "access to nutrient-rich food" and how would removing that target fix the US obesity epidemic? Is "nutrient-rich" a euphemism for high-calorie? My understanding is that there are plenty of places with high-nutrient food but different norms and much better health (e.g. Japan). We can and do measure population health across (including obesity), this isn't a proxy for an unmeasurable thing. --- Goal: Leaders that act in the best interests of the population Proxy: Leaders that have the most support in the population Strong version of Goodhart's law leads to: Leaders whose expertise and passions center narrowly around manipulating public opinion at the expense of social outcomes Is this really a case of "overfitting from too much data"? Or is this just a case of "some things are hard to predict?" Or even, "it's hard to give politicians incentives." It'd be interesting if we gave presidents huge prizes if the country was better 20 years after they left office. --- Goal: An informed, thoughtful, and involved populace Proxy: The ease with which people can share and find ideas Strong version of Goodhart's law leads to: Filter bubbles, conspiracy theories, parasitic memes, escalated tribalism Is "the goal" really a thoughtful populace? Because every individual's goal is pleasure, and the companies goals are selling ads. So I don't know who's working on that goal.
• teleforce 2y ago
  There is a big difference between efficiency and effectiveness, and all system should focus on the latter rather than the former whether it's AI based or not. There's a reason why the best-seller of self-help book for several decades now is the book by Stephen Covey entitled "The 7 Habits of Highly Effective People" not "Efficient People".
• o-o- 2y ago
  > FTA: This same counterintuitive relationship between efficiency and outcome occurs in machine learning. The "examples abound, in politics, economics, health, science, and many other fields" isn't a relationship between efficiency and outcome, but rather measuring and efficiency, or measuring and outcome. I think a better analogy is Heissenberg's uncertainty principle – the more you measure the more you (negatively) affect the environment you're measuring.
• netcan 2y ago
  Another example of this approximate law is in exercise physiology. To a normal person, there are a lot of good proxy indicators of fitness. You could train sprinting. You could hop up and down. Squat. Clean and jerk.. etc. Running faster,hopping higher, squatting heavier... all indicators of increasing fitness... and success of your fitness training. Two points: 1 - The more general your training methodology, the more meaningful the indicators. Ie, if your fitness measure is "can I push a car uphill," and your training method is sprinting and swimming... pushing a heavier car is a really strong indicator of success. If your training method is "practice pushing a car," then an equivalent improvement does not indicate equivalent improvement in fitness. 2- As an athlete (say clean and jerk) becomes more specialized... improvements in performance become less indicative of general fitness. Going from zero to "recreational weighlifter" involves getting generally stronger and musclier. Going from college to olympic level... that typically involves highly specialized fitness attributes that don't cross into other endeavors. Another metaphor might be "base vs peak" fitness, from sports. Accidentaly training for (unsustainable) peak performance is another over-optimization pitfall. It can happen when someone blindly follows "line go up." Illusary optimizations are actually just trapping you in a local maxima. I think there are a lot of analogies here to biology, but also ML optimization and social phenomenon.
  • bob1029 2y ago
    Clean & jerk is one of those movements that I would almost consider "complete". Especially if you are going to mix in variants of the squat. Not sure these are the best example. I don't know of anyone who can C&J more than their body weight for multiple repetitions who isn't also an absolute terminator at most other meaningful aspects of human fitness. Human body is one machine. Hormonal responses are global. Endurance/strength is a spectrum but the whole body goes along for the ride.
    • netcan 2y ago
      Perhaps. And you could probably test this but I would gamble that the principle still applies. IE, these weightlifters are probably also very capable (eg) shotputters because of all that weightlifting. But also... their shotput, sprinting and other tangential abilities probably peak at some point. From then on, they are mostly just getting stronger at clean and jerk. > Hormonal responses are global. Endurance/strength is a spectrum but the whole body goes along for the ride. This is true, and that is why most exercise is a general good for most people, and has similar physiological effects. However, at some point "specialization" (term of art), kicks in. At that point, a bigger clean and jerk no longer equates to a longer shot put. Fwiw... This isn't a point about exercise or how to exercise. Most people aren't that specialized or advanced in a sport and the ones who are have coaches. My point is that the phenomenon speculated to be broad in this post applies (I suspect) to physiology. Probably quite broadly. It's just easy to think about it in terms of sports because "training & optimization" directly apply.
