How Experts Sensemake

This is Part 2 of a short series on sensemaking. You may read Part 1 here.这是关于意义建构的短系列文章的第二部分。你可以在这里阅读第一部分。

In the previous instalment we talked about one way to make sense of AI (without losing your head). At the end of that piece I wrote that the approach is a good start, but it is not enough. In order to examine why, however, we need to introduce more concrete language about how humans make sense of situations. In particular, we want to know how experts sensemake differently from novices. Only after introducing these new ideas will we be able to talk about how to improve at sensemaking when faced with an uncertain new technology.在上一篇文章中，我们讨论了一种理解人工智能的方法（而不至于迷失方向）。在那篇文章的结尾，我写道这种方法是一个好的开始，但还不够。然而，为了探究原因，我们需要引入更具体的语言来描述人类如何理解情境。特别是，我们想知道专家与新手在意义建构上的不同之处。只有在引入这些新概念之后，我们才能讨论在面对不确定的新技术时如何改进意义建构。

In this instalment we will examine the best theory for sensemaking that we currently have. This theory comes out of research done for the US military, primarily funded by a contract with the Army Research Institute for the Behavioral and Social Sciences. It has proven itself useful in all sorts of domains, but it is a relatively young theory — which is to say that it has survived falsification for only two decades. (This is good enough for me, though you might want to calibrate your expectations appropriately).在本篇文章中，我们将审视目前最好的意义建构理论。这一理论源于为美国军方进行的研究，主要由陆军行为与社会科学研究所的合同资助。它已在各个领域证明了自己的实用性，但这是一个相对年轻的理论——也就是说，它仅经受住了二十年的证伪考验。（这对我来说已经足够，不过你可能需要适当调整你的期望。）

If we take a step back, however, you can easily imagine why the US military would be interested in a theory of sensemaking. A huge part of intelligence-gathering and warfighting is making sense of ambiguous information, under conditions of extreme uncertainty. (It’s called the ‘fog of war’ for a reason!)然而，如果我们退一步思考，你很容易想象为什么美国军方会对意义建构理论感兴趣。情报收集和作战的很大一部分是在极端不确定的条件下理解模糊信息。（这被称为“战争迷雾”，原因不言而喻！）

And what is true for sensemaking in war is true also for sensemaking in business and in investing. The sensemaking processes that skilled warfighters use in battle is the same one that a business leader uses when deciding what to do when faced with a new competitive threat. It is the same process that an investor uses when coming up with an investment thesis for a specific company (or when the investor decides that a previous thesis has been invalidated.) And it is the same process technology leaders and engineering managers must use when faced with a revolutionary new technology.战争中意义建构的道理同样适用于商业和投资中的意义建构。熟练的战士在战斗中使用的意义建构过程，与商业领袖在面对新的竞争威胁时决定如何行动所使用的过程相同。这也是投资者在制定特定公司的投资论点（或当投资者认为先前的论点已失效）时所使用的过程。同样，技术领导者和工程经理在面对革命性新技术时也必须使用这一过程。

If you’re interested in only what is immediately applicable, you might scoff at theory. But I assure you that there are plenty of things here that you can use. If nothing else, it will give you better language for your own cognition — concepts that are more useful than accounts like ‘selection bias’ or ‘confirmation bias’. That is: you will be able to notice things about your own thinking that you won’t before reading this piece.如果你只对直接可用的东西感兴趣，你可能会对理论嗤之以鼻。但我向你保证，这里有很多你可以使用的东西。至少，它会为你自己的认知提供更好的语言——比诸如“选择偏差”或“确认偏差”等描述更有用的概念。也就是说，你将能够注意到自己思维中以前未曾察觉的东西。

And most importantly: you will know how to get better. Which means you’ll be better equipped when AI impacts your corner of the world.最重要的是：你将知道如何变得更好。这意味着当人工智能影响你所在的领域时，你将更有准备。

What is Sensemaking?什么是意义建构？

The theory we’re going to examine is the Data-Frame Theory of Sensemaking, originally published in 2007 by Gary Klein, Jennifer K. Phillips, Erica L. Rall, Deborah A. Peluso. (If you are a Commoncog member, you may download a cleaned-up version of this paper in PDF and ePub formats here).我们将要审视的理论是意义建构的数据-框架理论，最初由Gary Klein、Jennifer K. Phillips、Erica L. Rall和Deborah A. Peluso于2007年发表。（如果你是Commoncog会员，可以在此下载该论文的PDF和ePub格式的清理版本。）

Sensemaking is defined as “the deliberate effort to understand events”. For instance, the thinking and actions involved with the question: “how will AI impact my career?” is a form of sensemaking. So is “how may I use AI to gain a competitive advantage against my competitors?” But many other activities are also considered sensemaking, and use the same cognitive processes in the human brain. For example: “where are we right now and how do we get to the train station?” is a sensemaking process, just as “I am a doctor; what is going on with this patient and what should I do next?”意义建构被定义为“理解事件的刻意努力”。例如，围绕“人工智能将如何影响我的职业生涯？”这一问题的思考和行动就是一种意义建构形式。“我如何利用人工智能获得相对于竞争对手的竞争优势？”也是如此。但许多其他活动也被视为意义建构，并且使用人脑中相同的认知过程。例如：“我们现在在哪里，如何到达火车站？”是一个意义建构过程，就像“我是一名医生；这位病人怎么了，我下一步该怎么做？”一样。

To give you a taste of what experts may accomplish when sensemaking, I’ll start with two real world examples, both taken from Klein et al’s paper. Here is example one (all bold emphasis mine):为了让你了解专家在意义建构时可能达到的效果，我将从Klein等人的论文中举两个现实世界的例子。这是第一个例子（所有粗体强调均为我所加）：

During a Marine Corps exercise, a reconnaissance team leader and his team were positioned overlooking a vast area of desert. The fire team leader, a young sergeant, viewed the desert terrain carefully and observed an enemy tank move along a trail and then take cover. He sent this situation report to headquarters. However, a brigadier general, experienced in desert-mechanized operations, had arranged to go into the field as an observer. He also spotted the enemy tank.

But he knew that tanks tend not to operate alone. Therefore, based on the position of that one tank, he focused on likely overwatch positions and found another tank. Based on the section’s position and his understanding of the terrain, he looked at likely positions for another section and found a well-camouflaged second section. He repeated this process to locate the remaining elements of a tank company that was well-camouflaged and blocking a key choke point in the desert. The size and position of the force suggested that there might be other higher and supporting elements in the area, and so he again looked at likely positions for command and logistics elements. He soon spotted an otherwise superbly camouflaged logistics command post. In short, the brigadier general was able to see and understand and make more sense of the situation than the sergeant. He had much more experience, and he was able to develop a fuller picture rather than record discrete events that he noticed.在一次海军陆战队演习中，一名侦察队队长和他的小队被部署在俯瞰大片沙漠地带的位置。这名年轻的班长仔细审视沙漠地形，观察到一辆敌方坦克沿着一条小径移动并随后隐蔽。他将这一情况报告发送给了指挥部。然而，一位在沙漠机械化作战方面经验丰富的准将，安排自己作为观察员进入现场。他也发现了那辆敌方坦克。但他知道坦克通常不会单独行动。因此，基于那辆坦克的位置，他专注于可能的掩护阵地，并找到了另一辆坦克。根据该排的位置和他对地形的理解，他寻找另一个排的可能位置，并发现了一个伪装良好的第二排。他重复这一过程，定位了一个坦克连的其余部分，这些坦克伪装良好，封锁了沙漠中的一个关键隘口。这支力量的大小和位置表明，该地区可能还有其他更高层级的支援单位，于是他再次寻找指挥和后勤单位的可能位置。很快，他发现了一个原本伪装极好的后勤指挥所。简而言之，这位准将能够比班长更深入地观察、理解和把握局势。他拥有更丰富的经验，能够形成更全面的图景，而不是仅仅记录他注意到的离散事件。

And here is example two, which comes from an account of a expert nurse in a Neonatal Intensive Care Unit (NICU) — again, all bold emphasis mine:这是第二个例子，来自一位新生儿重症监护室（NICU）专家护士的叙述——同样，所有粗体强调均为我所加：

This baby was my primary; I knew the baby and I knew how she normally acted. Generally she was very alert, was on feedings, and was off IVs. Her lab work on that particular morning looked very good. She was progressing extremely well and hadn’t had any of the setbacks that many other preemies have. She typically had numerous apnea episodes and then bradys [short for bradycardia — a common, usually temporary slowing of a newborn’s heart rate, frequently caused by immature brain development rather than disease], but we could easily stimulate her to end these episodes. At 2:30 her mother came in to hold her and I noticed that she wasn’t as responsive to her mother as she normally was. She just lay there and half looked at her. When we lifted her arm it fell right back down in the bed and she had no resistance to being handled. This limpness was very unusual for her. On this day, the monitors were fine, her blood pressure was fine, and she was tolerating feedings all right. There was nothing to suggest that anything was wrong except that I knew the baby and I knew that she wasn’t acting normally. At about 3:50 her color started to change. Her skin was not its normal pink color and she had blue rings around her eyes. During the shift she seemed to get progressively grayer. Then at about 4:00, when I was turning her feeding back on, I found that there was a large residual of food in her stomach. I thought maybe it was because her mother had been holding her and the feeding just hadn’t settled as well. By 5:00 I had a baby who was gray and had blue rings around her eyes. She was having more and more episodes of apnea and bradys; normally she wouldn’t have any bradys when her mom was holding her. Still, her blood pressure hung in there. Her temperature was just a little bit cooler than normal. Her abdomen was a little more distended, up 2 cm from early in the morning, and there was more residual in her stomach. This was a baby who usually had no residual and all of a sudden she had 5 cc to 9 cc. We gave her suppositories thinking maybe she just needed to stool. Although having a stool reduced her girth, she still looked gray and was continuing to have more apnea and bradys. At this point, her blood gas wasn’t good so we hooked her back up to the oxygen. On the doctor’s orders, we repeated the lab work. The results confirmed that this baby had an infection, but we knew she was in trouble even before we got the lab work back.这个婴儿是我的主要护理对象；我了解这个婴儿，也知道她通常的行为方式。通常她非常警觉，正在进食，并且已经停止静脉输液。那天早上的化验结果看起来非常好。她进展得非常顺利，没有出现许多其他早产儿常见的挫折。她通常会有多次呼吸暂停发作，然后出现心动过缓（bradys，即心动过缓的简称——新生儿心率常见且通常是暂时的减慢，通常由未成熟的大脑发育而非疾病引起），但我们可以通过刺激轻松结束这些发作。下午2:30，她的母亲来抱她，我注意到她对母亲的反应不如平时灵敏。她只是躺在那里，半睁着眼睛看着母亲。当我们抬起她的手臂时，它立刻落回床上，她对被摆弄没有任何抵抗。这种无力对她来说非常不寻常。那天，监护仪正常，血压正常，她也能很好地耐受喂养。没有任何迹象表明有什么问题，除了我了解这个婴儿，我知道她行为不正常。大约3:50，她的肤色开始变化。她的皮肤不再是正常的粉红色，眼睛周围出现了蓝色环。在值班期间，她似乎逐渐变得灰白。然后大约4:00，当我重新打开她的喂养时，我发现她胃里有大量食物残留。我想也许是因为她母亲抱着她，喂养没有很好地消化。到5:00，这个婴儿已经变得灰白，眼睛周围有蓝色环。她出现越来越多的呼吸暂停和心动过缓发作；通常当她母亲抱着她时，她不会有任何心动过缓。尽管如此，她的血压仍然维持着。体温比正常稍低。腹部比清晨更胀，增加了2厘米，胃里残留物也更多。这是一个通常没有残留物的婴儿，突然之间有了5到9毫升。我们给她用了栓剂，以为她只是需要排便。虽然排便后她的腹围减小了，但她仍然看起来灰白，并且继续出现更多的呼吸暂停和心动过缓。此时，她的血气结果不好，所以我们重新给她接上氧气。根据医生的医嘱，我们重复了化验。结果证实这个婴儿有感染，但我们在拿到化验结果之前就已经知道她出问题了。

Hold these bolded sections in abeyance; we’re going to come back to them.先记住这些加粗的部分；我们稍后会回到它们。

