If you’ve read a lot of articles about decision making, you might think that good decision making is simply a function of applying the right decision-making frameworks to the world, and then reaping the benefits.
如果你读过很多关于决策的文章，你可能会认为，好的决策仅仅是将正确的决策框架应用于世界，然后从中获益。
For instance, if you’re lucky enough to be picking between three career paths, you might decide to do an expected utility calculation to figure out which of the three to pursue. Or if you find yourself an observer to some internecine office politics, you might decide to fall back to Bayesian analysis to figure out what’s really going on.
举例来说，如果你有幸在三条职业道路中做出选择，你可能会决定进行期望效用计算，以确定在三条道路中选择哪一条。或者，如果你发现自己是一些办公室内部政治的旁观者，你可能会决定回到贝叶斯分析法，以弄清到底发生了什么。
I’ve talked a lot about these ideas on this blog, and I’ve usually had good things to say about all of them. But in this piece I want to take the other side for a bit, and talk about the limitations of these techniques. These limitations, as you’ll soon see, apply to just about every technique drawn from the judgment and decision making literature. They should be obvious to you if you’ve ever attempted to put these ideas to practice, or if you’ve dug deep enough to discover the origins of the ideas.
我在博客上谈了很多关于这些想法的内容，我通常对它们都有很好的评价。但在这篇文章中，我想从另一个角度谈谈这些技术的局限性。正如你很快就会看到的，这些局限性适用于几乎所有从判断和决策文献中总结出来的技巧。如果你曾经尝试过将这些观点付诸实践，或者你曾经深入研究过这些观点的起源，你就会发现它们的局限性。
Somewhat surprisingly, the bulk of the criticism may be captured in a single sentence: “action produces information.” Keep this aphorism in mind; it’ll come in handy in a bit.
令人惊讶的是，大部分批评都可以用一句话概括："行动产生信息"。请牢记这句箴言，它稍后就会派上用场。
Some Examples 一些实例
In order to understand the limitations of classical decision making frameworks, it helps to go through a bunch of real world examples. Here are two.
为了理解经典决策框架的局限性，我们可以通过一些现实世界的例子来了解。这里有两个。
1. Picking Careers 1.选择职业
A friend called me recently to talk about a difficult career decision he was about to make. He was finishing college, and had many options available to him.
一位朋友最近打电话给我，谈到他即将做出的一个艰难的职业决定。他即将完成大学学业，有很多选择。
“So I want to do a startup.” he said, “But I’m balancing that against the possibility of joining a FAANG company, or perhaps a prop trading firm.”
"所以我想做一家初创公司。"他说，"但我正在权衡加入 FAANG 公司的可能性，或者是加入一家道具交易公司的可能性。
“Why the prop trading firm?”
"为什么是道具交易公司？"
“Oh, the money’s good.” "哦，钱很好赚"
“Have you ever worked at one?”
"你在一家公司工作过吗？"
“Nope, but we have mutual friends who have.” (We did).
"没有，但我们有共同的朋友"。(我们有）。
And so it went on in this manner, for a bit. After a couple more questions, we worked out that my friend had:
就这样，我们聊了一会儿。又问了几个问题之后，我们才知道，我的朋友已经
- Interned at two major tech companies.
曾在两家大型科技公司实习 - Interned with two large startups (>50 and >150 people respectively)
曾在两家大型初创企业（分别超过 50 人和超过 150 人）实习 - Not had any experience at an early-stage startup (<10 people, or pre-product market fit) and not had any experience at a prop trading firm.
没有在早期初创企业（小于 10 人，或产品与市场契合前）工作的经验，也没有在道具交易公司工作的经验。
My friend was really smart; it was obvious that we could have done something like an expected utility calculation to work out his options. An expected utility calculation is basically a fancy version of the ‘pros and cons’ analysis that most of us are familiar with. Instead of listing all the pros against the cons, however, we list down a bunch of ‘utilities’ — that is, things that we value e.g.: money, smart teammates, challenging problems, etc — and then we evaluate each option (startup vs FAANG vs prop trading firm) with a score for each item in this list. Finally, we write down the probability that we would realise the utility for each option available to us. (If you want a more detailed example, you may find one here).
