Connectors require facts

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The state of learning (particularly memorization) is at a weird place in time. Since the launch of Google 25 years ago, and steadily increasing as things like Wikipedia, smartphones, and AI came along, the need to memorize information has become less important. However, it’s becoming increasing important to develop the ability to connect information to make new insights.

In a recent podcast from Adam Grant, where he spoke with OpenAI’s Sam Altman, Grant seemed to somewhat disagree, saying:

“And if you were a fact collector, that made you smart and respected. And now I think it’s much more valuable to be a connector of dots than a collector of facts that if you can synthesize and recognize patterns, you have an edge.”

Between Adam and myself, I think we see “connectors” in a few different ways:

  • Connecting facts and ideas. For example, this could be learning systems like “Traction” but also learning systems like “The 12 Week Year” and pulling the best pieces from each. You can’t notice those differences unless you understand both systems.
  • Recognizing patterns, such as various mental models that you can learn and apply, which relies less on facts stored in our heads.

Related is another concept from “The 12 Week Year” regarding knowledge and making use of it, where they shared:

“You’ve no doubt heard the saying knowledge is power. I disagree. Knowledge is only powerful if you use it, if you act on it. People spend lifetimes acquiring knowledge, but to what purpose? Knowledge alone benefits no one unless the person acquiring it does something with it.”

Regardless of the way you acquire knowledge, whether it’s learning and memorizing up front or making efficient use of technology to find answers as needed, the application of that knowledge is where the true value lives. Becoming a “connector of dots” will make you indispensable to any organization that you wish to serve, and you can only connect the dots that you already know.

A good mental model of the internet is essential

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I’ve talked a lot about mental models on here over the years, and even did a series of them a while back (found here). Charlie Munger is a huge proponent of them, and I found my time digging through them to be very worthwhile.

However, there are models that can be more specific than the generic mental models I listed before. For example, if you are a golf coach, then you likely have a handful of solid models of how a swing should look and can assess and critique based on that.

I see the same with the internet. Generally speaking, people have a good idea of how things work; you need a browser to visit websites, password resets will go to your email, things like that. When you’re lacking that basic model, though, things fall apart very quickly. Kyle Caldwell shares that in this amazing TikTok video:

@kylecaldwell2 #stitch with @JD Roninstone I’d be shocked to find out her target audience could see this video #boomer #millennial #genz #storytime ♬ original sound – Kyle Caldwell

I kind of feel bad for both of them in this example, but the man didn’t have even a basic clue about how the internet worked, and he seemed disinterested in learning. That may be the bigger issue, because this kind of problem is likely to happen to him quite often until he’s willing to understand the overall model of how things work online.

I try to be aware of other models as I go through life and learn them when necessary, kind of like I shared a few years ago when I dug into areas where I’m ignorant. Some places I think it’s ok to remain ignorant, and others I feel the need to fix. Knowing the difference, and being willing to do something about it, is key.

Thought Experiments

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The last mental model that I’m going to unpack, before going back to my normal cadence of blogging, is the idea of “thought experiments”. While these mental models were presented in alphabetical order, this one is a perfect one to end on, as it’s a great way to tie all of the rest together.

Sometimes thought experiments are ways to think through an experiment before you test it in the real world. This is what Galileo did in the 17th century, where he fought conventional wisdom and claimed that two balls (one light, one heavy), dropped from a tower would land at the same time. He spent a lot of time thinking through and calculating the experiment ahead of time, and then when he eventually did the experiment for real, he was proven correct.

Other thought experiments are important because they can’t be tested in the real world, so accuracy in the thought experiment itself is very important. One of the best examples of this are Einstein’s thoughts on what it would be like to catch up to a beam of light (which was made into a fascinating children’s book).

Some thought experiments are simply meant to cause you to think, and may have no real answer. An example of this is the famous “trolley problem“, which ultimately doesn’t have a right or wrong answer.

Thought experiments can be a great way to think about potential solutions to problems that you have, and running them through various mental models can help them be far more effective.

You can find the full list of the 21 mental models that I’ve discussed here, and it’s on to other topics starting tomorrow.

The Map Is Not the Territory

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The best map you’ve ever seen is imperfect. It has to be. If a map were to contain every bit of information about the area it represented, it’d be a cluttered mess. Every map has some degree of imperfection, and a large degree of omitted information. As the Farnam Street blog says, “A map with the scale of one mile to one mile would not have the problems that maps have, nor would it be helpful in any way.”