The Data-Frame Theory of Sensemaking starts out with a common-sense observation. Humans notice facts only in the context of a frame. What is a frame? At its most basic level, a frame is a story or a cognitive structure we construct to help explain the situation. Frames organise the relationship between pieces of information in our heads — and in so doing, allow us to use that information. These relationships may include spatial relationships (“where are we on a map?”), causal relationships (stories and scenarios), temporal accounts (stories and scenarios) and functional relationships (‘scripts’ — which in psychology is taken to mean ‘a regularly occurring sequence of events or activities that can be formulated as a template’ (Schank & Abelson, 1977)).意义建构的数据-框架理论从一个常识性观察开始。人类只有在框架的背景下才能注意到事实。什么是框架？在最基本的层面上，框架是我们构建用来帮助解释情境的故事或认知结构。框架组织了我们头脑中信息片段之间的关系——从而让我们能够利用这些信息。这些关系可能包括空间关系（“我们在地图上的哪里？”）、因果关系（故事和场景）、时间关系（故事和场景）以及功能关系（“脚本”——在心理学中，这被理解为“可以公式化为模板的定期发生的事件或活动序列”（Schank & Abelson, 1977））。

The two examples quoted earlier already give us multiple examples of frames. But even before we examine those examples, I suspect this account of ‘data and frame’ should make intuitive sense to you. For instance, have you ever had the following experiences? Let’s say that you’re a programmer. You read an error log and say “ok, I think the problem is such-and-such library …” and after a few minutes of debugging, you turn out to be correct. Meanwhile, your intern looks uncomprehendingly at the error log and spends two hours digging in exactly the wrong place in the codebase. Or, let’s say that you’re an exec in your company. You’re looking at a company dashboard, spot some data that seems odd to you (and only to you!), and a hunch forms in your head. Two dashboard checks, three SQL queries and one conversation later, you discover that 20% of trial customers have been silently dropping off for the past two months because the onboarding flow is broken. Meanwhile, your lead data analyst has completely missed this, despite looking at the raw data every week in preparation for the weekly company-wide data report.前面引用的两个例子已经给了我们多个框架的例子。但即使在审视这些例子之前，我猜想这种“数据和框架”的描述对你来说应该是直观的。例如，你是否有过以下经历？假设你是一名程序员。你阅读错误日志并说“好的，我认为问题出在某某库……”经过几分钟的调试，你发现你是对的。与此同时，你的实习生困惑地看着错误日志，花了两个小时在代码库中完全错误的地方挖掘。或者，假设你是公司的一名高管。你查看公司仪表板，发现一些只有你看起来奇怪的数据，然后你脑海中形成了一个预感。经过两次仪表板检查、三次SQL查询和一次对话，你发现20%的试用客户在过去两个月里因为入职流程出现问题而悄然流失。与此同时，你的首席数据分析师完全忽略了这一点，尽管他每周都在为全公司数据报告准备原始数据。

The difference between you, the experienced programmer, or you, the senior exec, with someone more junior is not the data you are looking at. In both cases your programming intern and your lead data analyst had access to the same information that you did. The difference is the frame with which you evaluated that data. In both cases, you drew inferences based on the causal mental models in your head (i.e. your frame), whilst the more junior person either drew the wrong inferences from the data, or treated it as irrelevant. In simpler terms, you made more sense of the data than your subordinate because of your superior frame construction abilities, which resulted in different information-gathering actions, and eventually different decisions.你，经验丰富的程序员，或你，高级高管，与更初级的人之间的区别不在于你们所查看的数据。在这两种情况下，你的编程实习生和首席数据分析师都能获得与你相同的信息。区别在于你评估数据时所使用的框架。在这两种情况下，你基于头脑中的因果心智模型（即你的框架）进行推断，而更初级的人要么从数据中得出错误的推断，要么将其视为无关紧要。简而言之，你比你的下属从数据中获得了更多的意义，因为你具有更优越的框架构建能力，这导致了不同的信息收集行动，并最终导致不同的决策。

This difference lies at the heart of expertise. I don’t want to put too fine a point on this, because this is important. The observation that experts construct different frames compared to novices is actually profound. Frame construction is the bit of tacit knowledge that matters. It is the bit that accelerated expertise training programs attempt to train for. If that isn’t enough, frame construction is directly linked to insight generation. Insight generation is really important! If you study cases of business strategy, every winning strategy boils down to a few small insights that frame the problem advantageously — which the rest of the strategy is then built around. Richard Rumelt calls this the ‘kernel’. Roger Martin calls this answering the twin questions of “where to play” and “how to win”. The point is: if you can frame the problem you’re facing properly, you have half the problem solved.这种差异是专业知识的核心。我不想过分强调这一点，因为这很重要。专家构建与新手不同的框架这一观察实际上意义深远。框架构建是重要的隐性知识。它是加速专业知识培训项目试图训练的内容。如果这还不够，框架构建与洞察力的产生直接相关。洞察力的产生非常重要！如果你研究商业战略的案例，每一个获胜的战略都可以归结为几个小的洞察力，这些洞察力有利地构建了问题——然后整个战略围绕这些洞察力展开。Richard Rumelt称之为“内核”。Roger Martin称之为回答“在哪里竞争”和“如何获胜”这两个孪生问题。关键是：如果你能正确地构建你所面临的问题，你就解决了一半的问题。

And so you might understand why I believe frame construction is ridiculously important to understand, and to improve at.因此，你可能会理解为什么我认为框架构建对于理解和改进来说极其重要。

For now, let us return to the two examples I listed above. We may describe the different frames in those examples as follows:现在，让我们回到上面列出的两个例子。我们可以将其中不同的框架描述如下：

• Fire team leader: “an enemy tank is moving along the terrain to take cover”火力组组长：“一辆敌方坦克正在沿着地形移动以寻找掩护”
• Brigadier general: “an enemy tank is moving as part of a larger manoeuvre” which is then reframed after he uncovers more information to “the enemy — a tank company — has taken control of a key choke point on the battlefield”准将：“一辆敌方坦克正在作为更大规模机动的一部分移动”，然后在他发现更多信息后重新构建为“敌人——一个坦克连——已经控制了战场上的一个关键隘口”
• NICU nurse: “this premie (prematurely born baby) is doing fine”NICU护士：“这个早产儿情况良好”
• NICU nurse: “this premie has sepsis”NICU护士：“这个早产儿患有败血症”

The frame we pick informs the data we notice. But the data we notice is also used to construct the frame. This implies the relationship between data and frame is recursive: both frame and data affect each other. Let’s examine a third example which demonstrates this relationship (which also comes from Klein et al’s paper):我们选择的框架决定了我们注意到的数据。但我们注意到的数据也用于构建框架。这意味着数据和框架之间的关系是递归的：框架和数据相互影响。让我们审视第三个例子，它展示了这种关系（同样来自Klein等人的论文）：

An accident happened during an Army training exercise. Two helicopters collided. Everyone in one helicopter died and everyone in the other helicopter survived. Our informant, Captain B., was on the battalion staff at the time.

Immediately after the accident, Captain B. suspected that because this was a night mission there could have been some complications due to flying with night-vision goggles that led one helicopter to drift into the other.

Then Captain B. found out that weather had been bad during the exercise, and he thought that was probably the cause of the accident; perhaps they had flown into some clouds at night.

Then Captain B. learned that there was a sling on one of the crashed helicopters, and that this aircraft had been in the rear of the formation. He also found out that an alternate route had been used, and that weather wasn’t a factor because they were flying below the clouds when the accident happened. So Captain B. believed that the last helicopter couldn’t slow down properly because of the sling. The weight of the sling would make it harder to stop to avoid running into another aircraft. He also briefly suspected that pilot experience was a contributing factor, because they should have understood the risks better and kept better distance between aircraft, but he dismissed this idea because he found out that although the lead pilot hadn’t flown much recently, the copilot was very experienced. But Captain B. was puzzled about why the sling-loaded helicopter would have been in trail. It should have been in the lead because it was less agile than the others. Captain B. was also puzzled about the route—the entire formation had to make a big U-turn before landing and this might have been a factor too. So this story, though much different than the first ones, still had some gaps.

Finally, Captain B. found out that the group had not rehearsed the alternate route. The initial route was to fly straight in, with the sling-loaded helicopter in the lead. And that worked well because the sling load had to be delivered in the far end of the landing zone. But because of a shift in the wind direction, they had to shift the landing approach to do a U-turn. When they shifted the landing approach, the sling load had to be put in the back of the formation so that the load could be dropped off in the same place. When the lead helicopter came in fast and then went into the U-turn, the next two helicopters diverted because they could not execute the turn safely at those speeds and were afraid to slow down because the sling-loaded helicopter was right behind them. The sling-loaded helicopter continued with the maneuver and collided with the lead helicopter.在一次陆军训练演习中发生了一起事故。两架直升机相撞。一架直升机上的所有人遇难，另一架直升机上的所有人幸存。我们的信息提供者B上尉当时在营部参谋部任职。事故发生后，B上尉立即怀疑，由于这是一次夜间任务，使用夜视镜飞行可能导致了一些并发症，使得一架直升机漂移到另一架直升机上。然后B上尉发现演习期间天气恶劣，他认为这可能是事故的原因；也许他们在夜间飞入了云层。接着B上尉了解到其中一架坠毁的直升机上有一个吊索，并且这架飞机位于编队的后方。他还发现使用了备用航线，并且天气不是因素，因为事故发生时他们在云层下方飞行。因此B上尉认为最后一架直升机因为吊索而无法适当减速。吊索的重量会使它更难停下来以避免撞上另一架飞机。他还短暂怀疑飞行员经验是一个促成因素，因为他们应该更好地理解风险并保持飞机之间的更大距离，但他否定了这个想法，因为他发现虽然主飞行员最近飞行不多，但副驾驶非常有经验。但B上尉对为什么吊索直升机会在编队后方感到困惑。它应该在前方，因为它不如其他飞机灵活。B上尉也对航线感到困惑——整个编队在着陆前必须做一个大U形转弯，这可能也是一个因素。所以这个故事，尽管与第一个故事大不相同，但仍然存在一些空白。最后，B上尉发现该小组没有排练备用航线。最初的航线是直线飞入，吊索直升机在前方。这效果很好，因为吊索载荷需要投送到着陆区的远端。但由于风向变化，他们不得不改变着陆方式，进行U形转弯。当他们改变着陆方式时，吊索载荷必须放在编队后方，以便载荷可以投送到同一地点。当领头直升机快速进入并开始U形转弯时，接下来的两架直升机转向了，因为它们无法以那些速度安全执行转弯，并且担心减速，因为吊索直升机就在它们后面。吊索直升机继续执行机动，并与领头直升机相撞。

Notice how the protagonist in this example, Captain B., uses data points as anchors to construct possible frames. Each frame serves as a hypothesis: a plausible explanation to make sense of the facts he has been given:注意这个例子中的主角B上尉如何使用数据点作为锚点来构建可能的框架。每个框架都作为一个假设：一个合理的解释，以理解他所获得的事实：

At first, Captain B. had a single datum, the fact that the accident took place at night. He used this as an anchor to construct a likely scenario. Then he learned about the bad weather, and used this fact to anchor an alternate and more plausible explanation.

Next he learned about the sling load, and fastened on this as an anchor because sling loads are so dangerous. The weather and nighttime conditions may still have been factors, but they did not anchor the new explanation, which centered around the problem of maneuvering with a sling load. Captain B.’s previous explanations faded away. Even so, Captain B. knew his explanation was incomplete, because a key datum was inconsistent—why was the helicopter with the sling load placed in the back of the formation?