• slashdave 2y ago
  I am surprised that the author left out another mitigation. To build solutions (models) that are constructed to be more transferable (amenable to out of domain data). For example, in machine learning, using physics informed models. Perhaps this is simply a sign that the author is a proponent of generic, deep-learning.
  • yldedly 2y ago
    Most people in ML, even if they are very proficient, don't understand why models should generalize out of domain. They just don't think about it.
• leoc 2y ago
  Related: "Dodo-Lean" by Darrell Mann https://www.darrellmann.com/dodo-lean/ https://www.darrellmann.com/dodo-lean/ , about systems which have been optimised into fragility.
• sillyLLM 2y ago
  It seems that this is related to the Tim Harford book Messy: The Power of Disorder to Transform Our Lives, but that book is not about deep learning.
• jrochkind1 2y ago
  Calling this the "strong version of Goodhart's law" was immediately brain-expanding for me. I have been thinking of goodhart's law a lot, but realized I had been leaning toward focusing on human reaction to the metric as the cause of it; but this reminded me it's actually fundamentally about the fact that any metric itself is inherently not an exact representation of the quality you wish to represent. And that this may, as OP argues, make goodhart's law fundamental to pretty much any metric used as a target. Independently of how well-intentioned any actors. It's not a result of like human laziness or greed or competing interests, it's an epistemic (?) result of the neccesary imperfection of metric validity. This makes some of OP's more contentious "Inject noise into the system" and "early stopping" ideas more interesting even for social targets. "The more our social systems break due to the strong version of Goodhart's law, the less we will be able to take the concerted rational action required to fix them." Well, that's terrifying.
• amai 2y ago
  Related is the book from deMarco (2002): Slack https://herbertlui.net/slack-tom-demarco-summary/ https://herbertlui.net/slack-tom-demarco-summary/ “People under time pressure don’t think faster!”
• cb321 2y ago
  I do like it when researchers try to connect the deep ideas within their work to broader more general systems, but caution is warranted to the optimism. This article is the kind of formal analogy that inspired/drove much of the marketing appeal of the Santa Fe Institute back in the 1980s. It's honestly always pretty fun, but the devil is usual in the details here (as is usual in making anything "work", such as self-organized criticality [1] which if you enjoyed this article you will also probably enjoy!). As just one example to make this point more concrete (LOL), the article mentions uncritically that "more complex ecosystems are more stable", but over half a century ago in 1973 Robert May wrote a book called "Stability and Complexity in Model Ecosystems" [2] explaining (very accessibly!) how this is untrue for the easiest ideas of "complex" and "stable". In more human terms, some ideas of "complex" & "stable" can lead you astray, as has been appearing in the relatively nice HN commentary on this article here. Perhaps less shallowly, things go off the rails fast once you have both multiple metrics (meaning no "objective Objective") and competing & intelligent agents (meaning the system itself has a kind of intrinsic complexity, often swept under the rug by a simplistic thinking that "people are all the same"). I think this whole topic folds itself into "Humanity Complete" (after NP-complete.. a kind of infectious cluster of Wicked Problems [3]) like trust/delegation do [4]. [1] https://en.wikipedia.org/wiki/Self-organized_criticality https://en.wikipedia.org/wiki/Self-organized_criticality [2] https://press.princeton.edu/books/paperback/9780691088617/stability-and-complexity-in-model-ecosystems https://press.princeton.edu/books/paperback/9780691088617/st... [3] https://en.wikipedia.org/wiki/Wicked_problem https://en.wikipedia.org/wiki/Wicked_problem [4] https://en.wikipedia.org/wiki/Demarcation_problem https://en.wikipedia.org/wiki/Demarcation_problem
• layer8 2y ago
  I propose to denote the worsening trajectory by the term “enshittification”.
• stefantalpalaru 2y ago
  [dead]
• bilsbie 2y ago
  This is why I don’t like focusing on GDP. I think a quarterly poll on life satisfaction and optimism would be a better measure. If you’re curious about GDP. I my car breaks and I get it fixed, that adds to GDP. If a parent stays home to raise kids, that lowers GDP. If I clean my own house that lowers GDP. Etc. Unemployment is another crude metric. Are these jobs people want or do they feel forced to work bad jobs.