我的朋友真的很聪明；很明显，我们可以通过类似于预期效用计算的方法来确定他的选择。预期效用计算基本上是我们大多数人所熟悉的 "利弊分析 "的一个花哨版本。不过，我们不是列出所有的利弊，而是列出一堆 "效用"，也就是我们所看重的东西，例如：金钱、聪明的队友、具有挑战性的问题等，然后我们对每个选项（初创公司 vs FAANG vs 道具交易公司）进行评估，为列表中的每个项目打分。最后，我们写下每个选项实现效用的概率。(如果你想要一个更详细的例子，可以在这里找到）。
But of course we didn’t do that. Expected utility calculations assume perfect information about each choice. That wasn’t the case here.
但我们当然没有这么做。预期效用计算假定每个选择的信息都是完美的。这里的情况并非如此。
“It sounds like you don’t have enough information,” I said, near the end of our call. “You already know what it’s like to work at a FAANG company, and you're currently interning at a latter-stage startup. But you don’t know what it’s like to do an early-stage startup, and you can't easily figure it out, because you don't have a good idea for a startup right now. Plus, you don't know what it’s like to work at a prop firm. So why not spend a year or two doing each of those things? Just to get that information?”
"听起来你掌握的信息还不够多，"电话快结束时，我说。"你已经知道在一家 FAANG 公司工作是什么样子，而且你目前正在一家处于后期阶段的初创公司实习。但你不知道早期初创公司是什么样的，而且你也不容易搞清楚，因为你现在还没有一个好的初创公司的想法。另外，你也不知道在道具公司工作是什么样子。那为什么不花一两年时间去做这些事情呢？就为了获得这些信息？"
“Hmm,” my friend said, thinking.
"嗯，"我的朋友想了想说。
“I say this because I think the decision will be easier a few years down the road, once you have more information. And a year or two isn’t much, given the long arc of an average career.”
"我之所以这么说，是因为我认为几年之后，一旦你掌握了更多的信息，做出决定就会容易得多。考虑到普通职业生涯的漫长弧线，一两年并不算长。
2. Bayesian Analysis in the Office
2.办公室中的贝叶斯分析
Bayesian updating is the method of ‘holding a belief, and then updating that belief as new information emerges’. It is a fantastic technique to use when making judgments of the world. I’ve written about this approach before, in my series on putting mental models to practice; I’ve also described how Superforecasters use Bayesian analysis to come up with well-calibrated forecasts of the future.
贝叶斯更新法是一种 "保持一种信念，然后随着新信息的出现更新这种信念 "的方法。在对世界做出判断时，这是一种非常好的技术。我曾经在 "心智模型实践 "系列中介绍过这种方法；我还描述过超级预测师如何利用贝叶斯分析法对未来做出精确的预测。
But good analysis and good forecasting isn’t the same thing as effective action.
但是，好的分析和好的预测并不等于有效的行动。
Let’s imagine, for instance, that you have a colleague who is constantly coming in late. The boss doesn’t appear to care. You don’t really understand what’s going on; you think that it’s a little bizarre that nobody talks about this. You conclude that your colleague is lazy but that there is something political going on, which makes it acceptable for him to come in late but not for you to do so.
例如，假设你有一位经常迟到的同事。老板似乎并不在意。你不太明白发生了什么事；你觉得没人谈论这件事有点奇怪。你得出的结论是，你的同事很懒，但这里面有政治因素，他迟到是可以接受的，但你迟到就不行了。
The Bayesian model will tell you to examine your priors, to calculate the probability that your chosen explanation is the right one. It then tells you to watch for new pieces of information that might shift your % confidence in that belief. This is all well and good, but note that Bayesian updating was built for the integration of new information; it doesn’t say anything about the generation of new information.