Eventually, he compiled the anchors: helicopter with a sling load, shift in wind direction, shift to a riskier mission formation, unexpected difficulty of executing the U-turn. Now he had the story of the accident. He also had other pieces of information that contributed, such as time pressure that precluded practicing the new formation, and command failure in approving the risky mission.起初，B上尉只有一个数据点，即事故发生在夜间这一事实。他以此作为锚点构建了一个可能的场景。然后他了解到恶劣天气，并利用这一事实作为锚点构建了一个替代且更合理的解释。接着他了解到吊索载荷，并以此作为锚点，因为吊索载荷非常危险。天气和夜间条件可能仍然是因素，但它们并没有锚定新的解释，新的解释围绕吊索载荷的机动问题展开。B上尉之前的解释逐渐消失。即便如此，B上尉知道他的解释并不完整，因为一个关键数据点不一致——为什么带有吊索载荷的直升机被放在编队后方？最终，他整合了锚点：带有吊索载荷的直升机、风向变化、转向更危险的编队、执行U形转弯的意外困难。现在他有了事故的故事。他还有其他信息片段，例如时间压力使得无法练习新编队，以及指挥层批准危险任务的失败。

Captain B. only stops with his sensemaking process when all the information he has received about the accident is coherent with his frame (in this case a story he constructed to explain the incident). Thus satisfied, he moves on from sensemaking and begins to think about taking action. Note that if he receives a new piece of information that is inconsistent with his current frame and too difficult to explain away, he will drop back to sensemaking once again.B上尉只有在所有关于事故的信息与他的框架（在这种情况下是他构建来解释事件的故事情节）一致时，才会停止意义建构过程。因此感到满意后，他从意义建构转向考虑采取行动。请注意，如果他收到一条与当前框架不一致且难以解释的新信息，他将再次回到意义建构状态。

We now have enough concepts to describe the Data-Frame theory in full.我们现在有足够的概念来完整描述数据-框架理论。

The Data-Frame Theory of Sensemaking意义建构的数据-框架理论

Figure 1 shows the four cycles of the Data-Frame Theory of Sensemaking. We shall examine each of these sensemaking cycles separately, as they involve different strategies:图1显示了意义建构数据-框架理论的四个循环。我们将分别审视每个意义建构循环，因为它们涉及不同的策略：

1. The basic data-frame cycle, where you use data to construct a frame, and you use the frame to evaluate what counts as data.基本数据-框架循环，其中你使用数据构建框架，并使用框架评估什么算作数据。
2. The elaboration cycle — this occurs when you’re committed to an existing frame but want to flesh it out, perhaps because there are gaps in your explanation.细化循环——当你致力于现有框架但希望充实它时发生，可能是因为你的解释中存在空白。
3. The preservation cycle — at some point you might encounter data that is inconsistent with your current frame. There are basically two paths available to you: first, you may reject the data point. Second, if you cannot explain away the data (or if there’s simply too much data that is incongruous), you may choose to abandon your current frame. The first path leads to the preservation cycle, which basically reinforces your existing frame. The second path leads you to …保留循环——在某个时刻，你可能会遇到与当前框架不一致的数据。基本上有两条路径可供选择：首先，你可以拒绝该数据点。其次，如果你无法解释该数据（或者有太多不一致的数据），你可以选择放弃当前框架。第一条路径通向保留循环，这基本上强化了你现有的框架。第二条路径通向……
4. The reframing cycle — your current frame is inadequate and you want to reframe. Here you have two options: either you construct (or take) a second frame in parallel to your current frame, and compare between them to see which you want to commit to. Or you decide to drop your current frame completely and construct a new one from a new set of anchors.重新构建循环——你当前的框架不充分，你想要重新构建。这里你有两个选择：要么你构建（或采用）与当前框架并行的第二个框架，并在它们之间进行比较，以决定你致力于哪一个。要么你决定完全放弃当前框架，并从一组新的锚点构建一个新框架。

All of this seems obvious — you might already recognise this in your own cognition — but bear with me. We need to go through each of these cycles in order to get to the good stuff.所有这些似乎都很明显——你可能已经在自己认知中认识到这一点——但请耐心听我说。我们需要逐一经历每个循环，才能进入精彩的部分。

The Basic Data-Frame Cycle基本数据-框架循环

The basic sensemaking act is the data-frame symbiosis. As previously discussed, your frame informs what is counted as data, but the data you examine extends and modifies your frame. In practice, what happens is that you switch back and forth between examining data and constructing frame.基本的意义建构行为是数据-框架共生。如前所述，你的框架决定了什么被视为数据，但你审视的数据扩展并修改了你的框架。在实践中，发生的情况是你在审视数据和构建框架之间来回切换。

Let’s talk details.让我们谈谈细节。

• Frames are constructed from maximum three to four data points, which are used as anchors. If you’re designing a decision support system, this number is useful to know.框架由最多三到四个数据点构建而成，这些数据点用作锚点。如果你正在设计决策支持系统，这个数字很有用。
• Notice that data points do NOT ‘arrive’ fully-formed, they must be interpreted and chosen as anchors. We say that data is always constructed — you have to choose to interpret certain facts and events as data. For instance, think about my hypothetical example of the lead data analyst, who did not notice an abnormality in a customer metric. To you, the experienced exec, the exception was notable. To the analyst, it was just noise. He didn’t even see it; it was not data to him.注意，数据点并非“完全形成”地到达，它们必须被解释并选择作为锚点。我们说数据总是被构建的——你必须选择将某些事实和事件解释为数据。例如，想想我假设的首席数据分析师的例子，他没有注意到客户指标中的异常。对你这位经验丰富的高管来说，这个异常是值得注意的。对分析师来说，它只是噪音。他甚至没有看到它；对他来说，它不是数据。
• One major problem that arises is frame fixation — that is, you stick to an inadequate frame when you should reframe instead. If you construct the wrong frame, perhaps because you’ve picked a flawed anchor, this will compromise your ability to solve the problem you’re facing. The earlier a flawed anchor is introduced in the sensemaking process, the more difficult it will be to recover. However, experts recover faster than novices. We’ll talk about how in a bit.出现的一个主要问题是框架固化——也就是说，当你应该重新构建时，你却坚持一个不充分的框架。如果你构建了错误的框架，可能是因为你选择了一个有缺陷的锚点，这将损害你解决问题的能力。在意义建构过程中越早引入有缺陷的锚点，就越难恢复。然而，专家比新手恢复得更快。我们稍后会讨论如何做到。
• A data point that is used as an anchor for one frame is difficult to be reused in another frame. This is very important, and one of the reasons frame fixation occurs longer than it should. Experts in different domains develop different strategies to account for this. We’ll talk about some of those methods later too.用于一个框架锚点的数据点很难在另一个框架中重复使用。这一点非常重要，也是框架固化持续时间过长的原因之一。不同领域的专家会制定不同的策略来解决这个问题。我们稍后也会讨论其中一些方法。
• You can have a repertoire of ready-made frames stored in your head. For instance, the nurse in Example Two had a ‘sepsis’ frame ready, which she picked up and elaborated in parallel whilst still holding the ‘baby is doing fine’ frame. The repertoire of frames you have affects what data elements you consider. Note that this nurse could not have done “here is a checklist of sepsis symptoms” because sepsis is complex, and symptoms differ with every premature baby. She had to do frame construction (using fragments of cases she’s seen before) and then do frame comparison between “the baby is ok” and “the baby has sepsis” frames because she wasn’t sure.你可以在头脑中存储现成框架的储备库。例如，例子二中的护士有一个“败血症”框架，她同时拿起并细化这个框架，同时仍然持有“婴儿情况良好”的框架。你拥有的框架储备库会影响你考虑的数据元素。请注意，这位护士不能简单地使用“败血症症状清单”，因为败血症很复杂，症状因每个早产儿而异。她必须进行框架构建（使用她以前见过的病例片段），然后进行框架比较，在“婴儿没事”和“婴儿有败血症”框架之间进行比较，因为她不确定。
• It’s tempting to say that you should train students by teaching them ready-made frames. But this is not always ideal — in dynamic domains like war or business or investing, it is common for practitioners to construct new frames in response to situations nobody has seen before. Experts, naturally, are better at adapting to this uncertainty than novices. How are frames constructed in such situations, then? Frames are constructed, on the fly, from fragments of knowledge and fragments of causal mental models about the domain. The more fragments you have, the better and richer your frames. This is fundamentally the same claim as Commoncog’s Calibration Case Method. (The name of the theory underpinning our case method is ‘Cognitive Flexibility Theory’ which was developed by Professor Rand Spiro and his collaborators in 1988; the Data-Frame Theory cites Spiro’s work).人们很容易认为应该通过教授现成框架来培训学生。但这并不总是理想的——在战争、商业或投资等动态领域，从业者通常会针对前所未见的情况构建新框架。自然，专家比新手更善于适应这种不确定性。那么，在这种情况下，框架是如何构建的呢？框架是即时构建的，来自知识片段和关于该领域的因果心智模型片段。你拥有的片段越多，你的框架就越好、越丰富。这基本上与Commoncog的校准案例方法相同。（支撑我们案例方法的理论称为“认知灵活性理论”，由Rand Spiro教授及其合作者于1988年提出；数据-框架理论引用了Spiro的工作。）
• When constructing a frame, the type of thinking used is most accurately described as abductive reasoning. Abductive reasoning is ‘inference to the best explanation’ — you observe something surprising, then settle on what you think is the most likely cause. Experts also use logical deduction during sensemaking (“all As have Xs, John is an A, therefore John has X”), but the dominant thinking method is abductive. In practice, what you’re doing during frame constructions looks like proposing and then throwing out possible frames, as with Captain B. in Example Three above.在构建框架时，所使用的思维类型最准确地描述为溯因推理。溯因推理是“对最佳解释的推断”——你观察到一些令人惊讶的事情，然后确定你认为最可能的原因。专家在意义建构过程中也使用逻辑演绎（“所有A都有X，John是A，因此John有X”），但主导的思维方法是溯因的。在实践中，你在框架构建过程中所做的工作看起来像是提出然后抛弃可能的框架，就像上面例子三中的B上尉一样。
• You stop sensemaking when data and frame is brought into congruence. You will usually feel a sense of satisfaction when you manage to figure out a frame that explains all the data you have. Sensemaking doesn’t go on forever. Once you feel you’ve got a grip on a situation, you move on from sensemaking to downstream decisions. You only drop back to sensemaking when enough data emerges that cannot be explained by your current frame — a ‘frame breaking’ event.当数据和框架达成一致时，你停止意义建构。当你设法找到一个解释所有数据的框架时，你通常会感到满意。意义建构不会永远持续下去。一旦你觉得掌握了情况，你就会从意义建构转向后续决策。只有当出现足够多无法用当前框架解释的数据时——一个“框架打破”事件——你才会回到意义建构。

The Elaboration Cycle细化循环

Sometimes you have committed to an initial frame, but you still have unanswered questions. One common move during sensemaking is to take action to fill in gaps in your current frame. You may:有时你已经致力于一个初始框架，但你仍然有未解答的问题。意义建构过程中的一个常见举措是采取行动填补当前框架中的空白。你可以：

• Add new gaps or fill open gaps in your understanding as you examine new data points.在审视新数据点时，添加新的空白或填补现有空白。
• Take action to generate new data.采取行动生成新数据。
• Seek out new data寻找新数据。
• Discover new data or new relationships through experimentation通过实验发现新数据或新关系。
• Ignore or discard data忽略或丢弃数据。

We already have one example of frame elaboration (the brigadier general in Example One), but I’ll illustrate with an example from the previous instalment in this series. One possible frame in the current AI revolution is “it is possible to produce complex, usable software at high velocity with little to no manual human intervention, but it requires around six months of iterating on the agent harness.” I’ve mentioned this frame in passing in the previous instalment, as an example of “What are the possible outcomes here?” But there remain many unanswered questions. For instance: are these teams actually producing good software? What kinds of software? How do I know? And then: how are these teams accomplishing this? What goes into their harness engineering? What do they know that I don’t?我们已经有一个框架细化的例子（例子一中的准将），但我将用本系列前一篇文章中的一个例子来说明。当前人工智能革命中的一个可能框架是“有可能以高速度生产复杂、可用的软件，几乎不需要人工干预，但需要大约六个月的时间迭代代理工具链。”我在前一篇文章中顺便提到了这个框架，作为“这里可能的结果是什么？”的一个例子。但仍然存在许多未解答的问题。例如：这些团队真的在生产好的软件吗？什么样的软件？我怎么知道？然后：这些团队是如何实现这一点的？他们的工具链工程包括什么？他们知道哪些我不知道的东西？

In response, you may take action to elaborate the frame:作为回应，你可以采取行动来细化框架：

• You may seek out teams who have successfully executed this approach and ask them to explain what they’ve done and describe how it has turned out. (Which I briefly suggested in Part 1).你可以寻找成功执行这种方法的团队，并请他们解释他们做了什么以及结果如何。（我在第一部分中简要建议过。）
• You may start experimenting with your own agent harnesses to give you a better grounding in the landscape of possible solutions.你可以开始实验你自己的代理工具链，以便更好地了解可能解决方案的格局。
• You may start to actively seek out data points that may point the way to this outcome. Notice that what I consider data is affected by my frame. For instance, after forming this frame, John Regehr’s Zero Degree-of-Freedom LLM (Large Language Model) Coding Using Executable Oracles leapt out to me as a plausible explanation for how these teams are accomplishing this. (That is: they are actively iterating on tools and frameworks to remove degrees-of-freedom, so that their LLMs will be more likely to produce correct code). This is a hypothesis that I may test with further investigation.你可以开始积极寻找可能指向这一结果的数据点。注意，我认为什么是数据受到我的框架的影响。例如，在形成这个框架后，John Regehr的《使用可执行预言机的零自由度LLM编码》突然出现在我面前，作为这些团队如何实现这一点的合理解释。（也就是说，他们正在积极迭代工具和框架以消除自由度，从而使他们的LLM更有可能生成正确的代码。）这是一个我可以通过进一步调查来检验的假设。