  • swed420 2y ago
    Agreed, and that goes for capitalism at large. Here's a rough outline for one proposed alternative to capitalism and the failed central planning alternatives of the past: https://jacobin.com/2019/03/sam-gindin-socialist-planning-models https://jacobin.com/2019/03/sam-gindin-socialist-planning-mo... Some relevant snippets: > Though planning and worker control are the cornerstones of socialism, overly ambitious planning (the Soviet case) and overly autonomous workplaces (the Yugoslav case) have both failed as models of socialism. Nor do moderate reforms to those models, whether imagined or applied, inspire. With all-encompassing planning neither effective nor desirable, and decentralization to workplace collectives resulting in structures too economically fragmented to identify the social interest and too politically fragmented to influence the plan, the challenge is: what transformations in the state, the plan, workplaces, and the relations among them might solve this quandary? > The operating units of both capitalism and socialism are workplaces. Under capitalism, these are part of competing units of capital, the primary structures that give capitalism its name. With socialism’s exclusion of such private units of self-expansion, the workplace collectives are instead embedded in pragmatically constituted “sectors,” defined loosely in terms of common technologies, outputs, services, or simply past history. These sectors are, in effect, the most important units of economic planning and have generally been housed within state ministries or departments such as Mining, Machinery, Health Care, Education, or Transportation Services. These powerful ministries consolidate the centralized power of the state and its central planning board. Whether or not this institutional setup tries to favor workers’ needs, it doesn’t bring the worker control championed by socialists. Adding liberal political freedoms (transparency, free press, freedom of association, habeas corpus, contested elections) would certainly be positive; it might even be argued that liberal institutions should flourish best on the egalitarian soil of socialism. But as in capitalism, such liberal freedoms are too thin to check centralized economic power. As for workplace collectives, they are too fragmented to fill the void. Moreover, as noted earlier, directives from above or competitive market pressures limit substantive worker control even withinthe collectives. > A radical innovation this invites is the devolution of the ministries’ planning authority and capacities out of the state and into civil society. The former ministries would then be reorganized as “sectoral councils” — structures constitutionally sanctioned but standing outside the state and governed by worker representatives elected from each workplace in the respective sector. The central planning board would still allocate funds to each sector according to national priorities, but the consolidation of workplace power at sectoral levels would have two dramatic consequences. First, unlike liberal reforms or pressures from fragmented workplaces, such a shift in the balance of power between the state and workers (the plan and worker collectives) carries the material potential for workers to modify if not curb the power that the social oligarchy has by virtue of its material influence over the planning apparatus, from information gathering through to implementation as well as the privileges they gain for themselves. Second, the sectoral councils would have the capacity, and authority from the workplaces in their jurisdiction, to deal with the “market problem” in ways more consistent with socialism. > Key here is a particular balance between incentives, which increase inequality, and an egalitarian bias in investment. As noted earlier, the surpluses earned by each workplace collective can be used to increase their communal or individual consumption, but those surpluses cannot be used for reinvestment. Nationwide priorities are established at the level of the central plan through democratic processes and pressures (more on this later) and these are translated into investment allocations by sector. The sector councils then distribute funds for investment among the workplace collectives they oversee. But unlike market-based decisions, the dominant criteria are not to favor those workplaces that have been most productive, serving to reproduce permanent and growing disparities among workplaces. Rather, the investment strategy is based on bringing the productivity of goods or services of the weaker collectives closer to the best performers (as well as other social criteria like absorbing new entrants into the workforce and supporting development in certain communities or regions). ... > No one paid greater economic homage to capitalism than the authors of The Communist Manifesto, marveling that capitalism “accomplished wonders far surpassing Egyptian pyramids, Roman aqueducts, and Gothic cathedrals.” Yet far from seeing this as representing the pinnacle of history, Marx and Engels identified this as speaking to a new and broader possibility: capitalism was “the first to show what man’s activity can bring about.” The task was to build on this potential by explicitly socializing and reorganizing the productive forces. > In contrast, for Hayek and his earlier mentor von Mises, capitalism was the teleological climax of society, the historical end point of humanity’s tendency to barter. Hayek considered it a truism that that without private property and no labor and capital markets, there would be no way of accessing the latent knowledge of the population, and without pervasive access to such information, any economy would sputter, drift, and waste talent and resources. Von Mises, after his argument that socialism was essentially impossible was decisively swept aside, turned his focus on capitalism’s genius for entrepreneurship and the dynamic efficiency and constant innovation that it brought. > Despite Hayek’s claims, it is in fact capitalism that systematically blocks the sharing of information. A corollary of private property and profit maximization is that information is a competitive asset that must be hidden from others. For socialism, on the other hand, the active sharing of information is essential to its functioning, something institutionalized in the responsibilities of the sectoral councils. Further, the myopic individualism of Hayek’s position ignores, as Hilary Wainwright has so powerfully argued, the wisdom that comes from informal collective dialogue, often occurring outside of markets in discussions and debates among groups and movements addressing their work and communities.