贝叶斯模型会告诉你检查你的先验，计算你所选择的解释是正确解释的概率。然后，贝叶斯模型会告诉你注意新的信息，这些信息可能会改变你对该信念的信心百分比。这一切都很好，但请注意，贝叶斯更新是为整合新信息而建立的，它对新信息的产生只字未提。
Test subjects in decision experiments may sit back and expect to have information revealed to them over the course of said experiment. Geopolitical forecasters cannot expect to influence the events they are forecasting. But if you are a real-world decision maker, you are in a different situation from these people. You may choose to analyse, or you may choose to act. In other words, there is a real trade-off that you're making here: time spent doing Bayesian analysis might actually be better spent acting, in order to unearth new information!
决策实验中的受试者可能会坐等信息在实验过程中显现出来。地缘政治预测者不能指望影响他们所预测的事件。但如果你是现实世界的决策者，你的处境就与这些人不同了。你可以选择分析，也可以选择行动。换句话说，你在这里需要做出真正的权衡：为了发现新信息，花在贝叶斯分析上的时间可能会比花在行动上的时间更好！
Let’s return to our example of the perpetually late colleague. Can you do something to generate new information? The answer is yes, you can. You may, for instance, arrange lunch with said person, and then ask head-fake questions to determine what’s going on in their life. Perhaps this generates more questions than answers, in which case you may drop back down to Bayesian analysis. But perhaps it results in an answer that’s so unambiguously true that you may save yourself the trouble: “Oh, my wife and I just had a baby, and I’m opting to come into the office because Jon wants me around for this deployment; once this is done I’m going to take my paternity leave and vanish for a month.”
让我们回到 "永远迟到的同事 "这个例子。你能做些什么来产生新信息吗？答案是肯定的，可以。比如说，你可以安排与对方共进午餐，然后提出一些假问题，以确定对方的生活情况。也许这样做产生的问题会多于答案，在这种情况下，你可以退回到贝叶斯分析法。但也许这样得到的答案是如此明确的真实，以至于你可以省去自己的麻烦："哦，我妻子和我刚生了个孩子，我选择来办公室是因为乔恩希望我参与这次部署；一旦部署完成，我就要休陪产假，然后消失一个月"。
The Cost of Decision Analysis
决策分析的成本
The main problem with the decision making frameworks you read about in books and blog posts is that they all come from the field of judgment and decision making, which in turn has its roots in rational choice theory, which in turn comes from economics. In these academic disciplines, the assumption for rational choice is that you have a bunch of options laid out in front of you, and you must pick from one of them. This picking usually happens in an environment of perfect information.
你在书本和博文中读到的决策框架的主要问题在于，它们都来自判断和决策领域，而判断和决策领域又源于理性选择理论，而理性选择理论又源于经济学。在这些学科中，理性选择的假设是，你面前有一堆选项，你必须从中挑选一个。这种选择通常发生在信息完善的环境中。
If you pick well (meaning that you’ve maximised your ‘utility’), you are said to have ‘acted rationally’. If you’ve picked badly, you are said to have acted in an ‘irrational’ manner.
如果你选得好（意味着你的 "效用 "最大化），你就被称为 "理性行事"。如果你选得不好，就可以说你的行为是 "非理性的"。
It is tempting to read this research and then conclude that the frameworks and models are immediately applicable to the real world. But in reality, the models are somewhat limited because the real world doesn’t share all the assumptions that the models must make.
读完这些研究，我们很容易得出这样的结论：这些框架和模型可以立即应用于现实世界。但实际上，这些模型有一定的局限性，因为现实世界并不与模型必须做出的所有假设相同。
For starters, you often have more choices available to you than are laid out in front of you. Some of these choices may be occluded by uncertainty, or are reachable only by creative problem solving. Other times, they are hidden due to lack of information. For instance, perhaps my friend has better options available to him; perhaps he was limiting himself to only three career choices based on what he currently knows.
首先，你可以做出的选择往往比摆在你面前的更多。其中一些选择可能被不确定性所遮蔽，或者只有通过创造性的问题解决方法才能实现。还有一些时候，由于缺乏信息，它们被隐藏了起来。例如，也许我的朋友有更好的选择；也许他根据自己目前所了解的情况，将自己的职业选择限制在三个。
Second, decisions in the real world are often time sensitive — the sooner you act, the more value you realise from having acted (but often the precise amount of that value is also occluded by uncertainty). Most decision-making frameworks don't take time selection into account, since there is no need to model time sensitivity in decision experiments. But in the real world, the utility of each choice may sometimes depend on the decisiveness with which you act on your analysis.