The key thing is that I have to commit to a frame before elaboration starts in earnest. Humans cannot investigate what they do not believe can be true. Someone who rejects the very idea of the frame, who has the opinion “this is bullshit, my experience of software engineering and the crappy results I’ve been getting from Claude Code tells me this isn’t even possible” means that they will either dismiss data points, or they will not take steps to seek out new confirming (or disconfirming) information.关键是，在细化开始之前，我必须致力于一个框架。人类无法调查他们不相信可能为真的事情。那些拒绝框架本身想法的人，持有“这是胡说八道，我的软件工程经验以及我从Claude Code得到的糟糕结果告诉我这甚至不可能”的观点，意味着他们要么会忽略数据点，要么不会采取措施寻找新的确认（或否定）信息。

In many ways, they cannot seek out disconfirming information at all. Recall that all data must be constructed, and all data are constructed within a frame. If you do not have (or refuse to commit to) a frame — you will not even see the relevant data. You would — quite literally — be blind.在许多方面，他们根本无法寻找否定信息。回想一下，所有数据都必须被构建，并且所有数据都是在框架内构建的。如果你没有（或拒绝致力于）一个框架——你甚至不会看到相关数据。你——字面意义上——将是盲目的。

The Preservation Cycle保留循环

At some point, you may encounter new data that does not fit into your frame. At this juncture, you may begin to question the frame you’ve committed to.在某个时刻，你可能会遇到不适合你框架的新数据。此时，你可能会开始质疑你所致力于的框架。

Here you are presented with two options:这里你有两个选择：

• You may choose to dismiss the incongruent data points, and opt to preserve your current frame.你可以选择忽略不一致的数据点，并选择保留你当前的框架。
• You may decide your current frame is inadequate and choose to reframe.你可以决定你当前的框架不充分，并选择重新构建。

Questioning a frame consists of the following mental activities:质疑一个框架包括以下心理活动：

• You begin to track anomalies. In some cases, experts are known to actively seek out anomalies.你开始追踪异常。在某些情况下，专家会主动寻找异常。
• You begin to detect inconsistencies in the relationships between the data points.你开始检测数据点之间关系的不一致性。
• You begin to judge the plausibility of your current frame, vs other possible frames.你开始判断当前框架与其他可能框架的合理性。
• You take action to evaluate data quality, in an attempt to rule in or rule out possible data.你采取行动评估数据质量，试图纳入或排除可能的数据。

The preservation cycle occurs when you choose to ignore data. You may also decide to discard most of the data but record a new gap in your current frame (which you may then act on: you may seek new information to plug that gap or to extend your existing frame). For instance, one outcome that might occur as I investigate the “AI coding with minimal human intervention” is that it turns out this is only possible for certain kinds of software. This is an outcome that nobody can predict in advance — but that’s par the course when you’re dealing with the uncertainty of a new technology.当你选择忽略数据时，就会发生保留循环。你也可能决定丢弃大部分数据，但在当前框架中记录一个新的缺口（然后你可以据此采取行动：寻找新信息来填补该缺口或扩展现有框架）。例如，在我调查“以最少人工干预进行AI编码”时，可能的结果之一是发现这只适用于某些类型的软件。这是一个无人能提前预测的结果——但当你面对新技术的不可预测性时，这很正常。

I think all of us have had experiences where we considered an alternative perspective, only to reject the evidence and recommit to our current frame. We’ll revisit this step when we talk about expert novice differences — in the next section.我想我们都有过这样的经历：考虑过另一种观点，但最终拒绝证据并重新承诺于当前框架。我们将在下一节讨论专家与新手差异时重新审视这一步。

The Reframing Cycle重构循环

The reframing cycle is where things get interesting. Let’s say that you’re seeing new data that is simply too incongruent with your current frame. If enough incongruent data accrues, you will experience a ‘frame breaking’ moment. You may decide to discard your current frame.重构循环是事情变得有趣的地方。假设你看到的新数据与当前框架完全不一致。如果积累了足够多的不一致数据，你将经历一个“框架破裂”的时刻。你可能决定丢弃当前框架。

Here you have two options:此时你有两个选择：

• You construct (or pick up) an alternative frame, and elaborate that in parallel with your current frame. You then compare between frames to decide which frame to commit to. This is very clearly on display in Example Two with the NICU nurse. The Data-Frame Theory observes that experts may only hold a maximum of two to three frames in parallel. Any more than three and decision performance will degrade.你构建（或拾起）一个替代框架，并与当前框架并行阐述。然后比较框架以决定承诺于哪一个。这在示例二中的NICU护士身上表现得非常明显。数据-框架理论观察到，专家最多只能同时持有两到三个框架。超过三个，决策表现就会下降。
• The second thing that you may do is to discard your old frame to seek a new frame. This basically restarts the sensemaking process from scratch. Notice that Captain B. was doing this in rapid succession in Example Three.你可能会做的第二件事是丢弃旧框架以寻找新框架。这基本上是从头开始重新启动意义建构过程。注意，B上尉在示例三中快速连续地进行了这一操作。

These four cycles explain the core of the Data-Frame Theory. But of course, if we want this to be useful, we should ask: what do experts do differently from novices?这四个循环解释了数据-框架理论的核心。但当然，如果我们希望这有用，我们应该问：专家与新手有何不同？

What do Experts do Differently?专家有何不同？

Expert-novice differences are useful because they tell us how to improve. If we know what experts do differently from novices, we may a) evaluate our skill level, b) design training programs for ourselves and for others, and c) we may avoid the worst of the novice errors.专家与新手差异很有用，因为它们告诉我们如何改进。如果我们知道专家与新手的不同之处，我们可以a)评估自己的技能水平，b)为自己和他人设计培训计划，c)避免最严重的新手错误。

With this in mind, it was a little surprising for me to learn that according to the Data-Frame theory, experts do not use different sensemaking strategies from novices. Both experts and novices use all four cycles in the Data-Frame model: they construct frames, question frames in response to sufficiently incongruent data, elaborate frames and perform reframing in the exact same ways.考虑到这一点，当我了解到根据数据-框架理论，专家并不使用与新手不同的意义建构策略时，我有点惊讶。专家和新手都使用数据-框架模型中的所有四个循环：他们构建框架，在遇到足够不一致的数据时质疑框架，阐述框架，并以完全相同的方式进行重构。

It sure seems like the Data-Frame model is a core part of human cognition!数据-框架模型似乎是人类认知的核心部分！

What differs is this:不同之处在于：

• Experts have richer causal mental models of the domain, and a richer repertoire of frames. Think of the brigadier general in Example One. Go back and reread all the bold bits. He saw a tank and immediately connected it to his understanding of armoured manoeuvre operations. So where the novice (the young fire team leader) just saw a solitary tank moving, the brigadier general immediately suspected that a larger manoeuvre was in progress. Ditto for the nurse in Example Two: she had a sepsis frame available to her, which she started elaborating the instant she saw her expectancies violated. When Klein et al say ‘mental models’, they mean “causal relationships within the domain”. The Data-Frame theory suggests that the way to get novices to perform like experts is to train them so they have better mental models (more causal relationships in their heads), and to expose them to more fragments that they may use to construct richer frames.专家拥有更丰富的领域因果心理模型和更丰富的框架库。想想示例一中的准将。回去重读所有粗体部分。他看到一辆坦克，立即将其与他对装甲机动行动的理解联系起来。因此，新手（年轻的火力小组组长）只看到一辆孤立的坦克在移动，而准将立即怀疑有更大的机动正在进行。示例二中的护士也是如此：她有一个败血症框架可用，并在看到期望被违反的瞬间开始阐述。当Klein等人说“心理模型”时，他们指的是“领域内的因果关系”。数据-框架理论表明，让新手像专家一样表现的方法是训练他们拥有更好的心理模型（头脑中有更多因果关系），并让他们接触更多可用于构建更丰富框架的片段。
• Experts also have better, richer expectancies when forming frames. They have a better idea of “what is going to happen next?” and “what should happen next?” Since they are constantly looking for what might (or should!) happen, they are quicker to notice when their expectancies are violated, and quicker to question their current frame as compared to novices. This is the key difference, I think. It’s not that experts construct frames faster or slower than novices. It’s that they are quicker to self-correct, because they have more specific expectancies.专家在形成框架时也有更好、更丰富的期望。他们更清楚“接下来会发生什么？”和“接下来应该发生什么？”由于他们不断寻找可能（或应该！）发生的事情，他们比新手更快注意到期望被违反，并更快地质疑当前框架。我认为这是关键区别。专家构建框架并不比新手更快或更慢。而是他们更快地自我纠正，因为他们有更具体的期望。
• Experts also tend to sensemake for a functional understanding, as opposed to an abstract understanding. An abstract understanding is something like “how AI affects jobs in the job market”. A functional understanding is “how does AI affect me, my company, and my role, and what can I do about it?” A different way of putting this is that experts tend to engage in more anticipatory sensemaking (“what needs to happen next?”): that is, they sensemake for action, not just for understanding. Of course, some domains call for sensemaking to an abstract understanding — this is the case when intelligence analysts write reports but are unsure how their intelligence will be consumed and to what purpose in the chain of command. But broadly speaking, whenever possible, experts will attempt to sensemake for use. A Commoncog-coded way of putting this is that experts are more outcome oriented than novices. When seen in this light, the sensemaking approach that I presented in the previous instalment (focus on field reports of AI use, in service of the four questions of uncertainty) helps you because it pushes you to a more functional understanding, as opposed to an abstract one.专家也倾向于为功能性理解而意义建构，而不是抽象理解。抽象理解类似于“AI如何影响就业市场中的工作”。功能性理解是“AI如何影响我、我的公司和我的角色，我能做些什么？”另一种说法是，专家倾向于进行更多的预期性意义建构（“接下来需要发生什么？”）：也就是说，他们为行动而意义建构，而不仅仅是为了理解。当然，有些领域需要抽象理解的意义建构——例如，情报分析师撰写报告但不确定情报将如何被使用以及在指挥链中的目的时。但总的来说，只要可能，专家会尝试为使用而意义建构。用Commoncog的方式来说，专家比新手更注重结果。从这个角度看，我在上一期中提出的意义建构方法（关注AI使用的实地报告，服务于四个不确定性问题的解答）有助于你，因为它推动你走向更功能性的理解，而不是抽象的理解。
• I’ve mentioned earlier that frame fixation is a problem, and one way that frame fixation occurs is that it is difficult to take a data point that serves as an anchor for one frame to be reused as an anchor for a different frame. Experts have different strategies to deal with this problem. Expert doctors, for instance, tend to organise diseases given a set of common symptoms into a ‘logical competitor set’ — a grouping of diseases that express the same symptoms but would never show up together in a medical textbook. The only reason these diseases would be lumped together cognitively is because they all have similar symptom presentation. The doctor would then work through the set to rule out diseases, and in this manner prevent themselves from making a frame error. (In this scenario, the symptoms are anchors, and the ‘logical competitor set’ helps with anchor reuse).我之前提到过框架固着是一个问题，框架固着发生的方式之一是，很难将一个作为某个框架锚点的数据点重新用作另一个框架的锚点。专家有不同的策略来处理这个问题。例如，专家医生倾向于将一组常见症状的疾病组织成一个“逻辑竞争集”——一组表现相同症状但在医学教科书中永远不会同时出现的疾病。这些疾病在认知上被归为一组的唯一原因是它们都有相似的症状表现。然后医生会逐一排除疾病，从而防止自己犯框架错误。（在这种情况下，症状是锚点，“逻辑竞争集”有助于锚点重用。）
• Unfortunately, this ‘logical competitor set’ strategy does not work in more dynamic domains. In medicine, the strategy works because the human body does not change that much, which means your logical competitor set can stay the same for most of your career. But in business and investing it’s possible for new developments (new technologies, spurious government policy, ham-fisted tariffs, etc) to upend the domain. In these more dynamic domains, what experts do is to commit early and eagerly to a frame but remain vigilant about possible abnormalities. In this they are helped by their richer mental models of the domain (we mentioned this earlier) which gives them more specific expectancies. They are also assisted by the number of frames they have available to them, and the number of fragments they have in their heads, which makes it easier to construct new frames. (It is easier to construct an alternative frame and seek confirming evidence for it as compared to searching for disconfirming evidence for a current frame, because human brains find it easier to reason about positive evidence as opposed to negative evidence). But they are also guided by painful experience: the researchers note that in many domains they’ve examined, experts tend to be wary of frame fixation, since they’ve all been burnt by it before.不幸的是，这种“逻辑竞争集”策略在更动态的领域不起作用。在医学中，该策略有效是因为人体变化不大，这意味着你的逻辑竞争集可以在大部分职业生涯中保持不变。但在商业和投资中，新的发展（新技术、不合理的政府政策、拙劣的关税等）可能会颠覆领域。在这些更动态的领域中，专家所做的就是尽早并热切地承诺于一个框架，但对可能的异常保持警惕。在这方面，他们更丰富的领域心理模型（我们之前提到过）帮助他们获得更具体的期望。他们还受益于他们可用的框架数量以及头脑中的片段数量，这使得构建新框架更容易。（构建替代框架并寻找确认证据比寻找当前框架的否定证据更容易，因为人脑更容易推理正面证据而非负面证据。）但他们也受到痛苦经历的指导：研究人员指出，在他们研究的许多领域中，专家往往对框架固着保持警惕，因为他们都曾因此吃过亏。