• redsparrow 2y ago
  This makes me think of going to chain restaurants. Everything has been focus-grouped and optimized and feels exactly like an overfit proxy for an enjoyable meal. I feel like I'm in a bald-faced machine that is optimized to generate profit from my visit. The fact that it's a restaurant feels almost incidental. "HI! My name is Tracy! I'm going to be your server this evening!" as she flawlessly writes her name upside down in crayon on the paper tablecloth. Woah. I think this place needs to re-calibrate their flair.
• shae 2y ago
  Gerrymandering is over fitting. Mitigation: randomize the actual shape of a district when the votes are counted.
• aswanson 2y ago
  You can also be too efficient in your career/life. You can take the "Inject noise into the system" as injecting positive randomness into your associations with people and ideas. If something seems slightly interesting but off your beaten track, learn more about it.
• idunnoman1222 2y ago
  The statement overfits its own idea. Testing students is not an example of an efficiency
• kzz102 2y ago
  I think of efficiency as one example where naive economic thinking has poisoned common sense. Economists view inefficiency as a problem. Because a healthy economy is efficient, therefore inefficiency is unhealthy. Any inefficient market is a "market failure". Efficiency is also the primary way a manager can add value. But the problem is, efficiency assumes existence of metrics, and indeed is counter productive if your metrics are wrong.
  • marcosdumay 2y ago
    > Efficiency is also the primary way a manager can add value. That's not right. The primary task of management is alignment.
    • kzz102 2y ago
      > That's not right. The primary task of management is alignment. Fair enough.. at least they think they can add value by improving efficiency.
      • marcosdumay 2y ago
        It's a way they can add value. It's far from their primary way, and it's a task that is not primary done by management. But yes, there are plenty of managers that focus on it.
• tonymet 2y ago
  The author is right that we rely on metrics too much. But he's biased against capitalism and his proposed cure is more socialism. What's actually lacking is wisdom and integrity.
• orcim 2y ago
  It's an effect that exists, but the examples aren't accurate. An overemphasis on grades isn't from wanting to educate the population; obesity isn't from prioritizing nutrient-rich food; and increased inequality isn't from wanting to distribute resources based on the needs of society. Living a well-lived life through culture, cooking, or exercise doesn't make you more susceptible to sensationalism, addiction, or gambling. It's a lack of stimulus that makes you reach for those things. You can argue that academia enables rankings, industrial food production enables producing empty calories, and economic development enables greater inequality. But that isn't causation. It also isn't a side effect when significant resources specifically go into promoting education as a private matter best used to educate the elite, that businesses aren't responsible for the externalities they cause, and that resources should be privately controlled. In many ways, it is far easier to have more public education, heavily tax substances like sugar, and redistribute wealth than it is to do anything else. That just isn't the goal. It used to be hard to get a good education, good food, and a good standard of living. And it still is. For the same reasons.
• chriscappuccio 2y ago
  While this intuitively seems likes good idea, his real life examples are severely lacking. This gets interesting when we see where the rubber hits the road, the causes and effects of what is being optimized for vs what is happening and we look deeply into improving that scenario.
• chrisweekly 2y ago
  Efficiency tends to come at the cost of adaptability. Don't put it on rails if it needs a steering wheel. So many enterprises suffer from extreme rigidity - often caused by optimizations that lead to local maxima.
