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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?
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.
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?
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.
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
This same counterintuitive relationship between efficiency and outcome occurs in machine learning, where it is called overfitting.
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.
When a measure becomes a target, if it is effectively optimized, then the thing it is designed to measure will grow worse.
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.
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.
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.
perfect reminder that when you focus too hard on the proxy, you might win the battle and lose the war
This reminds me of Eli Goldratt's quote: "Tell me how you measure me, I will tell you how I behave."
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.
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.
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. sug…
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.