As I’ve mentioned earlier, the Data-Frame theory appears to get at something universal about human cognition. And it gets there from naturalistic study of experts (and novices) working on real world tasks. This implies that improvement suggested by this theory should be easier: if real-world experts all converge on the same set of superior strategies, we have proof that it’s doable to train novices in them.正如我之前提到的，数据-框架理论似乎触及了人类认知的某种普遍性。它来自对专家（和新手）在现实世界任务中的自然主义研究。这意味着该理论建议的改进应该更容易：如果现实世界的专家都收敛于同一套优越策略，我们就证明了培训新手掌握这些策略是可行的。

But this is perhaps too easy. You might think, “Okay, on with it! This all seems very obvious. Let’s talk about how to put this to practice! Let’s apply this to sensemaking AI!”但这可能太容易了。你可能会想，“好吧，开始吧！这一切似乎都很明显。让我们谈谈如何付诸实践！让我们将其应用于AI的意义建构！”

Yes, we’ll get there. But first, I want to poke at your newfound knowledge, in ways that might unsettle you. I want to talk about …是的，我们会谈到。但首先，我想以可能让你不安的方式挑战你新获得的知识。我想谈谈……

Some Uncomfortable Implications一些令人不安的启示

I suspect that if you’ve reached this point, you’ve found yourself mostly nodding along. All of the elements of the Data-Frame theory can seem obvious, even trivial. In fact, I’m willing to bet that you recognise the various cycles in your own cognition. The only thing that might be new to you are the claims about the specific number of anchors and frames (e.g. that it will take no more than three-to-four anchors to construct a frame, that it is difficult to reuse an anchor for a different frame, that experts hold at most two-to-three frames in parallel, beyond which performance will degrade).我猜想，如果你读到这里，你大多在点头同意。数据-框架理论的所有元素似乎都很明显，甚至微不足道。事实上，我敢打赌你会在自己的认知中识别出各种循环。唯一可能让你感到新鲜的是关于锚点和框架具体数量的说法（例如，构建一个框架只需要三到四个锚点，很难将锚点重用于不同框架，专家最多同时持有两到三个框架，超过则表现下降）。

But if you feel that the Data-Frame model is common sense, you are mistaken. The easiest way to work out why the theory should be challenging to you is to go through a few other beliefs you might have, and then talk about why those other beliefs are invalidated by implication. We should start with …但如果你觉得数据-框架模型是常识，那你就错了。理解为什么该理论对你具有挑战性的最简单方法是回顾你可能持有的其他一些信念，然后讨论为什么这些信念被隐含地否定。我们应该从……开始

There is No Such Thing as Confirmation Bias不存在所谓的确认偏误

Confirmation bias is a cognitive bias that is extremely well-replicated, and confirmed in literally thousands of psychology studies and reviews. The American Psychological Association defines it as a tendency to gather evidence that confirms preexisting expectations. Psychologist Raymond Nickerson’s widely cited review describes it as a pervasive phenomenon that appears in many forms across judgment and reasoning. He even goes so far as to describe it as a “weakness” of human cognition — an assessment that puts him in good company. This view of ‘confirmation bias as human weakness’ is as close to mainstream consensus as we can get; it is held by some of the brightest stars in psychology: Daniel Kahneman and Amos Tversky and Phillip Tetlock included.确认偏误是一种认知偏误，已被大量复制，并在数千项心理学研究和综述中得到确认。美国心理学会将其定义为倾向于收集确认先前期望的证据。心理学家Raymond Nickerson被广泛引用的综述将其描述为一种普遍现象，在判断和推理中以多种形式出现。他甚至将其描述为人类认知的“弱点”——这一评价使他与许多优秀学者为伍。这种“确认偏误作为人类弱点”的观点尽可能接近主流共识；它被心理学界一些最聪明的人物所持有，包括Daniel Kahneman、Amos Tversky和Phillip Tetlock。

The following paragraph comes from the concluding section in Nickerson’s review paper:以下段落来自Nickerson综述论文的结论部分：

The question of the extent to which the confirmation bias can be modified by training deserves more research than it has received. Inasmuch as a critical step in dealing with any type of bias is recognizing its existence, perhaps simply being aware of the confirmation bias—of its pervasiveness and of the many guises in which it appears—might help one both to be a little cautious about making up one’s mind quickly on important issues and to be somewhat more open to opinions that differ from one's own than one might otherwise be.确认偏误在多大程度上可以通过训练改变，这个问题值得更多研究。由于处理任何类型偏误的关键步骤是认识到它的存在，也许仅仅意识到确认偏误——它的普遍性和出现的多种形式——可能有助于人们在重要问题上谨慎做出决定，并对与自己不同的观点更加开放。

What a reasonable recommendation! Note that I say this unironically: the weight of evidence for the cognitive process that produces what we call ‘confirmation bias’ is beyond doubt. Humans naturally want to confirm beliefs that we already have. Because confirmation bias is so pervasive, the logical thing to do is to tell folks to delay forming a conclusion, to keep an open mind.多么合理的建议！请注意，我并非讽刺：产生我们称之为“确认偏误”的认知过程的证据权重毋庸置疑。人类自然倾向于确认我们已经持有的信念。由于确认偏误如此普遍，合乎逻辑的做法是告诉人们延迟形成结论，保持开放心态。

I want to be completely honest with you: I believed this argument completely. I swallowed it hook, line, and sinker. Like many of my generation, I read Thinking: Fast and Slow and many other popular science books on psychology. By the early 2010s, the heuristics and biases view had so completely permeated our world that it is difficult to imagine a universe of ideas without it. Hell, you can’t read a self-help book today without a reference to some cognitive bias.我想完全诚实地告诉你：我完全相信这个论点。我完全接受了它。像许多同代人一样，我读了《思考，快与慢》和许多其他心理学科普书籍。到2010年代初，启发式和偏误观已经如此彻底地渗透到我们的世界，以至于很难想象没有它的思想宇宙。见鬼，你今天读不到一本不提及某种认知偏误的自助书。

Let’s agree that the confirmation bias describes a real cognitive phenomenon. Humans really do prefer to seek confirming evidence over disconfirming evidence. Most folks find it difficult to seek out disconfirming evidence without practice. But is this tendency really a weakness?让我们同意确认偏误描述了一种真实的认知现象。人类确实更倾向于寻找确认证据而非否定证据。大多数人未经练习很难寻找否定证据。但这种倾向真的是弱点吗？

I want you to think about Example Two — the one with the expert NICU nurse — above. Go back to that example and read all the bolded bits. When the nurse started elaborating a second frame in parallel (“this baby has sepsis”), she sought out confirming evidence for the competing frame. Was she engaging in confirmation bias? No. Her performance was in the top percentile of NICU nurses. She was simply using the reframing cycle of the Data-Frame theory.我希望你思考一下上面的示例二——关于专家NICU护士的那个。回到那个例子，阅读所有粗体部分。当护士开始并行阐述第二个框架（“这个婴儿患有败血症”）时，她为竞争框架寻找确认证据。她是在进行确认偏误吗？不。她的表现属于NICU护士中的顶尖水平。她只是在使用数据-框架理论的重构循环。

Klein et al write (bold emphasis mine):Klein等人写道（粗体强调为我所加）：

In natural settings, skilled decision makers shift to an active mode of elaborating the competing frame once they detect the possibility that their frame is inaccurate. (…) A person uses an initial frame (hypothesis) as a guide in acquiring more information, and, typically, that information will be consistent with the frame. Furthermore, skilled decision makers such as expert forecasters have learned to seek disconfirming evidence where appropriate.

It is not trivial to search for disconfirming information—it may require the activation of a competing frame. Patterson, Woods, Sarter, and Watts-Perotti (1998), studying intelligence analysts who reviewed articles in the open literature, found that if the initial articles were misleading, the rest of the analyses would often be distorted because subsequent searches, and their reviews were conditioned by the initial frame formed from the first articles. The initial anchors affect the frame that is adopted, and that frame guides information seeking. What may look like a confirmation bias may simply be the use of a frame to guide information seeking. One need not think of it as a bias.在自然环境中，熟练的决策者一旦检测到框架可能不准确，就会切换到积极阐述竞争框架的模式。（……）一个人使用初始框架（假设）作为获取更多信息的指南，通常这些信息会与框架一致。此外，熟练的决策者（如专家预测者）已经学会了在适当的情况下寻找否定证据。寻找否定信息并非易事——它可能需要激活一个竞争框架。Patterson, Woods, Sarter, and Watts-Perotti (1998) 研究了审查公开文献文章的情报分析师，发现如果初始文章具有误导性，后续分析往往会扭曲，因为后续搜索及其审查受到从第一篇文章形成的初始框架的影响。初始锚点影响所采用的框架，而该框架指导信息搜索。看起来像确认偏误的可能只是使用框架来指导信息搜索。我们不必将其视为偏误。

When you call something a ‘bias’, the natural response is to do bias correction, or error reduction. This is why Nickerson, in his paper, and in fact hundreds of other psychology papers and intervention experiments and popsci books all recommend the same thing: “keep an open mind”, “don’t form a conclusion too hastily.” What could be more reasonable?当你称某物为“偏误”时，自然的反应是进行偏误纠正或错误减少。这就是为什么Nickerson在他的论文中，以及实际上数百篇其他心理学论文、干预实验和科普书籍都推荐同样的事情：“保持开放心态”，“不要过早下结论。”还有什么比这更合理的呢？

Unfortunately the recommendation by the confirmation bias folks backfires completely: in just about all natural settings that the authors examined, folks who use the ‘keep an open mind’ or ‘delay forming a view too early’-type strategies underperformed the experts. In fact, Klein et al lays this out as one of the falsifiable assertions of the Data-Frame theory: if you can find a single expert in a naturalistic setting using the ‘keep an open mind’ approach, then the Data-Frame theory is falsified and needs updating. (And if you think you’ve found someone who does this, you should check: is this really a top percentile performer? Or is there someone — or many someones — who have better performance?)不幸的是，确认偏误支持者的建议完全适得其反：在作者检查的几乎所有自然环境中，使用“保持开放心态”或“延迟过早形成观点”策略的人表现不如专家。事实上，Klein等人将此列为数据-框架理论的可证伪断言之一：如果你能在自然环境中找到一个使用“保持开放心态”方法的专家，那么数据-框架理论就被证伪并需要更新。（如果你认为你找到了这样的人，你应该检查：这真的是顶尖表现者吗？还是有人——或许多人——表现更好？）

What do the experts do instead? In domain after domain, Klein and his colleagues have found that experts form frames quickly, with specific, concrete expectancies that may be violated. This allows them to course-correct when they encounter abnormalities (that is, when their expectancies are violated) much faster than someone who hasn’t committed to a frame.专家们做了什么？在一个又一个领域，Klein和他的同事发现专家快速形成框架，并带有具体、具体的期望，这些期望可能被违反。这使他们能够在遇到异常时（即期望被违反时）比未承诺于框架的人更快地纠正方向。

Note that I’ve elided the evidence Klein et al cite to justify this assertion; you may read the original papers here, here and here. But you may also do a literature review search to see how this claim has held up over the subsequent 19 years.请注意，我略过了Klein等人引用以证明这一断言的证据；你可以在这里、这里和这里阅读原始论文。但你也可以进行文献综述搜索，看看这一主张在随后的19年中如何被验证。

