Chaos, Ignorance and Newton’s Great Puzzle

混沌，无知以及牛顿的未解之谜

原文：Scott H.Young

译者：院校长在学习

Chaos theory is an investigation into mathematical and physical systems that are highly sensitive to initial conditions. The simulated three-pendulum systems above only differ in the tiniest way from each other, yet, after a few seconds, they are all wildly divergent in their movements.

混沌理论是对初始条件极度敏感的数学和物理系统的研究。以上图这个2段式的摆动模拟为例（原文是3段式，我找的是2段式动图），他们开始时仅有极小的差异，但几秒之后，他们的运动轨迹却大相径庭。

I’ve grown to prefer the word “chaos” to the more commonly used term “luck”. People talk about luck a lot. We argue about whether someone’s success was an inevitable consequence of their skill and decisions, or whether they were just lucky. But we also talk about luck in superstitious terms, as if it was something someone possessed (“He’s just lucky.”) or that could ebb and flow with the tides of fortunes (“He’s just had a streak of bad luck.”).

我越来越喜欢用“混沌”来形容“运气”。虽然人们谈论“运气”这个词要更多一些。我们也会去争论某些人的成功，是因为技能和决策的必然结果，还是说那仅仅只是运气。但我们对运气这东西也比较迷信，好像有些人天生就拥有它一样（"他只是运气好而已"）也可能因为时运不济的时候说（“他只是有点倒霉吧”）

Chaos, on the other hand, is a more mathematically precise concept. A system is more chaotic if small changes to its state can create wildly different outcomes. Compare the above system to the solar system. The fact that the sun rises each morning from the horizon may seem a banal fact, but it depends on the lack of chaos in our solar system. Adding a second sun creates a three-body system which, to this day, physicists do not have a way of precisely predicting the orbits.

在另一个方面，混沌，是一个精确的数学概念。如果一个细微行为导致了巨大差异，那么这个系统会更混乱。我们拿这个系统和太阳系来做对比。太阳从地平线上升起，这似乎是一个再寻常不过的事实，但这在太阳系里是一个混沌现象。直到今天，对于添加了另外恒星的三体系统来说，物理学家们都没有能精准预测轨道的方案。（译者注：三体问题是天体力学中的基本模型，即探究三个质量、初始位置和初始速度都为任意的可视为质点的天体，在相互之间万有引力的作用下的运动规律。它们有无数种可能的运动轨迹。最简单的例子就是太阳系中太阳，地球和月球的运动。）

Why Chaos Matters

为什么混沌如此重要

Just as you can consider the amount of chaos in physical system, you can also think of the amount of chaos in different pursuits in life. The more chaotic the pursuit, the more likely small changes can result in completely different outcomes years later.

就像你考虑物理系统中的混乱数量一样，你也可以去思考生活中，不同追求里所存在的混沌。越是混沌，那些微小的变化也越容易在几年后产生巨大的不同。

Although an absolute measurement of chaos is probably impossible, you can compare different pursuits by how chaotic they might be.

尽管我们不可能做到绝对精确的测量混沌本身，但你却可以通过比较混沌的不同来对比不同追求的结果。

Consider two professions: becoming an actor and becoming a doctor. Both are prestigious professions. Both require a lot of hard work without much reward in the beginning, but have the potential for big payoffs.

比如来分析这两种职业：成为一名演员，和成为一位医生。这两者都是很有声望的职业。两者从初期都属于投入巨大的努力却换来较小回报的类型，但他们都有着巨大的回报潜力。

However, acting is a much more chaotic profession than medicine. Landing the right audition can give you a name, which puts you up for bigger and bigger parts. But just as you rise, you can also fall. A bad movie or changing public tastes have also made many famous stars disappear in an equally short time.

然而，演戏要比治病复杂得多（混沌），通过了试镜的你可能会红，可能会让你的名气越来越大，但同样，你也可能因此陨落。比如拍了一部烂片，尝试了一些失败的角色定位，都会让你很容易谈出大家的视野。

Medicine is not chaotic. Although there will be marginal doctors who just get over the cutoff point for getting into medical school or landing a top residency, most are firmly within whatever category they eventually end up.