• otherme123 2y ago
  > Goal: Distribution of labor and resources based upon the needs of society > Proxy: Capitalism > Strong version of Goodhart's law leads to: Massive wealth disparities (with incomes ranging from hundreds of dollars per year to hundreds of dollars per second), with more than a billion people living in poverty Please, show me a point in all human history when we have less than 90% global population living in poverty, pre-capitalism. Yes, there are 1 billion people (out of 8 billion) living in poverty today. But they were 2 billion (of 4.5 billion total) living in poverty as recently as 1980 (https://www.weforum.org/agenda/2016/01/poverty-the-past-present-and-future/ https://www.weforum.org/agenda/2016/01/poverty-the-past-pres...). Poverty is steadily going down (https://www.weforum.org/agenda/2016/01/poverty-the-past-present-and-future/ https://www.weforum.org/agenda/2016/01/poverty-the-past-pres...) since we have data. The first countries to get rid of recurrent famines were the same that first adopted capitalism. The same countries where their population started having higher expectations than to live another day. Paraphrasing Churchill about democracy, "[capitalism] is the worst economic system except for all other systems that has been tried from time to time".
  • jaco6 2y ago
    There is no such thing as capitalism. “The first countries to get rid of recurrent famine” were those that began using the steam engine and field enclosure. These are not “capitalism”, they are “capital”, in tge jargon of the economist Marx, or, in modern parlance, technology. All of the parts of the world that are not starving are not starving due to their adoption of a variety of technologies, both mechanical and bureaucratic: fertilizer, machine tractors, and centralized governance using telephone lines and now internet. When people claim “capitalism” ended starvation, they ignore places like China and Russia which also ended famine despite adopting state ownership of farms. There was indeed famine during the transition period, but Russia ended famine by the 50s and China ended famine by the 70s, long before many “capitalist” countries in the 3rd world. That’s because the world isn’t about the phony field of “economics,” it’s about technology. Likewise, the other advancements of society that are claimed to be associated with “capitalism”—the end of smallpox and other old high-casualty diseases, the development of reliable air and trans-oceanic transport, instant global communications: all are due to the efforts of scientists working in labs mostly funded by governments and wealthy patrons, not “capitalism.” Capitalist enterprises almost never take major innovative risks. Even the latest glorious advancement that will doubtless be claimed by “capitalism,” Wikipedia-scraping chat bots, was developed by a non-profit. Everything is about technology—stop letting economists drag you into stupid, poorly formed debates using undefined terms like “capitalism.”
• leeoniya 2y ago
  "Efficiency trades off against resiliency" https://blog.nelhage.com/post/efficiency-vs-resiliency/ https://blog.nelhage.com/post/efficiency-vs-resiliency/
• ezekiel68 2y ago
  This certainly tugs at all the right levers of the intuition. Not sure how to "buck the trend" in any established organization/regime to adjust expectations according to the theory. Looks like this might need to be demonstrated in the wild at a new concern or as a turnaround job, where the leaders could have a strong influence on the culture (similar to how Jim Keller steered AMD and is now steering TensTorrent).
• 015a 2y ago
  "Efficiency" meaning, given some input cost, reducing the loss of applying that cost, toward some measured outcomes. High efficiency implies something about each of those three stages, none of which are reasonable to apply in all situations: 1. That the only input to the system is cost/money (or proxies of that, like compensated human time). Put another way: That the model you're working with is perfectly liquid, and you don't need to worry about fundamental supply constraints. 2. That the loss is truly loss, and there isn't some knock-on effects from that loss which might range from generally beneficial and good, to actually being somewhat responsible for the output metric, and your model is measuring the wrong thing. 3. That the output metric correctly and holistically proxies for the real-world outcomes you desire. Using the example from the article on standardized testing: A school administration might make an efficiency argument by comparing dollars spent to standardized test scores. * Dollars isn't the only input to this system, however; two major ones also include the quality of teachers and home life of the students. Increasing the spend of the system might do nothing to standardized test scores if these two qualities also can't be improved (you might make the argument that increasing dollars attracts better teachers, and there's some truth to this, but generally (even in tech) these two things just aren't strongly correlated; many organizations have forgotten what it even means to be "good at your job" and how to screen for quality in interviews. When organizations lose that, no amount of money can generate good hires because the litmus test doing the hiring is bad). * "Loss" in this system might be the increase of funding without seeing proportionally increasing test scores; which does not account for spending money in extracurriculars like music, art, and sports; all generally desirable things we believe money should be spent on (isn't it interesting that we call these things "extra"curriculars?). * Even if a school administration can apply this model to increase test scores, increasing test scores might not be an outcome anyone really wants. As the article says, all that guarantees is a generation of great test-takers. Increasing college acceptance rates? We've guaranteed a generation of debtors and bad degrees. Turns out, its impossible to proxy for the real world thing you want, in a way that can be measured on a societal level. All of this is really just symptoms of the "financialization of everything", which has been talked about endlessly. In particular to this discussion, society has broadly forgotten about what the word "service" means; that public transit in your city must be a capitalistic enterprise, it itself has an efficiency metric that must be internally positive, because the broader positive efficiency impact that public transit network has on the people and businesses in the city, and thus municipal tax income, is too complex to account for within a more unified economic model.