Why did the confirmation bias folks get things so wrong? The answer — which is a bit of an open secret — is that the entire field of cognitive biases and heuristics is built around a flawed methodology. The field conducts experiments by administering toy problems to unskilled test subjects (undergrads, mostly) in artificial lab environments. All the cognitive processes demonstrated by the participants in these studies are then labelled as ‘reasoning errors’ or ‘biases’ whenever they result in the wrong answers. And make no mistake: these cognitive processes are real; the vast majority of them are reliably replicated in lab study after lab study, over the course of decades. But if you study practitioners solving real problems in naturalistic environments — problems that they have actual expertise in — you will find that all the same cognitive processes that result in ‘reasoning errors’ on lab tests are suddenly deployed in ways that produce excellent performance. This has been the finding of the Naturalistic Decision Making (NDM) branch of applied psychology — which Klein helped start — in study after study, ‘bias’ after ‘bias’, for the past 30 years.为什么确认偏误支持者会错得如此离谱？答案——这有点公开的秘密——是整个认知偏误和启发式领域建立在有缺陷的方法论之上。该领域通过向不熟练的测试对象（主要是本科生）在人工实验室环境中提供玩具问题来进行实验。当这些研究中的参与者表现出导致错误答案的认知过程时，这些过程就被标记为“推理错误”或“偏误”。毫无疑问，这些认知过程是真实的；绝大多数在实验室研究中被可靠地复制，持续数十年。但如果你研究在自然环境中解决真实问题的实践者——他们拥有实际专业知识的问题——你会发现，所有在实验室测试中导致“推理错误”的相同认知过程突然以产生卓越表现的方式被部署。这是应用心理学自然主义决策制定（NDM）分支的发现——Klein帮助创立了该分支——在30年来的研究中，一个“偏误”接一个“偏误”。

I can write a longer essay about this ‘open secret’. Perhaps I will. But I want to leave you with the following observation: whenever you see a cognitive bias, you should understand that there are two ways to get better performance. You may do error reduction, or you may fix it by gaining expertise.我可以就这个“公开的秘密”写一篇更长的文章。也许我会的。但我想给你留下以下观察：每当你看到一种认知偏误时，你应该明白有两种方法可以获得更好的表现。你可以进行错误减少，或者通过获得专业知识来修复它。

Unfortunately, the first path, on error reduction, is a dead end. Kahneman himself has argued that he did not believe you can correct cognitive biases. To be fair, some of his colleagues do not agree with him, and have demonstrated that training interventions for bias correction can improve performance. But the improvements are comparatively minor, and the results are slow in coming. In 2021, two giants of the Judgment and Decision Making field, David Weiss and James Shanteau, published a paper titled The Futility of Decision Making Research. They were lamenting the uselessness of their findings, despite publishing decades of research. Meanwhile the field of NDM continues to produce useful results for the military and others, including effective decision training for the armed forces, better user interfaces for military and industrial control systems, and accelerated expertise training programs.不幸的是，第一条路径，即错误减少，是一条死胡同。Kahneman本人曾表示，他不相信可以纠正认知偏误。公平地说，他的一些同事不同意他的观点，并证明偏误纠正的培训干预可以改善表现。但改善相对较小，结果来得缓慢。2021年，判断与决策领域的两位巨人David Weiss和James Shanteau发表了一篇题为《决策研究的无用性》的论文。他们哀叹自己发现的无用性，尽管发表了数十年的研究。与此同时，NDM领域继续为军方和其他机构产生有用的结果，包括为武装部队提供有效的决策培训、更好的军事和工业控制系统用户界面，以及加速的专业知识培训计划。

It doesn’t take a genius to see why. Which should result in better performance: an error correction training program built on top of the findings of unskilled test subjects solving toy problems in artificial lab environments? Or an expertise acceleration training program built on top of how experts actually achieve a low error rate in their performance on real world scenarios? Exercise left for the alert reader.不难看出原因。哪个应该带来更好的表现：基于不熟练测试对象在人工实验室环境中解决玩具问题的发现而构建的错误纠正培训计划？还是基于专家如何在现实世界场景中实现低错误率表现的专业知识加速培训计划？留给警觉的读者作为练习。

Your View of Analysis is Wrong你对分析的看法是错误的

Here is a second implication of the Data-Frame model. Most of us have the following view of analysis:这是数据-框架模型的第二个启示。我们大多数人对分析持有以下看法：

The common view of analysis is that data gets turned into information, which gets turned into insight, which gets turned into decisions, which gets turned into (in this case) ‘alpha’ — which is investment terminology for “the return your investment earns beyond what the market gives you for free.”常见的分析观点是，数据转化为信息，信息转化为洞察，洞察转化为决策，决策转化为（在这种情况下）“阿尔法”——这是投资术语，意为“你的投资赚取的超出市场免费给予的回报”。

In this model of analysis, more data results in more insights, which leads to more decisions, which results in more alpha. Therefore LLMs and other decision support systems will increase decision quality and quantity by increasing the data points that a single analyst can process.在这个分析模型中，更多数据导致更多洞察，进而导致更多决策，最终导致更多阿尔法。因此，LLM和其他决策支持系统将通过增加单个分析师可以处理的数据点来提高决策质量和数量。

The Data-Frame theory immediately tells us this model cannot be true.数据-框架理论立即告诉我们这个模型不可能是真的。

I’ve spent much time in my exposition talking about how ‘data must be constructed’ and that ‘something is only considered data in the context of a frame.’ I’ve spent some time telling you about a hypothetical data analyst who fails to spot a data abnormality because of expertise: he lacked the right frame. The Data-Frame theory tells us that the high order bit for analysis isn’t data processing — it can’t be: data does not arrive fully formed in packets, ready for consumption. The high order bit for analysis is frame construction. It’s only by constructing the right frame that an analyst can pick out the right data from an event stream. Without the right frame, the analyst is effectively blind.我在阐述中花了很多时间谈论“数据必须被构建”以及“某物只有在框架的背景下才被视为数据”。我花了一些时间告诉你一个假设的数据分析师，他由于缺乏专业知识而未能发现数据异常：他缺少正确的框架。数据-框架理论告诉我们，分析的高阶位不是数据处理——它不可能是：数据并非以完整的数据包形式出现，准备被消费。分析的高阶位是框架构建。只有通过构建正确的框架，分析师才能从事件流中挑选出正确的数据。没有正确的框架，分析师实际上是盲目的。

And in fact, in study after study, Klein and his colleagues have found that performance degrades when an analyst is fed too much data.事实上，在一项又一项研究中，Klein和他的同事发现，当分析师被喂以过多数据时，表现会下降。

So what do you do, if you want to build a analysis augmentation or decision support system?那么，如果你想构建一个分析增强或决策支持系统，你该怎么做？

Klein et al suggests that you build a system that keeps track of anchors. There are two types of anchors: anchors that are used to construct your current frame, and ‘possible anchors’ — data points that are regarded as potential anchors by the analyst, but ultimately discarded in the course of analysis. Since frame construction happens with only a small number of anchors, keeping track of these anchors is trivial.Klein等人建议你构建一个跟踪锚点的系统。有两种类型的锚点：用于构建当前框架的锚点，以及“可能的锚点”——分析师视为潜在锚点但最终在分析过程中丢弃的数据点。由于框架构建只涉及少量锚点，跟踪这些锚点很简单。

But the potential impact is large: if the analyst encounters a frame-breaking moment, they may go back to their initial set of three-to-four anchors and reassess them. They may attempt to construct alternative frames from previously discarded possible anchors. We’ve discussed how humans find it difficult to take data points used to anchor one frame and reuse them as anchors for a different frame. Having a system that externalises anchor categorisation opens the analyst up for collaborative critique. It allows a team of intelligence analysts to do collective sensemaking.但潜在影响很大：如果分析师遇到框架破裂的时刻，他们可以回到最初的三到四个锚点并重新评估。他们可以尝试从之前丢弃的可能锚点构建替代框架。我们已经讨论过人类很难将用于锚定一个框架的数据点重新用作另一个框架的锚点。拥有一个外部化锚点分类的系统为分析师提供了协作批评的可能性。它允许一个情报分析师团队进行集体意义建构。

The Data-Frame theory also suggests a method for measuring the effectiveness of decision support systems: how long does an analyst take to detect faulty data? Say we insert bad data into a series of simulated events — often described as a ‘garden path’ intelligence testing scenario. A good system should allow the analyst to catch inconsistencies faster and reframe. A bad system will make it more difficult to recognise that one’s current frame is compromised.数据-框架理论还提出了一种衡量决策支持系统有效性的方法：分析师需要多长时间检测到错误数据？假设我们在模拟事件序列中插入错误数据——通常被称为“花园路径”情报测试场景。一个好的系统应该让分析师更快地发现不一致并重新构建框架。一个糟糕的系统会使识别当前框架受损变得更加困难。

There are other recommendations from the theory, and you should read the original paper if you want to dive deeper. (There have also been extensions worked out in the subsequent 19 years; a lit review should uncover them. I suspect some of you might already have ideas on how to augment frame construction with LLMs by this point.)该理论还有其他建议，如果你想深入阅读，应该阅读原始论文。（在随后的19年中也有扩展；文献综述应该能找到它们。我怀疑你们中的一些人此时已经有了如何用LLM增强框架构建的想法。）

But I’ll stop here.但我就在这里停下。

How to Use This?如何使用这些？

Let’s return to the overarching topic of this series. We want to talk about how to sensemake uncertain new developments that are likely to affect our careers, businesses, and our lives. Right now that’s a new technology: AI. In the future it might be some other thing: tariffs, perhaps, or — god forbid — war.让我们回到本系列的总主题。我们想讨论如何对可能影响我们职业生涯、企业和生活的不确定新发展进行意义建构。现在，这是一项新技术：AI。未来可能是其他东西：关税，或者——上帝保佑——战争。

I’ve taken you on a long detour to talk about the Data-Frame theory because I think it’s actually more important to understand how we make sense of things, in order to improve at it. (I believe that AI is just one of many uncertain things that will affect us over the course of our lives. As I write this, the Strait of Hormuz is closed to most ships. This may or may not develop into a widespread energy crisis. Is that not uncertainty that one must handle?)我带你绕了一个大弯来讨论数据-框架理论，因为我认为理解我们如何理解事物实际上更重要，以便改进。（我相信AI只是我们一生中影响我们的许多不确定事物之一。在我写这篇文章时，霍尔木兹海峡对大多数船只关闭。这可能会或可能不会发展成广泛的能源危机。这不正是必须处理的不确定性吗？）

But I digress. Now that we have a better language around the process, we may talk about how to apply this.但我离题了。既然我们对这个过程有了更好的语言，我们可以讨论如何应用它。

First, let’s discuss the most obvious way your sensemaking might fail. The biggest danger we face when sensemaking a revolutionary new technology is frame fixation: that is, we stick to our current frame — by which I mean our current understanding of our domain — and as a result ignore new data that can only make sense in the context of a new frame. This is especially dangerous if the new frame contradicts the way we’ve worked before.首先，让我们讨论你的意义建构可能失败的最明显方式。在对革命性新技术进行意义建构时，我们面临的最大危险是框架固着：也就是说，我们固守当前框架——我指的是我们当前对领域的理解——因此忽略了只有在新框架背景下才有意义的新数据。如果新框架与我们之前的工作方式相矛盾，这尤其危险。

It is possible that you’ve already realised this. In the previous instalment I gave you a simple method of sensemaking AI:你可能已经意识到了这一点。在上一期中，我给了你一个简单的AI意义建构方法：

1. You ignore predictions, prognostications and takes,你忽略预测、预言和观点，
2. You focus only on detailed field reports of use,你只关注详细的实地使用报告，
3. And when you do so, you ask yourself the four questions of uncertainty: a) what are the new outcomes available here? b) what are the further actions? c) what are the relative value of outcomes given my interests? And finally d) what are the causal mechanisms here?当你这样做时，你问自己四个不确定性问题：a) 这里有哪些新的结果？b) 有哪些进一步的行动？c) 根据我的兴趣，结果的相对价值是什么？最后d) 这里的因果机制是什么？

And that’s all well and good, but the problem is that if you are not sensitive to alternative new frames, you may dismiss data points that indicate new outcomes, new actions and new causal mechanisms. Which means my recommendation is as good as useless.这都很好，但问题是，如果你对新的替代框架不敏感，你可能会忽略指示新结果、新行动和新因果机制的数据点。这意味着我的建议几乎毫无用处。

Fortunately, the Data-Frame theory has one obvious answer: be more willing to elaborate a new, alternative frame. You don’t have to commit totally to this new frame — it’s logical to hold on to your current frame whilst you elaborate the new frame in parallel. In truth, my frame of “it’s possible to build good software with AI without much human intervention” is an alternative frame I’m actively elaborating. At this point I am not yet sure if I should commit completely to it.幸运的是，数据-框架理论有一个明显的答案：更愿意阐述一个新的替代框架。你不必完全承诺于这个新框架——在并行阐述新框架的同时保留当前框架是合理的。事实上，我的“可以在没有太多人工干预的情况下用AI构建好软件”框架是我正在积极阐述的一个替代框架。目前我还不确定是否应该完全承诺于它。