其次，现实世界中的决策往往具有时间敏感性--行动得越早，实现的价值就越大（但价值的确切数额往往也被不确定性所掩盖）。大多数决策框架都不考虑时间选择，因为没有必要在决策实验中模拟时间敏感性。但在现实世界中，每个选择的效用有时可能取决于你根据分析结果果断采取行动的程度。
Third, and most importantly, action often generates new information, which then allows you to make better decisions. In other words, there is often a cost associated with doing decision analysis, but the frameworks do not take that cost into account.
第三，也是最重要的一点，行动往往会产生新的信息，从而让你做出更好的决策。换句话说，进行决策分析往往需要付出代价，但这些框架并没有考虑到这一成本。
In fact, this third observation — that action generates new information, which then allows you to make better decisions — is a pretty powerful one. As it turns out, none of the decision making frameworks from the judgment and decision making literature will tell you when you should stop analysing, and when you should act instead. The reason they do not do so is because these frameworks were originally designed for use in economic modelling. In decision experiments, you do not typically expect a participant to act aggressively in order to gain more information from their environment — you expect them to do the analysis!
事实上，第三个观点--行动会产生新的信息，从而让你做出更好的决策--是一个非常有力的观点。事实证明，判断和决策文献中的决策框架都不会告诉你什么时候应该停止分析，什么时候应该采取行动。之所以没有这样做，是因为这些框架最初是为经济建模而设计的。在决策实验中，你通常不会期望参与者为了从环境中获取更多信息而积极行动，而是期望他们进行分析！
I should note that this is not a particularly novel set of observations to make. The three critiques I've laid out above are old criticisms of the judgment and decision making field; psychologist Jonathan Baron takes great pains to include them in his seminal textbook of the subject.
我要指出的是，这并不是一套特别新颖的观点。我在上文提出的三点批评，是判断与决策领域的老生常谈；心理学家乔纳森-巴伦（Jonathan Baron）在他的这一主题的开创性教科书中，不厌其烦地提出了这些批评。
And the observations are also quite obvious if you know where to look. Watch any group of entrepreneurs for a long enough period of time, for instance, and you would notice that the best entrepreneurs aren’t necessarily the best calibrated Bayesian updaters or expected utility calculators. Instead, the best entrepreneurs tend to have a mix of bias-to-action and fast adaptation in response to new information.
如果你知道如何观察，这些观察结果也是显而易见的。例如，长期观察任何一群企业家，你就会发现，最优秀的企业家并不一定是最好的贝叶斯更新者或预期效用计算者。相反，最优秀的创业者往往既偏重行动，又能快速适应新信息。
(A Chinese businessman we had done business with once put it to me like this: “Why you think so much? Just act first! Then you watch and see what happens. Maybe the customer don't like it. Or maybe your competitor do something to you because you do this. But then you know more than if you just sit here and think think think!” )
(一位与我们有生意往来的中国商人曾这样对我说）："你想那么多干什么？先行动起来！然后你就看着，看看会发生什么。也许顾客不喜欢。或者你的竞争对手会因为你这样做而对你不利。但是，你知道的比你坐在这里想东想西要多！")
Why is this the case? Like well-adapted predators, good entrepreneurs are able to adapt their behaviours to match the contours of reality, and the contours of reality in business seem to be:
为什么会这样呢？就像适应性强的掠食者一样，优秀的企业家能够调整自己的行为，以适应现实的轮廓，而商业现实的轮廓似乎就是这样：
- A sizeable portion of decisions in business are reversible decisions.
商业决策中有相当一部分是可逆决策。 - The information that comes from action is often more valuable than the insight that comes from analysis. This is especially true if there is a high level of uncertainty in your industry.
