Tracking what you know to know more.

I recently read an article by Shawn Wang called Learn in Public. The essence of the article is this: If you want to get good at something, make your learnings public.

Now, it doesn’t necessarily need to be written, it can be a speech, a conference, or even just helping someone else learn what you’ve learnt one on one. Nor does it need to always be public, you could keep a private blog where you record what you learn and try to explain it to yourself. But whatever you do, don’t just learn and leave, instead learn and create.

Lifelong learning is more important than ever. In a changing information economy, the skills that are valuable change every year. However, being able to learn throughout your whole life is a skill that will always be valuable. Cal Newport, in his book Deep Work, describes two skills that are more valuable now than ever: the ability to produce at an elite level, both in terms of quality and speed, and the ability to learn hard things. (If you haven’t read deep work then I highly recommend it.)

In another blog post, Shawn describes different types of learners through the lens of algorithmic efficiency. If you aren’t familiar with the notation used to describe how efficient an algorithm is, I’ll very quickly summarise. It is written with “big-O notation”, and looks like this: O( _ ) where the inside of the brackets changes depending on how fast an algorithm takes to finish running. Often the inside of the brackets will perform some calculation on the size of the data that the algorithm is processing, as more data tends to take longer to process. One example is O(n), which will increase in running time with the amount of data you feed in. Another example worth noting is O(n²) which increases in running time exponentially with the size of the data it is working with. Instead of running time and data size, learning algorithms or methods explain how much you’ll know depending on how long you’ve been learning.

The worst case scenario for learning style/learning algorithm is L(1) (like big-O notation, but for learning rather than performance, clever eh?). This means that no matter how long you use this style of learning, you will only know one thing. This is the case for someone who isn’t learning at all. Shawn calls this a “One Trick Pony”. The next worse case Shawn calls a “Lossy Learner”. This performance is L(log N). If you can remember what a log graph looks like from high-school algebra it grows quickly to start, but over time grows slows until any increase is nearly imperceptible. This category of person learns, but forgets what they learn and repeats the same mistakes. Most people are here.

The ideal learner, in most people’s minds, would then learn at a rate of L(N). Every year they learn some more, and they don’t forget what they learn. If they make a mistake once, they don’t then make further mistakes. But you can do better. What if when you learnt something new, you reflected on how you learnt it. When you learnt something useful, you reflected on where you learnt it from, who you learnt it from, and adjusted your behaviours to learn quicker in the future. What if, in addition to this you reflected on the areas that you were learning the most useful stuff in, and made shifts to learn even quicker in the future, ahead of when you’d need it. This kind of learning is exponential. By reflecting on what you learn and using that information to guide future knowledge acquisition, you learn faster in the future. The learning notation for this is L(N²). While this may not actually be possible for people to maintain consistently, it is a goal to strive for.

Learning in public comes into the equations described by Shawn in the form of an extra variable P, which stands for “Peers”, “Public”, or “Possible Mentors”. And that is one of the reasons that learning in public helps you learn more. By writing publicly about what you learn, you might get noticed by people who are experts in the field. By interacting with the topic you can talk with the people who know the field well and add something to the conversation. It doesn’t matter if you are wrong, or have misunderstood an idea. By creating in public you are bound to be corrected. Learning in public has an L notation of L(PN), or L(PN²).

How does learning in public work? Now, I can’t say for certain. I haven’t done any experiments on the topic, but here are a few ideas I have as to why learning in public helps.

1. If you know you are going to write about it, you pay more attention the first time. Additionally, when you write up what you’ve learnt, you might find gaps in your knowledge that you hadn’t noticed. You go back to the original material, you look at additional material, you synthesise multiple sources to get a complete integrated understanding of the topic.
2. When you explain a topic to someone else, learn it better yourself. This is known as the Protege Effect. Part of the protege effect is the point mentioned above, that when you are going to teach a topic you spend more time learning it. But that isn’t all there is. When you teach a topic, you are forced to move the parts of it around and lay it out in a way that makes sense. You also monitor what you say to make sure that it makes sense, and that you don’t think anything that doesn’t make sense. This process ensures that your knowledge is sound and reliable. [1]
3. You get feedback. An important requirement exponential knowledge growth is solid foundations. Whether starting to learn in a new field or pushing towards the edge of what you know, having other people correct the foundations early will save you a bunch of time further down the line. And people won’t remember your errors, they’ll remember the interaction they had with you and that you were keen to learn. Building that kind of impression is far more valuable than always being right.
4. You get to connect with experts and get opportunities to learn from the best. When you write about a topic that you are learning about and are interested in, people start to see you as a content creator. Because only 1% of the Internet is estimated to contribute [2] (the rest commenting or lurking), creating publicly is one of the best ways of putting yourself in that visible 1%. When you are in that 1%, you will have more value to offer to a conversation, so the experts will be more likely to be willing to chat with you.

As I’m new to this idea, this article is my first attempt at learning in public, and I can tell you that it has certainly helped me learn more about the idea of Learning in Public. Without writing this article, I wouldn’t have found out about the Protege Effect nor about the 1% participation effect. As a next step for myself, I’ve started thinking about how I can write about what I am learning at the moment (how to protect a population against Fake News, and how subjective pricing is).

What could you learn in public? It could be something you are currently learning about but what to learn more on, or something you have an interest in but aren’t an expert in. Let me know what you think.

[1] – you can take advantage of this effect without the public aspect. It is called plastic platypus teaching, or in the coding world this is known as rubber duck debugging. However, the learning effect has recently been suggested to be just another part of the “testing effect“.
[2] – This follows Zipf’s law.