The broader danger is clear now, I think: you hold on to your current frame too tightly that you don’t even consider alternatives. Which means you’ll be blind to certain data, and therefore blindsided when the best practices of your domain shifts under you.更广泛的危险现在很清楚了，我认为：你过于紧握当前框架，以至于根本不考虑替代方案。这意味着你会对某些数据视而不见，因此当你的领域的最佳实践在你脚下发生变化时，你会措手不及。

We’ll have more to say about this failure mode in the next instalment.我们将在下一期中进一步讨论这种失败模式。

There is one other recommendation that falls out of the Data-Frame theory. The theory states that you cannot improve by learning better ‘sensemaking skills’. Those don’t exist; novices sensemake in the exact same way that experts do. Instead, you improve by:数据-框架理论还提出了另一个建议。该理论指出，你不能通过学习更好的“意义建构技能”来改进。这些技能不存在；新手与专家以完全相同的方式进行意义建构。相反，你通过以下方式改进：

1. Improving your mental models of your domain. (As a reminder, Klein et al mean something narrow when they say ‘mental models’ here: they mean the causal relationships of your domain. Specifically, you want knowledge that gives you narrow, specific expectancies that may be violated by events, actions and outcomes).改进你对领域的心理模型。（提醒一下，Klein等人在这里说“心理模型”时指的是狭窄的东西：他们指的是你领域的因果关系。具体来说，你需要能给你狭窄、具体期望的知识，这些期望可能被事件、行动和结果违反。）
2. You collect fragments of previous cases — that is, cases of similar situations — so that you may combine those to construct new frames.你收集先前案例的片段——即类似情况的案例——以便组合这些片段来构建新框架。

This may all seem a little theoretical, so here’s an example. 这看起来可能有点理论化，所以这里有一个例子。

I shall give you a fragment: an example of a new technology leading to a competitive advantage for a specific company. Observe your own cognition as you’re reading this fragment; I’m willing to bet that your sensemaking will change by the end.我将给你一个片段：一个新技术为特定公司带来竞争优势的例子。阅读这个片段时，观察你自己的认知过程；我敢打赌，到结尾时你的理解会发生变化。

Here goes:开始：

The PC revolution occurred over the course of the 80s. Initially, demand for personal computers was insatiable. The first killer app for the personal computer was the spreadsheet — VisiCalc, originally written for the Apple II. This caused sales of the Apple machine to take off. It’s a little hard to imagine today, but the interactive digital spreadsheet represented something remarkable: before desktop spreadsheet software, accountants and businesspeople had to update the ‘cells’ of a literal spreadsheet (on paper) manually. Nobody who saw the interactive spreadsheet in action could forget it. Demand was instantaneous. When Lotus 1-2-3 — a better, more powerful spreadsheet package — launched for the IBM PC, the same thing happened for the entire IBM PC-compatible market. 个人电脑革命发生在80年代。最初，对个人电脑的需求是难以满足的。个人电脑的第一个杀手级应用是电子表格——VisiCalc，最初是为Apple II编写的。这导致苹果机器的销量飙升。今天很难想象，但交互式数字电子表格代表了一件了不起的事情：在桌面电子表格软件出现之前，会计师和商人必须手动更新纸质电子表格的“单元格”。看到交互式电子表格在运行的人都不会忘记它。需求是瞬间产生的。当Lotus 1-2-3——一个更好、更强大的电子表格软件包——在IBM PC上推出时，整个IBM PC兼容市场也发生了同样的事情。

This meant that dozens of PC manufacturers sprung up to sell machines to consumers. And despite the average PC costing in the thousands of dollars, consumers and businesses simply couldn’t get enough of them.这意味着数十家PC制造商涌现出来，向消费者销售机器。尽管普通PC售价高达数千美元，但消费者和企业仍然供不应求。

Spreadsheet software was quickly followed by desktop word processing software. The market leader was WordPerfect. Microsoft veteran Steven Sinofsky was a graduate student at the time; in his memoirs, he wrote:电子表格软件之后很快出现了桌面文字处理软件。市场领导者是WordPerfect。微软资深人士Steven Sinofsky当时是一名研究生；在他的回忆录中，他写道：

[In the 90s] Windows Office followed Mac Office but with a slightly bumpier journey. Externally, with Windows, the challenge was first winning critical acclaim and customer love over the category competition. The journey would take years for some customers—not only were the MS-DOS leaders [Lotus 1-2-3 and Wordperfect] loved, but those products were hard to learn and customers invested a lot in the keystrokes, macros, plugins, and in the existing files, which were difficult to import and export with Excel and Word. The MS-DOS PC era [in the 80s] was characterized by investing in a PC and software to the tune of $3,000 ($7,000 in 2019 dollars) or more, and then literally taking classes and buying books to learn to use the computer. I spent two summers in the mid-80s at Martin Marietta teaching people how to use WordPerfect and 1-2-3 (but never both to any one person as “secretaries” learned WordPerfect and managers learned 1-2-3).[在90年代] Windows Office紧随Mac Office之后，但旅程稍微坎坷一些。在外部，对于Windows来说，挑战首先是赢得评论界的赞誉和客户对同类竞争产品的喜爱。这个过程对一些客户来说需要数年时间——不仅MS-DOS的领导者[Lotus 1-2-3和WordPerfect]深受喜爱，而且这些产品很难学，客户在按键、宏、插件以及现有文件上投入了大量精力，这些文件很难与Excel和Word导入导出。MS-DOS PC时代[在80年代]的特点是投资一台PC和软件，花费高达3000美元（2019年相当于7000美元）或更多，然后还要上课和买书来学习使用电脑。我在80年代中期花了两个夏天在Martin Marietta教人们如何使用WordPerfect和1-2-3（但从未同时教同一个人，因为“秘书”学WordPerfect，而经理学1-2-3）。

The classifieds of all the major newspapers at the time were plastered with ads for computer classes. Course creators, then as now, were making bank in response to this new technology, capitalising on widespread excitement (and widespread fear of missing out). The opinion pages were filled with breathless speculation about the ‘paperless office’; tech pages were filled with PC reviews; even non-technology writers weighed in on how the computer would change business, and society. 当时所有主要报纸的分类广告都贴满了电脑课程的广告。课程创作者，和现在一样，通过这种新技术赚得盆满钵满，利用广泛的兴奋情绪（以及广泛的错失恐惧症）。评论版充满了关于“无纸化办公室”的激动人心的猜测；科技版充满了PC评测；甚至非科技作家也纷纷评论电脑将如何改变商业和社会。

If this sounds familiar, it is because it is. In the early stages of a revolutionary new technology — you should expect to see rabid, widespread adoption. Then, firms were purchasing computers by the boatload and paying for training. Today, companies are mandating ‘AI-usage throughout the enterprise’ and course creators are teaching “how to use AI” courses and are raking it in.如果这听起来很熟悉，那是因为事实如此。在一项革命性新技术的早期阶段——你应该会看到狂热的、广泛的采用。然后，公司成批购买电脑并支付培训费用。今天，公司强制要求“在整个企业中使用AI”，课程创作者教授“如何使用AI”课程并赚得盆满钵满。

Did any of these PC-adopter companies win? No, of course not. Mass adoption meant that everyone upgraded within the same decade, which meant nobody had a permanent advantage against their competitors. In the long run, PCs made certain activities — like accounting, finance, desktop publishing, typesetting, and so on — permanently easier. In some cases, PC adoption let smaller companies punch above their weight. Barcode scanners, for instance, gave medium-sized retail chains the ability to fight back against the national chains … for a time. Similarly, desktop publishing software led to a short flourishing of new weekly newspapers, monthly periodicals, and even daily newspapers in 1985 to 1990 (a trend that would soon reverse with the advent of the Internet). But PC adoption by itself did not lead to sustained competitive advantage.这些采用PC的公司中有赢家吗？不，当然没有。大规模采用意味着每个人都在同一个十年内升级，这意味着没有人拥有对竞争对手的永久优势。从长远来看，PC使某些活动——如会计、财务、桌面出版、排版等——永久性地变得更容易。在某些情况下，PC的采用让小公司能够超越自身规模。例如，条形码扫描仪让中型零售连锁店能够在一段时间内与全国性连锁店抗衡。同样，桌面出版软件在1985年至1990年间带来了短暂的每周新报纸、月刊甚至日报的繁荣（这一趋势随着互联网的出现很快逆转）。但PC采用本身并没有带来持续的竞争优势。

So who won as a result of the PC revolution? Who built a sustained competitive advantage and crushed their competitors? 那么，谁在PC革命中获胜了？谁建立了持续的竞争优势并击败了竞争对手？

The answer: Walmart. 答案是：沃尔玛。

Walmart was an early adopter of personal computer technology, but not the earliest adopter. The first barcode scanner, for instance, was installed at an outlet of Marsh Supermarkets in 1974. Marsh was a medium-sized regional grocer. Over the course of the 70s and the 80s, the barcode scanner helped many smaller retailers compete with national chains. (I should note that it did not help the smallest of retail shops — the mom-and-pops — since computer equipment was still expensive and mom-and-pops lacked the bargaining power necessary to get manufacturers to print bar-codes on their packages). But for a ‘mere’ decade, at least, the barcode scanner helped many medium size chains resist total dominance by the nationals — a notable thing, since the nationals had superior economies of scale. Walmart adopted barcode scanners in their stories starting from 1983. It was not a laggard by any means. But it was only in the late 80s that Walmart had barcode scanners in all of their distribution centres. This second thing was more important, as we shall soon see.沃尔玛是个人电脑技术的早期采用者，但不是最早的。例如，第一个条形码扫描仪于1974年安装在Marsh超市的一家门店。Marsh是一家中型区域杂货商。在70年代和80年代，条形码扫描仪帮助许多小型零售商与全国性连锁店竞争。（我应该指出，它并没有帮助最小的零售店——夫妻店——因为电脑设备仍然昂贵，而且夫妻店缺乏必要的议价能力来让制造商在包装上印条形码）。但至少在“仅仅”十年里，条形码扫描仪帮助许多中型连锁店抵抗了全国性连锁店的完全主导——这是一件值得注意的事情，因为全国性连锁店拥有规模经济优势。沃尔玛从1983年开始在其门店采用条形码扫描仪。它绝不是落后者。但直到80年代末，沃尔玛才在所有配送中心安装了条形码扫描仪。这第二件事更为重要，我们很快就会看到。

Walmart was also not the only consumer of information technology. During this period, Sears — then the largest retailer in the US — was a major spender, and in many ways a leader on certain retail technologies. In his 2022 book, The New Goliaths, economist James Bessen points out that Sears was IBM’s largest customer in the late 80s. They also partnered with IBM and spent $1 billion to create the jointly-owned Prodigy system, pioneering e-commerce in the process. 沃尔玛也不是信息技术的唯一消费者。在此期间，西尔斯——当时美国最大的零售商——是主要支出者，并且在许多方面是某些零售技术的领导者。经济学家James Bessen在其2022年的著作《新巨人》中指出，西尔斯在80年代末是IBM的最大客户。他们还与IBM合作，花费10亿美元创建了共同拥有的Prodigy系统，在此过程中开创了电子商务。

Today Sears is effectively defunct. Kmart, the second largest retailer during the 80s, is also dead. In 1979, Kmart was the king of the discount retailing industry — a segment it helped create. At times the company even surpassed Sears in revenue. It had 1891 stores and average revenues per store of $7.25 million; that same year, Walmart had only 229 stores and per-store revenues that were half of the average Kmart store. A mere 10 years later, in 1989, Walmart had achieved the highest sales per square foot, inventory turns, and operating profit of any discount retailer. In 1990, Walmart overtook Kmart as the second-largest US retailer by revenue. 今天，西尔斯实际上已经倒闭。凯马特，80年代的第二大零售商，也已消亡。1979年，凯马特是折扣零售行业（它帮助创建的一个细分市场）的王者。有时该公司甚至在西尔斯的收入之上。它有1891家门店，平均每家门店收入725万美元；同年，沃尔玛只有229家门店，每家门店的收入是凯马特平均水平的一半。仅仅10年后，1989年，沃尔玛实现了所有折扣零售商中最高的每平方英尺销售额、库存周转率和营业利润。1990年，沃尔玛超越凯马特，成为美国第二大零售商（按收入计算）。