行动中获得的信息往往比分析中获得的洞察力更有价值。如果您所在的行业存在很大的不确定性，情况就更是如此。
To put this simply: analysis has its limits. Expected utility calculations may tell you how to pick the best option from a suite of limited options, in an environment of perfect information. Bayesian updating tells you how to update your beliefs when presented with new information. But neither technique has anything to say about how to act to generate the best options, or the best information. It shouldn’t surprise us, then, that sometimes the people who act quickly and remain adaptive are more likely to win than those who perform the best Bayesian analysis in the world.
简单地说：分析有其局限性。预期效用计算可以告诉你，在信息完善的环境下，如何从一系列有限的选择中选出最佳方案。贝叶斯更新法则告诉你在遇到新信息时如何更新你的信念。但是，这两种技术都没有告诉你如何行动才能产生最佳选择或最佳信息。因此，我们不应该感到惊讶的是，有时行动迅速并保持适应性的人比那些进行了世界上最好的贝叶斯分析的人更有可能获胜。
Heuristics for Acting 行动启发法
The title of this blog post comes from Brian Armstrong, the founder and CEO of Coinbase. In his interview with investor Patrick O’Shaughnessy, Armstrong says:
这篇博文的标题来自 Coinbase 创始人兼首席执行官布莱恩-阿姆斯特朗（Brian Armstrong）。在接受投资者帕特里克-奥萧纳西（Patrick O'Shaughnessy）的采访时，阿姆斯特朗说道：
It doesn't even matter what you do as long as you do something, because that's my other favourite quote, is “action produces information.” So at a certain point, you got to stop pontificating about this stuff and just try something, anything. You're going to be embarrassed by the V1 until you go out there and you create. That's part of the product development process, is just dramatically scaling back kind of the ambition and the feature set and everything to rapidly iterate and prototype these things, but go do anything. The first thing you try is almost guaranteed not to work. So don't give up, just go try the next thing, and the next thing, and the next thing. That's the only way that new products and companies ever get created in the world. You got to put a lot of shots on goal to get one to eventually work.
你做什么并不重要，只要你有所行动，因为这是我最喜欢的另一句话："行动产生信息"。所以到了一定程度，你就不能再喋喋不休地讨论这些东西了，而是要去尝试一些东西，任何东西。在你走出去创造之前，V1 会让你感到尴尬。这也是产品开发过程的一部分，就是要大幅缩减野心、功能集和所有东西，以便快速迭代和制作原型。你尝试的第一件事几乎肯定不会成功。所以不要放弃，去尝试下一件事，下一件事，再下一件事。这是世界上新产品和新公司诞生的唯一途径。你必须多次射门，才能最终成功。
If the frameworks from decision science are limited in their usefulness, then perhaps we should look to actual practitioners to see how they manage the tension between analysis and action.
如果决策科学的框架作用有限，那么我们或许应该向实际工作者学习，看看他们是如何处理分析与行动之间的矛盾的。
As it turns out, there are many examples from businesspeople and product managers and practitioners. You merely have to know how to look. Here are three.
事实证明，商务人士、产品经理和从业人员都有很多这样的例子。你只需知道如何寻找。这里有三个。
Scott Berkun, on Product Bets
斯科特-伯肯（Scott Berkun），关于产品投注
In The Year Without Pants, veteran product manager Scott Berkun talks about a major bet he had to make with his team at Automattic:
在《没有裤子的一年》（The Year Without Pants）一书中，资深产品经理斯科特-伯昆（Scott Berkun）讲述了他与 Automattic 团队不得不做出的一次重大赌注：
Part of the reason that perfect decision formulas can't exist is that you never know if you're buying too much or too little insurance. Did you see the right doctor for your elbow? Did you ask the right questions? You can make the correct decision in the wrong way. One risk with our plan B was that two weeks wasn't enough. We might need to spend months to improve even one weak spot. Fear of this uncertainty motivates people to spin their wheels for days considering all the possible outcomes, calculating them in a spreadsheet using utility cost analysis or some other fancy method that even the guy who invented it doesn't use.
