科学与数学en.wikipedia.org
杰文斯悖论
Jevons paradox
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杰文斯悖论（Jevons paradox）指：技术进步提高了资源使用效率，反而可能增加而非减少该资源的总消耗量。维基百科条目以工业革命时期煤炭使用效率提升却带动煤炭总需求上升为例，说明效率提升会降低单位成本、扩大需求，从而抵消节省。
为什么经久不衰：它是理解能源、算力、带宽乃至软件性能问题的经典思维框架，评论区用宽带、桌面软件、机器学习等现代例子印证了它，所以反复上首页。
HN 评论精选
用拨号转宽带的经历说明效率提升会被迅速膨胀的新需求吞噬。
这和当年美国从拨号上网转向宽带的情形很像。理论上，你本可以更快下完需要的东西，带宽应当闲置更久；但实际是大家开始什么都下载——本来因下载困难而放弃的多个 Linux 发行版、游戏试玩、家用文件服务器，如今几分钟就能搞定。新的效率很快被过去受限于技术瓶颈的既有流程吞噬：只要有受约束的问题，效率提升就会被迅速吸收进现有或新流程。人类很擅长在资源变得充裕时立刻找到把它吃干用尽的办法。
Frost1x查看英文原文
It seems similar to anecdata as the US migrated from dialup to broadband internet services. In theory you could finish downloading everything you needed in a fraction of the time so the bandwidth should remain unused longer. In practice, people started downloading everything. I just need this Linux distribution but here's a dozen more I can now get in minutes and try out that were prohibitive to download before to experiment with but now aren't. People started downloading video game demos like crazy, setup file servers on their home systems, etc. It turns out the new efficiencies tend to be quickly gobbled up by existing processes that were stonewalled before. As long as there are problems bound by some technological constraint, any efficiency gains disappear rapidly as they're quickly integrated into existing or new processes. You didn't use that much bandwidth before because it was scarce but now you have a firehydrant so let's see what we can use. Humans are pretty great at quickly finding ways to gobble up and expand resources whenever they're found. Once you break one of those barriers it may be that the other barriers weren't as unsormountable which leads to rapid rise in demand. That's been my 2 cents.
补充指出布雷斯悖论是同类的反直觉效率现象。
另一位相关角色是布雷斯悖论：在道路网络中增加一条或多条道路，反而可能使整体交通流量变慢。
fernly查看英文原文
And in this corner: Braess's Paradox, "the observation that adding one or more roads to a road network can slow down overall traffic flow through it." https://en.wikipedia.org/wiki/Braess%27s_paradox
用杰文斯悖论解释硬件变快但桌面软件依然缓慢。
我一直有个猜想：杰文斯悖论解释了为什么硬件速度大幅提升后，桌面软件依然很慢。硬件效率提高后，软件就开始索要更多 CPU 周期，因为优化软件的动力变小了。
jasonhansel查看英文原文
A theory I've often had: the Jevons paradox explains why desktop software remains so slow despite the massive improvements in hardware speeds. When you improve the efficiency of the hardware, software starts to demand more CPU cycles, since there's less of an incentive to optimize.
机器学习中数据永远不够用，是杰文斯悖论的又一体现。
在数据科学和机器学习中，这个悖论体现为数据永远不够：一旦有足够数据跑一个简单模型，你就想把模型做得和数据允许的一样复杂甚至更复杂，于是又缺数据了。
karmakurtisaani查看英文原文
In data science and machine learning you can see this paradox in the fact that no amount of data is enough: once you get enough data to run a simple model, you will want to make the model as complicated as the available data allows and beyond. And you're short on data again.
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