First Principles

转载一篇关于第一性原理的意义和价值的博客文章，中英对照。

「非正式周报」万事留痕，认识自己，展示自己。

原文：https://matt-rickard.com/first-principles

翻译工具：ChatGPT, DeepL

校对：LuisY

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If you are to do important work then you must work on the right problem at the right time and in the right way. Without any one of the three, you may do good work but you will almost certainly miss real greatness.Richard Hamming

如果你从事重要的工作，你必须在正确的时间、以正确的方式解决正确的问题。缺少这三者中任何一个，你可能会做的很好，但几乎肯定会错过真正的伟大。——Richard Hamming

No magic answer will guarantee that all three conditions are satisfied. But, by making a series of 95% confidence bets and gathering more data along the way, you can quickly find seemingly impossible futures.

没有一个神奇的答案可以保证这三个条件都得到满足。但是，通过进行一系列的95%置信度的赌注，并在过程中收集更多的数据，你可以迅速发现看似不可能的未来。

A first principle cannot be deduced from any other axiom or assumption. First-principles thinking is about minimizing assumptions, and fewer assumptions mean less risk. So first principles thinking is intertwined with risk minimization. That makes first principles thinking a great decision-making framework.

第一性原理不能从任何其他公理或假设中推导出来。第一性原理是为了尽量减少假设，假设越少，风险越小。因此，第一性原理与风险最小化交织在一起。这使得第一性原理成为一个出色的决策框架。

We make better predictions with more data. Bayes’ Theorem tells us how prior knowledge of conditions can affect the probability of an outcome. Machine learning models often do much better with more training data. First-principles thinking helps build a foundation by breaking down big decisions into a series of small but probable bets.

我们可以通过更多的数据做出更好的预测。贝叶斯定理告诉我们先验知识如何影响结果的概率。机器学习模型通常在有更多训练数据的情况下表现更好。第一性原理通过将重大决策分解为一系列小但可能的赌注来帮助建立基础。

I didn’t know what I wanted to do in undergrad and thought that anyone who thought they did was lying. But, I did know that STEM fields seemed to lead to more exciting outcomes - economists, physicists, mathematicians, computer scientists, and engineers. So, I studied mathematics for maximum optionality. I could always go from math to physics or math to computer science easier than in the other direction. It was a small bet, considering the optionality. So, I hedged with classes in philosophy, history, and the classics.

我不知道我在大学想要做什么，并认为任何认为自己知道的人都是在撒谎。但是，我知道STEM领域似乎会带来更令人兴奋的结果 - 经济学家、物理学家、数学家、计算机科学家和工程师。因此，我学习数学以获得最大的选择性。从数学到物理或从数学到计算机科学，总比从另一个方向容易。考虑到可选性，这是一个小赌注。于是，我又选修了哲学、历史和古典文学。

The possibilities were narrower after making that decision, but the choices were more straightforward. Where is the most important work happening? In the 1950s, the answer was physics, but now it’s easily computer science.

做出这个决定后，可能性变窄，但选择变得更为明确。最重要的工作在哪里？在20世纪50年代，答案是物理学，但现在很明显是计算机科学。

Within computer science, what is here to stay? Programming languages go in and out of style. However, algorithms, data structures, and abstractions seemed to be foundational knowledge. That’s why I stayed as far away from taking classes that required programming, electing to learn that on my own instead. The riskier bet is to know what languages or frameworks will exist in the future, so focus on the theory.

在计算机科学中，什么是不变的？编程语言时兴时衰。然而，算法、数据结构和抽象似乎是基础知识。这就是我避免参加需要编程的课程，选择自学编程的原因。风险更大的赌注是要知道未来会有哪些语言或框架，所以要把重点放在理论上。

With a foundation in mathematics and computer science, the paths again narrow. Now, hardware or software?

有了数学和计算机科学的基础后，路径再次变窄。现在，是硬件还是软件？

I made a bet on software. It has more leverage and optionality and is non-rival. In addition, I could iterate faster in software rather than hardware, trying and testing new paths to find important work.

我押注在软件上。它具有更多的杠杆和选择性，而且是非竞争性的。此外，我在软件中可以更快地迭代，尝试和测试新的路径以找到重要的工作。

To find specialization in software, I asked myself, what is obvious? One hypothesis that seemed obvious is that nearly all companies will utilize cloud computing somehow in the future. This statement may be obvious to many, but it has profound implications. Cloud also has high optionality. Cloud covers many areas of software - operating systems, networking, distributed computing, databases, machine learning, and pretty much every other subfield of computer science.

为了找到软件专业，我问自己，什么是显而易见的？一个看似显而易见的假设是将来几乎所有公司都将以某种方式利用云计算。这个说法对许多人来说可能是显而易见的，但它有着深远的影响。云计算还具有高度的选择性。云覆盖了软件的许多领域 - 操作系统、网络、分布式计算、数据库、机器学习以及几乎计算机科学的每个子领域。

What’s the next logical decision after cloud? As you get higher up the stack, the decisions become individually riskier. However, the cost of a wrong leaf decision is lower with the correct foundation.

在云计算之后，下一个逻辑决策是什么？随着你在堆栈中升高，决策变得更加独立冒险。然而，有了正确的基础，错误的叶子决策成本较低。

A crucial part of Hamming’s quote is getting the timing right. Decisions further up the stack have shorter half-lives (on the longevity of ideas, see The Lindy Effect). Platforms are the next logical step in my mind. Platforms are the act of codifying best practices into new abstractions. Gluing the building blocks together to make the theoretical model match the world. Unfortunately, platforms are more likely to be wrong, and the half-life is shorter.

Hamming的引言中一个至关重要的部分是把握好时机。在决策堆栈中越靠前的决策，其半衰期越短（关于思维的寿命，请参阅《林迪效应》）。在我看来，平台是下一个合乎逻辑的步骤。平台是将最佳实践编码为新的抽象的过程。将构件粘合在一起，使理论模型与现实世界相匹配。不幸的是，平台更容易出错，半衰期更短。

But this is the benefit of foundational knowledge. A wrong bet can easily be pivoted into a correct guess the higher up the stack you are. You are still directionally right with the cloud if you pick the wrong platform. And the knowledge of what doesn’t work is valuable in making future decisions. So you’re increasingly less likely to choose the wrong paths.

但这正是基础知识的好处。在堆栈越高的地方，错误的赌注很容易转变为正确的猜测。如果选择了错误的平台，云计算的方向仍然是正确的。知道什么不起作用对未来决策是有价值的。因此，你越来越不太可能选择错误的道路。

The series of bets results in a thesis that many people could never get to without a foundation. So you can finally start answering, what do I know that nobody else knows? To others, the prediction will seem near impossible. But by first principles and foundational decision making, it will have been obvious.

这一系列赌注的结果，是许多人在没有基础的情况下永远无法得出的结论。因此，你终于可以开始回答：我知道什么是别人不知道的？在其他人看来，这种预测几乎是不可能的。但根据第一原则和基础决策，这将是显而易见的。