完美决策公式之所以不存在，部分原因在于你永远不知道自己买的保险是多了还是少了。你的肘部看对医生了吗？你问对问题了吗？你可以用错误的方式做出正确的决定。我们的 B 计划有一个风险，那就是两周时间是不够的。我们可能需要花几个月的时间来改善哪怕一个薄弱点。对这种不确定性的恐惧促使人们花上几天的时间考虑所有可能的结果，在电子表格中使用效用成本分析或其他连发明者都不用的花哨方法进行计算。
But all that analysis just keeps you on the sidelines. Often you're better off flipping a coin and moving in any clear direction. Once you start moving, you get new data regardless of where you're trying to go. And the new data makes the next decision and the next better than staying on the sidelines desperately trying to predict the future without that time machine.
但是，所有的分析都只会让你保持观望。通常情况下，你最好掷硬币决定前进的方向。一旦你开始行动，无论你想去哪里，你都会得到新的数据。有了新的数据，下一个决策和下下一个决策就会变得更好，而不是在没有时光机的情况下，还在一旁拼命预测未来。
In the book, you'll learn that Berkun flipped the coin ... and got lucky. But even if he didn't, that would have been ok — as Berkun points out, his team would have course corrected anyway, when it became clear it was the wrong path for their project. The point was to make the coin flip, and to move on.
在书中，你会了解到伯昆掷硬币......获得了幸运。但即使没有，也没关系--正如伯昆所指出的，他的团队无论如何都会改正错误，因为他们已经意识到这对他们的项目来说是一条错误的道路。关键是要掷出硬币，然后继续前进。
Gary Klein and the Zone of Indifference
加里-克莱因与 "冷漠区
This begs a natural question: how do you know when it's better to 'flip a coin and move in any clear direction’, as Berkun puts it? Psychologist Gary Klein, who works primarily with the military, has a heuristic he calls ‘accepting the zone of indifference.’
这就引出了一个很自然的问题：你怎么知道什么时候 "掷硬币，朝着任何明确的方向前进 "会更好呢？心理学家加里-克莱因（Gary Klein）主要为军队工作，他有一个启发式方法，称之为 "接受冷漠区"。
The zone of indifference is when you cannot tell which decision is the best option. Klein writes:
冷漠区是指你无法判断哪个决定是最佳选择。克莱因写道
If you had to compare two options, one of which is outstanding and the other of which is terrible, you wouldn’t need to do any analysis. It would be an easy choice. As the two options get closer and closer together in their attractiveness, the decision gets harder. (...) In the example of purchasing a used car, we can see that the three options are all very close—they each have comparable strengths and weaknesses. There just isn’t much that differentiates them. The options were so close together that simply flipping a coin would have been sufficient. (...) I call this the zone of indifference problem.
如果你必须比较两个选择，一个很出色，另一个很糟糕，你不需要做任何分析。这将是一个简单的选择。当两个选项的吸引力越来越接近时，做出决定就变得越来越困难。(......)在购买二手车的例子中，我们可以看到三个选项都非常接近--它们各自的优缺点都差不多。它们之间的区别并不大。这些选项如此接近，以至于只需掷一枚硬币就足够了。(......)我把这称为 "冷漠区 "问题。
Recognising that certain decisions are in the zone of indifference is a good way to save decision-making time: imagine, for instance, that you are leading a meeting where you have to decide five things in 30 minutes. A brutally effective way to get through as many of those decisions as possible is to use the zone of indifference to rule out certain decisions, which would then allow you the time to focus on only the most important, most tractable problems.
认识到某些决策属于 "冷漠区"，是节省决策时间的好方法：比如，想象一下，你正在主持一个会议，你必须在 30 分钟内决定五件事。要想尽可能多地通过这些决策，一个非常有效的方法就是利用 "冷漠区 "来排除某些决策，这样你就有时间只关注最重要、最容易解决的问题。
Klein continues: 克莱因继续说道：
We usually think that the goal of decision making is always to pick the best choice. There are few decisions more important than on the battlefield or on the fireground, where lives are at stake. Yet military leaders and fireground commanders recognize that it is better to make a good decision fast and prepare to execute it well rather than agonizing over a “perfect” choice that comes too late. We can rarely know what is the best choice, and the quest for a best choice can drive us to obsess over inconsequential details. How often do we get ourselves trapped into splitting hairs, to find the very best option out of a set of perfectly good choices? Better to make your goal one of selecting a good option that you can live with. If one option emerges as the clear winner, fine. If two or more options wind up in the zone of indifference, that’s fine too—just pick one of them and move on. If you can accept the impossibility of making the “right” choice, you can free yourself from unnecessary turmoil and wasted time.
