思考与写作sohl-dickstein.github.io
效率过高反而使一切更糟
Too much efficiency makes everything worse (2022)
2022 — 2024，它一次次回到首页
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导读
这是斯坦福研究者 Jascha Sohl-Dickstein 的博文，讨论所谓「强古德哈特定律」：当一个系统被过度优化去最大化某个可测量的指标时，它会把资源全部押在这个指标上，逐渐丧失应对意外的余裕，最终在真实世界的扰动下崩溃——效率越高，系统反而越脆弱。文章把机器学习中的过拟合、供应链的准时制、社会治理的规则化等现象串在同一个框架下。
为什么经久不衰：它用一条简洁的抽象规律解释了跨领域反复出现的失败模式，从 JIT 供应链到 Spectre 漏洞都能对上号，所以隔几年就会被重新翻出来讨论。
HN 评论精选
用 Tainter 的文明崩溃理论类比：靠不断加规则来解决问题，本质是制度层面的过拟合。
推荐读 Joseph Tainter 的《复杂社会的崩溃》。复杂社会解决问题的方式就是不断加规则加管制，因为历史上一直这么做且曾经奏效，更关键的是这往往是它们唯一会用的工具。本质上这些社会在把法律体系过拟合到一个个狭窄的特例上，直到再也处理不了任何意外，因而极度脆弱。我在自己的国家每天都亲眼见到这种情况，住在那儿像躺在普罗克鲁斯忒斯的床上。
lqet查看英文原文
I recommend reading "The Collapse of Complex Societies" by Joseph Tainter [0]. Complex societies tend to address problems by adding more and more rules and regulations, simply because they have always done so and it has been successful in the past. More importantly, though, it is typically the only tool they have. Essentially these societies are increasingly overfitting their legislature to narrow special cases until it cannot handle anything unexpected anymore. Such a society is highly fragile. I witness this firsthand every day in my own country. Living here feels like lying in a Procrustean bed. [0] https://www.amazon.com/Collapse-Complex-Societies-Studies-Archaeology/dp/052138673X
问题不在效率，而在于用易测的代理指标替代最终目标来评判成败。
这个道理总在新领域被重新发现。JIT 曾是制造业的救星，直到人们发现一次堵车导致的一次延迟交货，损失可以远超库存省下的钱。优化到处都重要，但衡量优化本身比衡量最终结果更容易、更便宜、也更早，于是人们就去量库存天数、库存金额这类代理指标——直到疫情来袭，「效率」的含义突然从最低成本变成了稳定性。这种事一再重演：分支预测/Spectre、抵押贷款证券化⋯⋯基于二阶三阶效应的优化，一旦落到任何抽象都只是近似的真实世界就会爆炸。所以问题不在效率本身，而在于把焦点从最大化期望产出转向最大化效率，并用效率优化的表现来衡量成功。
brookst查看英文原文
This keeps getting rediscovered in new domains. JIT was the savior of manufacturing, until people learned that a single traffic jam that delayed a single delivery could create costs far in excess of the inventory savings. Optimizations are critical, everywhere. And measuring optimizations is important because it is is easier and cheaper and earlier than measuring end results. Measuring days of inventory, or dollars in inventory, is a pretty good proxy for supply chain efficiency, until covid hits and suddenly “efficiency” means stability rather than minimum costs. Over and over. Branch prediction / Spectre. Mortgage-Backed Securities. Optimizations based on second- and third-order effects blow up in the real world where any abstraction is approximate. So it’s not the efficiency that’s the problem, it’s changing the focus from maximizing the desired output to maximizing efficiency, and measuring success based on the performance of efficiency optimizations.
效率与灵活性是权衡；榨干安全边际会让失败来得更猛烈。
一切都是权衡。效率通常是一种限制灵活性的权衡。如果你恰好不需要灵活性，就能获得惊人的效率：轨道车的轮子比脚高效得多，但前提是它待在轨道上。到了更复杂的地形上，脚突然更划算，因为它能适应各种条件——当然要以牺牲效率为代价（复杂度、能耗、需要更高级的控制回路等）。做生意通常得为意外留安全边际；若以效率和利润之名把它榨干，一切正常直到不正常，然后就败得惨得多，甚至灾难性。Taleb 整本书《反脆弱》讲的就是这个，还配了一堆有趣的真实例子。
nine_k查看英文原文
Everything is a tradeoff. Efficiency is usually a tradeoff that limits flexibility. If you don't happen to need flexibility, you can have amazing efficiency: a wheel of a rail car is so much more efficient than a foot. But only when it stays on a rail. If we look at a somehow more difficult terrain, a foot suddenly happens to be a better deal, because it adapts to a variety of conditions. Of course, at the expense of efficiency (complexity, energy consumption, need for a much more advanced control circuits, etc). In business you usually have to have some safety margin for the unexpected. If you squeeze it out in the name of efficiency (and profit), all goes well until it does not, and then it fails much harder, maybe catastrophically. (Nassim Taleb wrote a whole book about that, "Antifragility", where he gives a ton of fun real life examples to make basically the above point.)
引用反脆弱：过度规则化的「稳健」最终会被黑天鹅击穿。
这正是 Taleb 写过的：系统起初非常脆弱，随着时间推移，各种边缘情况和事件让规则越来越多、越来越僵化，系统变得越来越「稳健」；直到某天出现意外——即黑天鹅事件——它已经僵化到无法应对变化。唯一的解法是构建这样一种系统：它在遭遇混乱的过程中反而变得更善于应对混乱。
ToJans查看英文原文
This is exactly what Taleb wrote about [1]. Systems start out very fragile. Over time they get more and more robust, as edge cases and events result in more rules and rigidity. This works until they become so robust that they can no longer cope with the change when something unexpected -i.e. a black swan event - happens. The only way to counter this, to make a system that improves in coping with disorder/chaos as it encounters it. [1] https://en.wikipedia.org/wiki/Antifragility Update: reworded that last sentence a bit.
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