This is not a bad thing, but it’s an important concept to keep in mind.

The problem arises when you have a solid map and try to apply it to everything, like when Ron Johnson tried to apply Apple’s successful techniques over at J.C. Penney and failed miserably. His map was great, but it wasn’t the one he needed in this situation.

It’s a common trap, and one that Charlie Munger explains as:

“When 1 sperm gets into a human egg, there’s an automatic shut-off device. The human mind tends toward the same result & so, people accumulate large mental holdings of fixed conclusions that are not re-examined even though they are wrong”

This is a concern when learning these mental models; knowing them is great, but knowing when to apply them is the real value. The more you can connect mental models to use pieces of various models in any given situation, the better the result is likely to be.

Social Proof

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The mental model of Social Proof is used frequently in marketing, but it’s a bit deeper than I’ve often realized. The main concept, as written by Robert Cialdini in his 1984 book “Influence” is that it:

maintains that a person who does not know what the proper behavior for a certain situation is, will look to other people to imitate what they are doing and to provide guidance for his actions.

This can be a bad thing, such as when teens face improper peer pressure, or when someone is afraid to speak out against something but prefers to just fall in line with those around them.

In many cases, though, it’s often more of just a cultural norm.

  • Articles that get shared a lot tend to get shared even more.
  • If a line has formed for something, most will get in line rather than push to the front.
  • While many hate “laugh tracks” on television shows, they’re shown to be rather effective in getting others to laugh.

When it comes to marketing, social proof can be a great way to convince others to buy your product. “See our reviews” is essentially saying “see why other people like you found our product helpful”. It’s compelling. Almost every website that we build has some degree of customer testimonials on it to help show new visitors why they should care more about that particular product.

Psychology Notes HQ wraps it up nicely:

Social proof is a very powerful weapon of persuasion and influence. Awareness of how its mechanisms work and affect our personal life, consumer choices, education, social interactions, and every other aspect of our daily existence, could be very helpful.

Skill Stack

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The next mental model that I’m digging into is the concept of the “skill stack”. The idea behind this is that having a wide range of skills can help multiply your success. It’s similar to the idea I shared earlier this year about wider study helping even in narrow fields.

Andrew McVagh gives a great example of this:

Imagine two similar software programmers that work on the same team, Abe and Bob. They are both equally good at programming, but Bob has also mastered business writing. Which one is more likely to progress in their career? I imagine the one that can write clearly will more likely make their customers (and boss) happy.

Now, if Bob has gone too wide with his skills while Abe has become a much better programmer, the game changes — Abe is the better fit. All things being equal, though, Bob is in a better position.

Pulling from that post of mine earlier this year, data has shown that scientists have a 22x better chance of winning a Nobel prize if they have certain unrelated activities in their lives. Specifically:

Comparing Nobel prize-winning scientists to other scientists, the figures show that Nobel laureates are a full 22 times more likely to be an amateur actor, magician, dancer or performer.

I’ll admit, it’s a tough balance. You need to focus on your core skills to make sure you’re excellent with those, but also find some time to mix in other activities. I don’t have the answer on how to do that, but the overall concept is something to keep in mind as you do any future planning.

Second-Order Thinking

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The mental model of Second-Order Thinking simply means to look beyond your initial output, and think about what other consequences might result from it.

A great way to think about this is to use a similar pattern to the Five Whys, and just follow your decision with a few “And Then What?” statements.

A good example might be a task at work. It’s a task that you are great at doing, but really should start being handled by someone else. The next time it comes up, you take care of it yourself, and the first-order result is that you solved the problem and saved the day.

However, with some second-order thinking, you can consider the other ramifications of what you did:

  • When it happens again, people will still turn to you.
  • Others on your team aren’t learning this valuable skill.
  • If they’re not learning, they’re not growing, which is bad for both parties.

Learning to delegate certain tasks and processes is much easier said than done, but second-order thinking can help you get there.

The Garden

Adam Amran at Untools has a great example related to gardening to show it from a different angle:

Consider the decision of buying a house outside of the city.

The immediate effects might be having a garden, more space for your family, but also suddenly living an hour away from work.

Now look at higher-order consequences of each:

– having a garden → able to grow your produce → having fresh herbs and vegetables
– more space for family → more rooms to clean → more stress from a messy home
– living an hour away from work → need to buy a car → spending two hours of each day in a car

There’s some good and some bad from each item, but unpacking those will help you make a more informed decision. Many of these mental models are intended to help with decision making, and second-order thinking is a great one to add to your toolkit.