医学行业就没那么复杂了，虽然有些医生只是刚刚过了行业水准的及格线，但绝大多数医生都会在其中任何一个类别里坚持到最后。

If you considered only a cruder metric, such as success rates, however, you might totally miss this picture. Most would-be actors and doctors fail to eventually reach success in their profession. But they may fail for different reasons. Would-be doctors fail because the profession is difficult and long, and so creates a high-dropout rate, not because it is inherently chaotic. Would-be actors may fail or succeed because small events create feedback loops causing wildly different fortunes.

如果你认为，只存在一种简单的度量方式，比如“成功率”，那么你可能会错过这张图片。大多数想从事演艺和医学行业的人，最终都没能取得成功。他们会出于不同的原因而失败。医生可能会因为职业道路艰难漫长，所以辍学率高。而并不是因为这其中会有太大变数（混乱）。演员的失败或成功，更多会因为小的因素带动了巨大的变化和反馈，造成了不同的命运。

The Problem with Mercury

关于水星的问题

For two hundred years since Newton, physicists had a problem: Mercury. The closest planet to the sun hada strange orbit that precessed. According to Newtonian physics, this wasn’t possible. The object would always have a fixed orbit around the sun, not one that wobbled around.

自牛顿后的200年来，物理学家们一直有一个问题：水星。那颗离太阳最近的行星用非常奇怪的方式在轨道上运动。根据牛顿的物理学说来看，这是不可能的。物体是以固定的轨道围绕太阳运动。而不是不稳定的运动。

Physicists struggled against the problem of Mercury for years. Some suggested there must be a phantom planet, Vulcan, even closer to the sun, which was kicking Mercury’s orbit around.

物理学家们针对“水星问题”斗争了很多年，有些人认为那里还存在一个看不见的星球---“Vulcan”，每当接近太阳的时候它都会对水星产生影响。

Eventually, however, the true answer emerged: Newtonian physics is wrong. Albert Einstein introduced general relativity and its warped spacetime curvature. The new equations correctly predicted the precession and Mercury’s orbit was finally understood.

然而，真正的答案是：牛顿说错了。爱因斯坦在广义相对论中提出了时空扭曲概念。它正确的预测出了水星的运动，从而帮助人们理解了水星的运动规律。

In Mercury’s case, physicists could safely rule out chaos because its precession was so orderly. However, in life, we often don’t get to observe the exact same conditions again and again to see the patterns. We only live once, so everything we experience is, in a certain sense, experienced for the first time.

针对水星问题，物理学家们可以准确的排除混乱，因为他们找到了规律。而在生活中，我们没办法以相同的条件去一遍一遍的观察来找到规律。人，只能活一辈子。所以在某种意义上来讲，我们经历过的每一件事，都是我们的第一次体验。

As a result, the randomness we perceive in life always has a mixture of two possible components. The first is chaos. This is the amount that the system would be unpredictable, even if we had a near perfect understanding. The second is based on our own ignorance of the system, the amount it defies our expectations because we don’t really understand how it works.

所以，我们在生活中遇到的随机一般分为两种情况：

1.混沌：这是系统中不可知的量。（即使我们对生活背后的规律，有一些接近完美的理解）

2.对系统的无知：它的结果超出我们的预期，因我们我们并不真正理解繁杂生活背后的运作规律。

How to Use Chaos in Your Thinking

如何在你的思考过程中运用“混沌”？

In practice, it’s not possible to cleanly separate which systems are chaotic and which are merely poorly understood. However, it’s still useful to understand the existence of these two different sources of randomness.

在实践中，我们不可能完全区分哪些是混沌的，哪些是我们自己无知造成的。但是！！了解这两类随机性对我们还是很有用的。

When approaching any problem in life where outcomes are highly divergent, there’s always two possible mistakes that can be made.

生活中任何问题的结果都是高度发散的，这里面我们会经常触犯两个误区。

The first is to overestimate the possibility of knowledge. By assuming that the system can be understood, but it is actually dominated by chaos, you may be gambling more than you realize. People who make this mistake may be convinced they’ve found the secret of success, but they’re really fooling themselves.