• whizzter 2y ago
  This has become a societal problem in Sweden during the past 20 or so years. 1: Healthcare efficiency is measured by "completed tasks" by primary care doctors, the apparatus is optimized for them handling simple cases and they thus often do some superficial checking and either send one home with some statistically correct medicine (aspirin/antibiotics) or punt away cases to a specialized doctor if it appears to be something more complicated. The problem is that since there's now fewer of them (efficient) they've more or less assembly line workers and have totally lost the personal "touch" with patients that would give them an indication on when something is wrong. Thus cancers,etc are very often diagnosed too late so even if specialized cancer care is better, it's often too late to do anything anyhow. 2: The railway system was privatized, considering the amount of cargo shipped it's probably been a huge success but the system is plagued by delays due to little gaps in the system to allow late trains to speed up or to even do more than basic maintenance (leading to bigger issues).
  • EasyMark 2y ago
    I wish these were the biggest problems facing US train and healthcare industries.
  • ksec 2y ago
    There are examples everywhere. To quote Steve Jobs; >When a company grows big enough, they want to replicate their initial success. They all thought about the "process" of how the first success was created. So they replicate those "process" across the company. And before very long the people confused that the process was the content. And you can fit that from small companies to the world's largest government. Most of them forgot about their content.
• thomassmith65 2y ago
  I noticed an example of this rule at my local hardware superstore. Around a decade ago, the store installed anti-theft cages. At first they only kept high-dollar items in the cages. It was a bit of an inconvenience, but not so bad. If a customer is dropping $200+ on some fancy power tool, he or she likely doesn't mind waiting five minutes. But a few years later, there was a change - almost certainly a 'data-driven' change: suddenly there was no discernible logic to which items they caged and which they left uncaged. Now a $500 diagnostics tool is as likely to sit open on a shelf, as a $5 light bulb to be kept under lock and key. Presumably the change is a result of sorting a database by 'shrinkage' - they lock up the items that cumulatively lose the hardware store the most money, due to theft. But the result is (a) the store atmosphere reads as "so profit-driven they don't trust the customers not to steal a box of toothpicks" and (b) it's often not worth it for customers to shop there due to the waiting around for an attendant to unlock the cage. I doubt the optimization helped their bottom-line, even if it has prevented the theft of some $3 bars of soap.
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  • tshaddox 2y ago
    It’s much more convenient to buy from Amazon than to try to find someone to unlock a glass case at the pharmacy. Especially since any pharmacy with glass cases for basic items will also be understaffed.
    • thomassmith65 2y ago
      A few of my visits to this hardware store have been... memorable! (1) locate the aisle with the item I want to verify it is still in stock (2) spend 5 minutes wandering around the isles looking for a free attendant (3) give up, press the 'service' button (4) wait at the service desk for another 5 minutes until an attendant arrives to unlock the cage and hand me my $10 item (5) have attendant inform me that he must immediately frogmarch me to the nearest self-checkout station, even though I had further items I wanted to buy. Otherwise, I would have a chance to secret the $10 item in my pocket, or to dash for the door? (6) self-checkout and walk through the parking lot to place the item in my vehicle (7) walk back to the store to buy my other items (8) realize one of my other items is also locked in a cage (9) got back to step (1) and repeat for a second item I realize, as I type this, that the store has turned into the American equivalent of the British 'catalog merchant' except, in the UK, a catalog store gives you a numbered ticket, so the customer has, at least, a streamlined procedure and knows what to expect.
