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An interesting aspect of efficiency is that it’s often at odds with resiliency. For example, consider an airline. Maximum efficiency would involve every airwor

An interesting aspect of efficiency is that it’s often at odds with resiliency. For example, consider an airline. Maximum efficiency would involve every airworthy plane being fully occupied making flights that are 100% full. Ok, sounds good. Now, one of your planes hits a sign on takeoff and has to return to the airport and be grounded for repairs. How do you deal with all of these passengers who expect to get to their destination? You can't, your incredible efficiency means you’re fucked when something goes wrong. Now imagine that each of those people is part of some other enterprise that’s 100% efficient. The failure will cascade and before long, society’s only concern for efficiency will be about how to most efficiently crack each other’s heads open and feast on the goo inside.

opportune 8y ago
But, this is just one simplistic definition of efficiency. You could include as part of your model some % slack coefficient, some % of failure for each component in the network, and find some kind of allocation that maximizes expected "efficency". The problem isn't optimization, it's that your model doesn't include enough variables
- alexpetralia 8y ago
  Can your model ever include enough variables though? In other words, you can stuff all the variables you want in there, but you will likely still have error - especially with complex systems (regime switches, etc.) - that you did not put in your model. I think economists tried the "model stuffing" technique, and I'm not sure how much it helped in planning something as complex as the economy.
  - mandelbrotwurst 8y ago
    While surely the answer is no for many systems, there are often still be insights to be gained from attempting to model things out as long as you recognize that it will never be perfect.
  - arkades 8y ago
    >Can your model ever include enough variables though? Resiliency isn’t invincibility.
- deleted 8y ago
  [deleted]
kartickv 8y ago
Your point is indeed interesting, and true in a theoretical sense, but not in a practical sense, I think. People and companies do build in buffers, because efficiency != best-case efficiency. For example: https://en.wikipedia.org/wiki/Bus_factor https://en.wikipedia.org/wiki/Bus_factor If you have a meeting at 11:00, you won't plan to reach the office at 10:59. Personally I plan to reach by 10:30 so that I don't have to spend time worrying about being late, which is a waste of mental capacity. For me that's maximally efficient. So I think your definition of efficiency is wrong, if you get what I'm trying to say.
- mikeash 8y ago
  You’re right that there are different ideas of efficiency. I wouldn’t say the zero-slack version is wrong, but it’s certainly not the only one. Even with this broader idea of efficiency, the drive for efficiency can cause trouble. How does one define maximally efficient? Do you optimize to handle cases that occur once a month, or once a year, or a decade, or century? Short-term profit makes it hard to optimize for these rarer cases, because you’ll look less efficient for a long time, and the average executive will have moved on before disaster strikes.
  - kartickv 8y ago
    Your idea of zero-slack efficiency is interesting theoretically, and I haven't heard it articulated as well as you did. But I don't think it's valid in a practical sense, because you can't just ignore the cost of (say) missing a flight because you were a minute too late. If you ignore one side of the coin, you no longer have a valid model of efficiency. Going back to your example of a flight that's canceled, the airline should account for it. One simple way is to multiply the cost of that event by its probability. (And multiply it 5x or something, since passengers will be angrier at having an hour of their time wasted than happier at having an hour saved.) When you look at it with this framework, it makes sense why a pilot doesn't skip his checklist because the cost is very high, even if it's a 1 in 1000 case he's trying to prevent. In other words, it's not just how often it occurs, as you suggested. Another example is how Bill Gates, in the early days of Microsoft, kept a year's worth of salary in the bank at all times. And how just-in-time supply chains broke when the 2008 financial crisis hit. Another way I heard this stated is that the rich are not the people with the most money, but with the most options. Or, hypothetically, a company with 100 engineers who builds iOS and Android native apps dedicating one engineer to building a hybrid app in Flutter because wasting 1% of engineering is time is better than betting the entire company in a wrong direction, thus wasting 100 engineers' time, though the "plan B" engineer's work is unlikely to yield a launchable app. You also rightly point out issues like short-termism and misaligned incentives, but those have been discussed many times already, so I'm not going there.

opportune 8y ago

But, this is just one simplistic definition of efficiency. You could include as part of your model some % slack coefficient, some % of failure for each component in the network, and find some kind of allocation that maximizes expected "efficency". The problem isn't optimization, it's that your model doesn't include enough variables

alexpetralia 8y ago
Can your model ever include enough variables though? In other words, you can stuff all the variables you want in there, but you will likely still have error - especially with complex systems (regime switches, etc.) - that you did not put in your model. I think economists tried the "model stuffing" technique, and I'm not sure how much it helped in planning something as complex as the economy.
- mandelbrotwurst 8y ago
  While surely the answer is no for many systems, there are often still be insights to be gained from attempting to model things out as long as you recognize that it will never be perfect.
- arkades 8y ago
  >Can your model ever include enough variables though? Resiliency isn’t invincibility.
deleted 8y ago
[deleted]

kartickv 8y ago

Your point is indeed interesting, and true in a theoretical sense, but not in a practical sense, I think. People and companies do build in buffers, because efficiency != best-case efficiency. For example: https://en.wikipedia.org/wiki/Bus_factor https://en.wikipedia.org/wiki/Bus_factor If you have a meeting at 11:00, you won't plan to reach the office at 10:59. Personally I plan to reach by 10:30 so that I don't have to spend time worrying about being late, which is a waste of mental capacity. For me that's maximally efficient. So I think your definition of efficiency is wrong, if you get what I'm trying to say.

mikeash 8y ago
You’re right that there are different ideas of efficiency. I wouldn’t say the zero-slack version is wrong, but it’s certainly not the only one. Even with this broader idea of efficiency, the drive for efficiency can cause trouble. How does one define maximally efficient? Do you optimize to handle cases that occur once a month, or once a year, or a decade, or century? Short-term profit makes it hard to optimize for these rarer cases, because you’ll look less efficient for a long time, and the average executive will have moved on before disaster strikes.
- kartickv 8y ago
  Your idea of zero-slack efficiency is interesting theoretically, and I haven't heard it articulated as well as you did. But I don't think it's valid in a practical sense, because you can't just ignore the cost of (say) missing a flight because you were a minute too late. If you ignore one side of the coin, you no longer have a valid model of efficiency. Going back to your example of a flight that's canceled, the airline should account for it. One simple way is to multiply the cost of that event by its probability. (And multiply it 5x or something, since passengers will be angrier at having an hour of their time wasted than happier at having an hour saved.) When you look at it with this framework, it makes sense why a pilot doesn't skip his checklist because the cost is very high, even if it's a 1 in 1000 case he's trying to prevent. In other words, it's not just how often it occurs, as you suggested. Another example is how Bill Gates, in the early days of Microsoft, kept a year's worth of salary in the bank at all times. And how just-in-time supply chains broke when the 2008 financial crisis hit. Another way I heard this stated is that the rich are not the people with the most money, but with the most options. Or, hypothetically, a company with 100 engineers who builds iOS and Android native apps dedicating one engineer to building a hybrid app in Flutter because wasting 1% of engineering is time is better than betting the entire company in a wrong direction, thus wasting 100 engineers' time, though the "plan B" engineer's work is unlikely to yield a launchable app. You also rightly point out issues like short-termism and misaligned incentives, but those have been discussed many times already, so I'm not going there.