第一个便是高估了知识的可能性。我们可以用一个假设来理解生活，但事实上它是由混沌来主导的。你的投机可能大过于你本身的认知，通常犯了这种错误的人会觉得他们找到了成功的方法，但这可能只是一厢情愿。

The second error is to overestimate the role of chaos. By assuming that the system is inherently unpredictable, you may forego the possibility of mastery.

第二个误区是高估了混沌的作用。假设这个体系本质上是不可预测的，你可能会因此放弃掌握它的可能性。

In my own life, I’ve seen this error with people new to blogging. Many people believe the field is inherently chaotic, depending on “going viral” and getting a lucky bit of publicity. The truth, however, is far more mundane. Most new bloggers I’ve met have obviously different levels of quality, consistency and work-ethic, which leads predictably to different outcomes, given enough time. Blogging success may be both rare and difficult, but I believe it’s less chaotic than many pundits would suggest.

在我的生活里，我就看到很多很多的新博主身上出现这种问题。很多人都相信在自媒体这个领域里就是混乱的。他们更多依靠“病毒性营销”和一点点带运气的推广。但其实，真相更为世俗。我遇到的大部分博主中，都非常明显的存在不同程度的能力，一致性以及职业道德。只要有足够的时间，就会导致出不同的结果来。一个博客的成功，可能确实非常的罕见和不易。但我相信它的混沌程度要低于那些专家所说的程度。

Calibrating Between Assumptions of Chaos and Ignorance

在混沌与无知的假设间进行调整和修正

One of my goals is to try to calibrate my expectations of chaos versus ignorance in different domains. Is my failure to predict because of inherent unpredictability or a lack of understanding?

我的目标之一就是修正我在不同领域上，对混沌和无知的预期。

This is difficult to do, but I’ve found there’s a few heuristics that can make the process easier:

尽管这做起来很难，但我还是发现了一些启发性的关键流程，让事情更简单：

1.Theoretical justifications.I bias towards believing success in picking individual stocks is mostly chaos. I say this, not out of any first-hand experience, but because the efficient market hypothesis strikes me as being a reasonable theory. As such, I’m not interested in learning the skill of picking stocks, and have opted to invest in index funds instead.

1.理论依据

我倾向于相信---那些在个股股票中的成功，通常也是混乱（混沌）的结果。我这么说，并不是因为我自己经历过。而是因为“有效市场”的假说一直在警醒我。 （译者注：有效市场假说认为，在一个充满信息交流和信息竞争的社会里，一个特定的信息能够在股票市场上迅速被投资者知晓。随后，股票市场的竞争会驱使股票价格充分且及时地反映该组信息，从而使得该组信息所进行的交易不存在非正常报酬，而只能赚取风险调整的平均市场报酬率）因此，我对学习如何选股的技巧并不感兴趣，而是选择去投资指数型基金。

2.Existence of expertise.Look at the predictions of the best people. How often are they correct? If even the best don’t predict well, that doesn’t bode well for me. However, if successful predictions are common, it might demonstrate that there’s an understanding that exists which I don’t possess.

2."专家建议"的存在

去看看那些最厉害的人，他们的预测是否会经常正确呢?如果连那些顶尖的人预测起来也不是那么准确的话，对我来说可能并不是好消息。但相反，如果成功的预测是很常见的，那倒是说明，有一种我还不具备的理解存在。

3.Diminishing randomness.Another heuristic is to see how your experience of randomness is decreasing over time. If I’ve gone from 0/10 to 2/10 on a level of mastery from newbie to expert, and my ease at predicting success has gone up substantially, I’m more inclined to believe this trend will continue.

3.正在减少的随机性

另一种探索型的方法是让你了解：感受随时间递减的随机性。如果从新手到专家的过程中，我的熟练度已经从0 / 10升到了2 / 10，而我对成功预测的把握也越来越高。我会更倾向于相信这种趋势还会持续下去。

Randomness, whether from ignorance or chaos, is not ultimately avoidable. But by better calibrating your understanding between the two, you can see where investments in learning more are worthwhile and where it is probably a waste of time.

随机性，无论是从无知还是混乱来说，最终都是难以避免的。 但是，通过更好地修正对两者之间的理解，你就能知道，在学习的过程中，投资到哪些地方会更值得，而哪些地方则可能是在浪费时间。