• fwungy 2y ago
  Efficiency means minimizing the use of the costliest components. It's installing fault points into a system on purpose. Robust systems minimize fault points. Efficient systems come at the cost of robustness, and vice versa given a fixed definition of what is being conserved, i.e. costs or energy. For example, a four cylinder engine that gets 15mpg will have a longer life than one that gets 30mpg, given the same cost.
• efitz 2y ago
  I am skeptical of the analogy to overfitting, although I understand where the author is coming from and agree with the sentiment. The basic problem is stupid simple. Optimizing a process for one specific output necessarily un-optimizes for everything else. Right now much of commerce and labor in the United States is over-optimized for humans because tech businesses are optimizing for specific outcomes (productivity, revenue, etc) in a way that ignores the negative impacts on the humans involved. The optimizations always turn into human goals, eg my manager needs to optimize for productivity if they want a bonus (or not get optimized out themselves), which means they need to measure or estimate or judge or guess each of their employees’ productivity, and stupid MBA shit like Jack Welch’s “fire the lowest 10% every year”) results in horrible human outcomes. Sure there are people who need to be fired, but making it an optimization exercise enshittified it. Same for customer service. Amazon wants to optimize revenue. Customer service and returns are expensive. Return too many things? You’re fired as a customer. Call your mobile providers customer service too often? Fired. Plus let’s not staff customer service with people empowered to do, well, service. Let’s let IVRs and hold times keep the volumes low. All anecdotes but you’ve experienced something similar often enough to know it is the rule, not the exception, and it’s all due to over-optimization.
• mch82 2y ago
  Really interesting to learn about the ML perspective of the cost of localized efficiency. Local efficiency can also make things worse from a queueing theory perspective. Optimizing a process step that feeds a system bottleneck can cause queues to pile up, decreasing system-level productivity. Automating production of waste forces downstream processes to deal with added waste.
• fazkan 2y ago
  Kind of reminds of the cellular automaton, about how reducing the number of components can lead to complex worlds and rules. Maybe tangentially related. Conway game of life is another example.
• _wire_ 2y ago
  Too much efficiency countering too much efficiency makes everything worse. This whole thesis easily tips over into a semantic gobbledygook, as efficiency is not a property of the larger world, but an utter contrivance of thought. Focus on anything to the exclusion of everything use and things are going wrong. How has the obviousness of this observation has turned into a breakthrough? AI is the perfect nexus for such a discovery: trying to optimize a system when you don't understand how it works naturally has pitfalls. So what can it mean to try to mathematically formalize a misunderstanding? Maybe there's a true breakthrough lurking near this topic: that all understanding is incomplete, so look for guiding principles of approximation? The author is right to call out the forest for the trees. — Web Design: The First 100 Years https://idlewords.com/talks/web_design_first_100_years.htm https://idlewords.com/talks/web_design_first_100_years.htm How the SR71 Blackbird Works https://m.youtube.com/watch?v=gkyVZxtsubM https://m.youtube.com/watch?v=gkyVZxtsubM
• MorningBell 2y ago
  Premature optimization is the root of all evil. - Donald Knuth Head and hands need a mediator. The mediator between head and hands must be the heart! - movie "Metropolis"
• michaelcampbell 2y ago
  This hits home. Management has started measuring things at my workplace. They won't admit it, but I said from the start it was because they are easy to measure, not because they are useful.
• 2grue 2y ago
  This is super interesting. The author states "the strong version of Goodhart's law" as a fact, but does not provide a theorem which shows that it is true. This recent paper does the job.[0] The authors write about Goodhart's law in the context of AI alignment but they are clear that their theorem is much more broadly applicable: > we provide necessary and sufficient conditions under which indefinitely optimizing for any incomplete proxy objective leads to arbitrarily low overall utility > Our main result identifies conditions such that any misalignment is costly: starting from any initial state, optimizing any fixed incomplete proxy eventually leads the principal to be arbitrarily worse off. [0]: https://proceedings.neurips.cc/paper/2020/hash/b607ba543ad05417b8507ee86c54fcb7-Abstract.html https://proceedings.neurips.cc/paper/2020/hash/b607ba543ad05...