Redundancy

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The mental model of redundancy is one that we’re all familiar with, and likely becomes more engrained as we grow older and learn from our mistakes. Creating redundancy can take a bit of work, but has tremendous benefits if it’s called into play.

A common redundancy that I push for is to keep more backups on hand. This post that you’re reading is backed up in at least three places other than my website (two daily backups that we run, plus the automatic backup from our hosting company). There is essentially no chance of ever losing the content from this site, or any site that we manage.

We see this a lot in travel as well. Your car likely has a spare tire; the tire cost extra money and takes up valuable space in your car, but in the event that you need it, it’s worth gold.

Airlines are another good example. Planes are full of redundant systems, almost always including multiple engines and even multiple pilots.

Many of us add redundancies after a mistake slowed us down. I think we all know someone that lost the data on their computer and didn’t have a backup, but now they’re fanatical about keeping things safe. The further ahead you can plan and build some redundancies into your life and your systems, the smoother things are likely to go for you.

Reason Respecting

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The mental model of Reason Respecting, sometimes known as “Because Why”, is the idea that giving reasons behind requests make people much more likely to succeed at the task you’ve given them.

There are a few good examples of this, some of which almost seem counter-intuitive. The 1977 Copy Machine Study is a great example. In this study, people waiting in line to make copies asked the person in front of them if they could cut in, and gave a variety of reasons why. Even silly reasons, such as “Excuse me, I have 5 pages. May I use the xerox machine, because I have to make copies?” caused the vast majority of people to allow them to cut in.

Another common example of this is the idea of “Commander’s Intent”. As summarized in the Harvard Business Review:

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Commander’s Intent is the definition and description of what a successful operation will yield. Good Commander’s Intent allows employees and teams to adapt the plan using improvisation, initiative, and adaptation to reach the original plan objectives.

Helping people to understand the “why” of a task is important for motivation, but can also help tactically. If people know why a certain task needs to be done, they’re more able to find unique solutions to the problem rather than just plowing ahead even if the initial plan doesn’t seem to be working.

Fortunately, this is a pretty easy concept to start using. The next time you give direction to someone, spend a few extra seconds to explain why. The difference between “the client needs that graphic by Thursday” and “the client needs that graphic by Thursday because they’re meeting with investors” only takes a few seconds to say, but can make a big difference in how things get done.

Probabilistic Thinking

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The next mental model I’m digging into is the idea of Probabilistic Thinking. This is the effort to try to determine the likelihood of any particular outcome ahead of time. With most things in life there are an infinite set of factors to consider, but you can focus on the big ones and get a fairly good idea of what the odds look like for any situation.

According to the Farnam Street blog, there are three good tools you can use to help estimate outcomes.

Bayesian thinking

Given that we have limited but useful information about the world, and are constantly encountering new information, we should probably take into account what we already know when we learn something new. As much of it as possible. Bayesian thinking allows us to use all relevant prior information in making decisions. Statisticians might call it a base rate, taking in outside information about past situations like the one you’re in.

Fat-tailed curves

A “fat-tailed curve” is similar to the normal bell curves that you’ve seen before, but it’s more flattened out, meaning a wider variety of possibilities exist. Farnam explains it like this:

In a bell curve type of situation, like displaying the distribution of height or weight in a human population, there are outliers on the spectrum of possibility, but the outliers have a fairly well defined scope. You’ll never meet a man who is ten times the size of an average man. But in a curve with fat tails, like wealth, the central tendency does not work the same way. You may regularly meet people who are ten, 100, or 10,000 times wealthier than the average person. That is a very different type of world.

Understanding the parameters of what you’re looking at will help guide your thoughts.

Asymmetries

This is a natural tendency that we have, which we should fight to overcome. In many cases, we’ll give too much weight to one side of a situation, generally the over-optimistic side, when it really should be more balanced.

Farnam gives two examples: One is related to projected investor returns, which almost never hit their goals (some of the stocks do, some don’t, and you meet in the middle). The other is how we calculate travel time to meeting:

How often do you leave “on time” and arrive 20% early? Almost never? How often do you leave “on time” and arrive 20% late? All the time? Exactly. Your estimation errors are asymmetric, skewing in a single direction. This is often the case with probabilistic decision-making.

This is a fascinating and complex topic, but becoming good at it can generate huge advantages. I encourage you to read the full post on the Farnam blog for more examples and details.