What happened? 发生了什么？

Walmart invested in information technology, but did so in a particular way. Barcode scanning generated a torrent of purchase data. This allowed stores to calculate store finances and update inventory data on-the-fly. This benefit was obvious, and was touted by the makers of barcode scanning systems; many regional chains built or bought software to accomplish exactly this set of outcomes. When the national chains hopped onto the barcode scanner wagon, they built their own versions of this software but grafted it onto their own centralised company structure. This was logical: the national chain store format was originally built on the premise that centralised purchasing and streamlined product line ups would mean increased purchasing power, and therefore increased economies of scale. 沃尔玛投资了信息技术，但以特定的方式。条形码扫描产生了大量的购买数据。这使得门店能够即时计算门店财务并更新库存数据。这个好处是显而易见的，并且被条形码扫描系统的制造商所吹捧；许多区域连锁店构建或购买了软件来实现这一系列结果。当全国性连锁店加入条形码扫描仪的行列时，他们构建了自己的软件版本，但将其嫁接到自己集中的公司结构上。这是合乎逻辑的：全国性连锁店的形式最初建立在这样一个前提上：集中采购和精简产品线将意味着更强的购买力，从而带来更大的规模经济。

Walmart noticed this benefit of barcodes and the affordances of cheap computers and took it to its logical extreme.沃尔玛注意到了条形码的这一好处以及廉价计算机的便利性，并将其发挥到了极致。

The problem with stocking new product lines is that you have to coordinate purchasing and restocking with your suppliers. A larger selection of SKUs and services results in increased complexity for store managers. Walmart built custom software to handle this complexity. By the late 1970s, all of Walmart’s distribution centres were linked by a computer network. In 1987, Walmart built its own $24 million satellite network for communication between stores and headquarters. In 1990, Walmart launched Retail Link — custom software that connected its stores, distribution centres, and suppliers and provided live inventory data to all parties. With this final piece, even Walmart’s suppliers could see when inventories at specific stores were running low, and initiate a new purchase order and delivery. 库存新产品线的问题在于，你必须与供应商协调采购和补货。更多的SKU和服务种类会增加门店经理的复杂性。沃尔玛构建了定制软件来处理这种复杂性。到70年代末，沃尔玛的所有配送中心都通过计算机网络连接起来。1987年，沃尔玛建立了自己的2400万美元卫星网络，用于门店和总部之间的通信。1990年，沃尔玛推出了Retail Link——一种连接其门店、配送中心和供应商的定制软件，并向所有各方提供实时库存数据。有了这最后一块拼图，甚至沃尔玛的供应商也能看到特定门店的库存何时不足，并启动新的采购订单和配送。

More importantly, the net result of all of Walmart’s information technology investments inverted the chain store command structure: they redirected the flow of information to enable store managers and even suppliers to make stocking decisions. This reduced the cost of managing and therefore adding additional product lines. As a result, Walmart began expanding its offerings. In 1988 it introduced the Supercenter format. In Supercenter stores, Walmart not only sold general merchandise but also dry and frozen goods, meat, poultry, fresh seafood and produce, pharmacies, optical stores, photography services, tire and lube services, hair and nail salons, and eventually cell phone stores, banking products and fast food. This increased variety of products and services drew customers to Walmart; the company touted ‘one-stop shopping’ as the reason “customers choose our Supercenters.” One study even found that consumers were willing to pay a premium to shop at Walmart due to this advantage.更重要的是，沃尔玛所有信息技术投资的最终结果颠覆了连锁店的指挥结构：它们重新引导了信息流，使门店经理甚至供应商能够做出库存决策。这降低了管理成本，从而增加了产品线。结果，沃尔玛开始扩大其产品范围。1988年，它推出了超级中心模式。在超级中心门店中，沃尔玛不仅销售一般商品，还销售干货和冷冻食品、肉类、家禽、新鲜海鲜和农产品、药店、眼镜店、摄影服务、轮胎和润滑油服务、美发和美甲沙龙，最终还有手机店、银行产品和快餐。这种增加的产品和服务种类吸引了顾客到沃尔玛；该公司吹嘘“一站式购物”是“顾客选择我们超级中心”的原因。一项研究甚至发现，由于这一优势，消费者愿意在沃尔玛购物时支付溢价。

But Walmart didn’t stop there. It abandoned the typical chain store warehouse model in favour of a logistics practice called cross-docking. In a traditional chain store warehouse, suppliers would deliver goods to the company warehouse, where it would be shelved and inventoried. Later, when stores needed to be resupplied, the company would take products out of the warehouse and deliver it to the stores. Walmart got rid of this practice. Instead, when supplier trucks arrived at a Walmart distribution centre, goods would be unloaded from the supplier’s truck and immediately loaded onto various Walmart trucks. These Walmart trucks would then drive to each individual store. This meant that both store trucks and supplier trucks must arrive at the distribution centre at the exact same time — and that loaders needed to know how much of each shipment needed to be put into which Walmart truck. This was only possible with (again) custom software Walmart had built.但沃尔玛并没有止步于此。它放弃了典型的连锁店仓库模式，转而采用一种称为交叉对接的物流实践。在传统的连锁店仓库中，供应商将货物送到公司仓库，在那里上架和盘点。之后，当门店需要补货时，公司从仓库取出货物并送到门店。沃尔玛摒弃了这种做法。相反，当供应商的卡车到达沃尔玛配送中心时，货物从供应商的卡车上卸下，并立即装载到各种沃尔玛卡车上。然后这些沃尔玛卡车开到每个门店。这意味着门店卡车和供应商卡车必须同时到达配送中心——并且装卸工需要知道每批货物中有多少需要装到哪辆沃尔玛卡车上。这只有通过（再次）沃尔玛构建的定制软件才可能实现。

The impact of cross-docking was that Walmart could enjoy the same economies of scale that came with purchasing in bulk, but without the usual inventory and handling costs. It passed on all of this cost savings to the consumer. The savings from cross-docking meant that in the grocery segment, products in Walmart Supercenters cost about 10% less in the same markets relative to traditional supermarkets. Walmart’s competitors simply couldn’t compete. In every market that Walmart entered, they began dying. They were toast.交叉对接的影响是，沃尔玛可以享受批量采购带来的规模经济，但没有通常的库存和处理成本。它将所有这些成本节省转嫁给了消费者。交叉对接节省的成本意味着，在杂货领域，沃尔玛超级中心的产品在同一市场上的价格比传统超市低约10%。沃尔玛的竞争对手根本无法竞争。在沃尔玛进入的每个市场，它们都开始消亡。它们完蛋了。

Bessen calls this “winning through economies of scope” — the ability to service vastly increased product variety due to investments in information technology.Bessen称之为“通过范围经济获胜”——由于信息技术投资，能够服务大大增加的产品种类。

So whilst all of Walmart’s competitors were investing in ‘everyone gets a computer’ and ‘we need to digitise our workflows’ and ‘here are stipends for computer courses’, Walmart was using information technology to do something very, very different.因此，当沃尔玛的所有竞争对手都在投资“每个人都有一台电脑”、“我们需要数字化工作流程”和“这里是电脑课程津贴”时，沃尔玛正在利用信息技术做一些非常非常不同的事情。

In The New Goliaths, Bessen concludes (lightly paraphrased, bold emphasis mine): “Walmart married information technology to a new type of organisation. It turned the chain store model around and used information technology to decentralise decision-making, allowing countless decisions to be made quickly and efficiently, creating stores that better met customer needs. It was this combination of technology and organisation that allowed Walmart to grow while Sears and Kmart and countless smaller retailers struggled and often failed. 在《新巨人》中，Bessen总结道（轻微改写，粗体强调是我的）：“沃尔玛将信息技术与一种新型组织相结合。它扭转了连锁店模式，利用信息技术分散决策，使无数决策能够快速高效地做出，创建了更好地满足客户需求的门店。正是这种技术与组织的结合，使沃尔玛得以成长，而西尔斯、凯马特以及无数小型零售商挣扎并常常失败。”

So, let’s see. Did your thinking about AI change in response to this?那么，让我们看看。你对AI的看法是否因此发生了变化？

I suspect yes. Why?我猜是的。为什么？

The short answer is that I’ve given you a new causal mental model with this fragment. The causal model goes something like this: “When mass adoption of a new technology happens, nobody wins — or at least not for long. The way to truly beat competitors with new technology is to use that new technology in a way that cannot be easily copied.”简短的回答是，我通过这个片段给了你一个新的因果心智模型。这个因果模型大致是这样的：“当一项新技术被大规模采用时，没有人会赢——或者至少不会长久。真正用新技术击败竞争对手的方法，是以一种不易被复制的方式使用这项新技术。”

You may also draw certain other inferences, such as “‘how to use AI’ courses will not lead to sustained competitive advantage”, or “top-down ‘everyone must use AI’ mandates will not lead to winning.” Perhaps if you are more sophisticated, you might think “new technology is best when it is used to be enforce an existing competitive advantage.” But in truth these inferences are not that important. What is important is that the fragment lives in your head.你可能还会得出其他推论，比如“‘如何使用AI’的课程不会带来持续的竞争优势”，或者“自上而下的‘每个人都必须使用AI’的命令不会带来胜利。”也许如果你更老练，你可能会想“新技术最好用于加强现有的竞争优势。”但实际上这些推论并不那么重要。重要的是这个片段存在于你的脑海中。

The way you use AI to win will be different from the way that Walmart won with PCs. AI is a different technology from personal computers, and the competitive advantages available to you will be different compared to the competitive advantages available to Walmart, in retail.你用AI获胜的方式将不同于沃尔玛用PC获胜的方式。AI是一种不同于个人电脑的技术，你可以获得的竞争优势也将不同于沃尔玛在零售业中获得的竞争优势。

But because this fragment about Walmart lives in your head, you will be sensitive to new data that hints at a path to sustained competitive advantage for your specific context. Your brain will be able to assemble alternate new frames from fragments of what you know about your industry, combined with this Walmart fragment. Some of the frames you construct will be genuinely new insights — frames that nobody has ever constructed before. Hopefully, you’ll have a shot at competitive advantage that your peers do not see coming.但由于这个关于沃尔玛的片段存在于你的脑海中，你将能够敏锐地察觉到暗示你特定情境下持续竞争优势路径的新数据。你的大脑将能够从你对行业了解的知识片段与这个沃尔玛片段结合，组装出新的替代框架。你构建的一些框架将是真正的新见解——从未有人构建过的框架。希望你能获得你的同行没有预见到的竞争优势。

This is why it is important to collect cases of companies winning with new technology. Yes, I’m making a meta point here: the way to improve your sensemaking with regard to AI is to collect fragments of companies adopting revolutionary new technology and winning (as well as companies adopting new technology and losing). The more fragments in your head, the better the frames you will be able to construct. The more data you will be able to notice. The quicker you can self-correct from frame fixation. And the better prepared you will be if disruption comes for you and yours.这就是为什么收集公司用新技术获胜的案例很重要。是的，我在这里提出一个元观点：提高你对AI理解能力的方法是收集公司采用革命性新技术并获胜的片段（以及公司采用新技术但失败的片段）。你脑海中的片段越多，你就能构建出更好的框架。你能注意到的数据就越多。你就能更快地从框架固着中自我纠正。如果颠覆降临到你和你的人身上，你就能准备得更好。

In the coming weeks, after this series concludes, Commoncog will be researching and publishing these fragments for you.在接下来的几周里，在这个系列结束后，Commoncog将为你研究和发布这些片段。

It’s time to wrap up. What have I shown you? I’ve given you a new model of sensemaking — what I believe to be the best theory we currently have. This model appears intuitive but invalidates several common sense notions of cognition you and I might have. We know how experts sensemaking differently from novices. We know what traps lie in wait for us when we’re sensemaking a radically new technology such as AI. And we know how to improve.是时候总结了。我向你展示了什么？我给了你一个新的理解模型——我认为是目前最好的理论。这个模型看起来直观，但推翻了你我可能有的几个关于认知的常识观念。我们知道专家如何与新手不同地理解。我们知道在理解像AI这样全新的技术时，有哪些陷阱在等着我们。而且我们知道如何改进。

In the next instalment, we’ll discuss concrete details of how to improve on the sensemaking approach that I outlined in the previous instalment. Specifically, we’ll talk about preventing frame fixation, and about hunting for new fragments that will help you see.在下一部分中，我们将讨论如何改进我在上一部分中概述的理解方法的具体细节。具体来说，我们将讨论防止框架固着，以及寻找能帮助你看到的新片段。

See you there.到时候见。

This is Part 2 in a short series on sensemaking. You may read Part 3 here: How to Improve at Sensemaking AI这是关于理解的一个短系列的第2部分。你可以在这里阅读第3部分：如何提高对AI的理解能力

Originally published 07 April 2026, last updated 14 June 2026.最初发表于2026年4月7日，最后更新于2026年6月14日。