81–90 of 377 posts
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I feel that a 100% efficient system is not resilient. Even minor disruptions in subsystems lead to major breakdowns. There’s no room to absorb shocks. We saw a drastic version of this during COVID-19 induced supply chain collapse. Car manufacturers had built near 100% just in time manufacturing that they couldn’t absorb chip shortages and it took them years to get back up. It also leaves no room for experimentation.…
There is a fundamental tension between efficiency and resilience, you are completely correct. And yea, it’s a systems problem, not limited to tech. There is an odd corollary, which is that capitalistic systems which reward efficiency gains and put downward pressure to incentivize efficiency, deal with the resilience problem by creating entirely new subsystems rather than having more robust subsystems, which is fundam…
Is what you’re saying that capitalism breaks down resilience problems into efficiency problems?
I think that’s an extremely motivating line of thinking, but I’ll have to do some head scratching to figure out exactly what to make of it. On one hand, I think capitalism is really good at resilience problems (efficient markets breed resilience, there’s always an incentive to solve a market inefficiency), on the other (or perhaps in light of that) I’m not so sure those two concepts are so dialectically opposed
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The problem is that people who agree to a task being low priority still expect it to be done in nine months and all of a sudden they become high priority if that doesn’t happen. So you’re fixing the micro economics of the queue but not the macro. Queues still suck when they fill up, even if they fill with last minute jobs.
This totally depends on the system in question and what the agreements with your users are. Eg if you are running video conferencing software, and all of a sudden you are having bandwidth problems, you typically first want to drop some finer details in the video, and then you want to drop the audio feed. In any case, if you dropped something, you leave it dropped, instead of picking it back up again a few seconds lat…
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.
Yep, I used to work in a factory. Target utilization at planning time was 80%. If you over-predict your utilization, you waste money. If you under-predict, a giant queue of “not important” stuff starts to develop
If she had 0, she ran the risk of turning customers away and losing money. Any more than 1 is excess waste. Having just 1 meant she’d served every possible customer and only “wasted” 1 slice.
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.
I’m remembering reading once that cities are incredibly efficient in how they use resources (compared to the suburbs and rural areas, I guess), and, in light of your comment about waiting time, I’m realizing why now why they’re so unpleasant: constant resource contention.
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.
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 i…
In the process, acting somewhat like a generalization of the problem it describes: overly precise and narrow approaches to "improve" ineffable qualities. But the author seems to understand that - he comments on the absurdity of some direct transfers of ML methods to real world problems. I think he just added a bunch of not necessarily well solvable, but particularly suffering from "overfitting", example problems. It's a food for thought article, not a grand proposal.
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.
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Amusingly this is something that I see as being a huge divide in rural and urban politics. Yes, it’s inefficient. Yes, some people want that!
Right. Living is not an optimization problem.
> 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.