我们通常认为，决策的目标总是选择最佳方案。在战场或火场上，几乎没有什么决策比这更重要，因为这关系到生命的安危。然而，军事领导人和火场指挥官都认识到，与其为来不及做出的 "完美 "选择而苦恼，不如迅速做出正确的决定，并做好充分的准备来执行它。我们很少能知道什么是最佳选择，而对最佳选择的追求会驱使我们纠结于无关紧要的细节。为了从众多完美的选择中找出一个最好的，我们常常会陷入 "分毫不差 "的境地。最好的办法就是选择一个你能接受的好方案。如果有一个方案明显胜出，那很好。如果有两个或更多的选择处于 "无所谓 "的状态，那也没关系--只需选择其中一个，然后继续前进。如果你能接受做出 "正确 "选择的不可能性，你就能把自己从不必要的混乱和浪费时间中解脱出来。
Jeff Bezos’s Reversible And Irreversible Decisions
杰夫-贝索斯的可逆和不可逆决定
Amazon CEO Jeff Bezos has a remarkably similar, if simpler formula: if the decision is reversible, act quickly, and delegate decision rights. If the decision isn't, then analyse all you want.
亚马逊首席执行官杰夫-贝索斯（Jeff Bezos）有一个非常相似的、甚至更简单的公式：如果决策是可逆的，就迅速采取行动，并下放决策权。如果决策不可逆转，那就尽情分析。
He describes this heuristic in his 2015 shareholder letter:
他在 2015 年的股东信中描述了这种启发式：
Some decisions are consequential and irreversible or nearly irreversible — one-way doors — and these decisions must be made methodically, carefully, slowly, with great deliberation and consultation. If you walk through and don’t like what you see on the other side, you can’t get back to where you were before. We can call these Type 1 decisions.
有些决定是后果严重、不可逆转或几乎不可逆转的--单向门--这些决定必须有条不紊、小心谨慎、缓慢地做出，并经过深思熟虑和协商。如果你走了过去，却不喜欢另一边的景象，你就无法回到原来的位置。我们可以称之为第一类决定。
But most decisions aren’t like that — they are changeable, reversible — they’re two-way doors. If you’ve made a suboptimal Type 2 decision, you don’t have to live with the consequences for that long. You can reopen the door and go back through. Type 2 decisions can and should be made quickly by high judgment individuals or small groups.
但大多数决定都不是这样的--它们是可改变、可逆转的--它们是双向的。如果你做了一个次优的第二类决定，你不必承受那么长时间的后果。你可以重新打开这扇门，回到过去。第二类决策可以也应该由高判断力的个人或小组快速做出。
As organisations get larger, there seems to be a tendency to use the heavy-weight Type 1 decision-making process on most decisions, including many Type 2 decisions. The end result of this is slowness, unthoughtful risk aversion, failure to experiment sufficiently, and consequently diminished invention*. We’ll have to figure out how to fight that tendency.
随着组织规模的扩大，人们似乎倾向于在大多数决策（包括许多第 2 类决策）中使用重量级的第 1 类决策程序。这样做的最终结果是行动迟缓、不假思索地规避风险、无法进行充分的实验，从而削弱了发明创造*。我们必须想办法与这种倾向作斗争。
*The opposite situation is less interesting and there is undoubtedly some survivorship bias. Any companies that habitually use the light-weight Type 2 decision-making process to make Type 1 decisions go extinct before they get large.
*相反的情况就不那么有趣了，无疑存在一些幸存者偏差。任何习惯于使用轻量级的第二类决策过程来做出第一类决策的公司，在做大之前就已经灭绝了。
I can’t make it more concise than that.
我没法说得更简洁了。
Originally published , last updated .
最初发布于 2020 年 8 月 25 日，最后更新于 2021 年 7 月 1 日。
