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Learning Cycle
When you first start using your PKB, it can be tantalizingly tempting to capture everything interesting that you come across. You end up building a huge collection of articles that you've skimmed, and have built what is essentially a bad version of Wikipedia or Google. A variety of techniques and systems have emerged to handle this kind of "Personal Knowledge Management":
In the spirit of Be Useful, it's worth remembering what cognitive problem you're trying to solve with these practices! Very few people that we traditionally think of as "very successful" have publicly stated that they use any processes like this. Indeed, Niklas Luhmann, the creator of the Zettelkasten method, was an extremely prolific writer, and only near the end of his life documented his practices in a couple of small essays that became what we call Zettelkasten today.
It suggests the question: is it Zettelkasten that made Luhmann successful, or is it that he was relentlessly focused on problems he cared about, and Zettelkasten was the set of cognitive processes that he designed for his brain and his problems?
These systems are seductive because writing notes feels like work. You can see yourself filling out holes in your knowledge! It constantly feels like you're on the verge of achieving a dense enough network of facts and links from which novel insight and a PhD thesis will emerge. To cut through this, let's revisit some of the general cognitive problems you might be facing, so that you can evaluate how to pick and choose the best parts from these systems for yourself.
What is Learning?
One of the most salient cognitive problems is the question of learning. What does it mean to do it well? Can we do it faster? There are a variety of frameworks for thinking about this, such as the Dreyfus model, the Bloom Taxonomy, the Benner Progression, or even the guild progression (Novice -> Journeyman -> Master). I think it's actually illustrated pretty well in the following meme:
The point is that we start learning about the world by collecting data and observations. Babies learn language not by being told formal rules of grammar but by copying what people say to them. Children learn to ride bikes by using training wheels, not by studying physics. Even when studying advanced abstract mathematics, one of the main tools is to use concrete examples and simplified examples to build intuition.
On top of these observations, we invent concepts so that we can easily talk about and work with a collections of observations at once. For example, the concept of "fruit" makes it easy to talk about the entire class of products that enable certain plans to reproduce. Of course, useful concepts are typically used in multiple contexts (e.g. botany vs. kitchen), which results in arguments over things like whether tomatoes are actually fruit.
As you collect more and more observations and theories in a particular area, you'll start seeing how disparate parts of a domain are connected, or you may even be able to draw connections across the boundaries that people traditionally place when studying these things in school.
Novice
How are we supposed to talk about options strategies if you have to spend 5 seconds thinking about what the payoff for a call option is?
The role of rote practice, while overused in schools, is probably underused by adults. An appropriately-sized amount of concrete practice or experience can be incredibly important for getting some actual data which will serve as the foundation for knowledge, and then insight. Skipping this step will generally lead to the kinds of failure we see when someone has clearly only read about something but never actually tried what they read, or when someone tries to reason about a domain they aren't familiar with by analogizing it to their own. The high-level ideas sound OK, but because no two domains are ever the same, there's always some concrete way in which the ideas won't work.
Therefore, make sure you're using your PKB in a way that is conducive to the things you need to do to learn as a novice. Oftentimes, you should just use it as a scratch space and not spend much time worrying about structure: your early focus is on gathering data points, whereas theory building will come later. Oftentimes, the "end product" of your PKB usage early in the learning cycle doesn't really matter, and that you may find yourself deleting large chunks of it - this is perfectly normal. At this stage, your PKB is often just a place to help you externalize cognition and provide temporary guiding structure so that your working memory isn't overwhelmed.
Last modified 4mo ago
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