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Fiction is irreducible

I recently tried to suggest a friend read Liu Cixin’s The Three Body Problem, and it was fascinating how much of a struggle it was to come up with a convincing argument to read such an extraordinary book. The best reason I could come up with: “It has such cool world-building, and the characters are so dramatically different than the ones in most western fiction novels… you have to read it, man.” It was an endorsement that pales in comparison to the incredible experience of reading the book. I wondered why it’s so easy to talk about and recommend non-fiction books while it’s impossible to capture the essence of good fiction.

Fiction is fundamentally irreducible. Whereas non-fiction is easy to summarize into a few key points, the narrative structure of fiction makes that impossible. If you attempt to summarize fiction, the best you can do is water it down into some platitude that pales in comparison to the underlying novel. Why is that? Perhaps it is because, at its core, all fiction follows an archetypical story — the foundational pattern of all stories are the same. As Paulo Coelho remarked: “Borges said there are only four stories to tell: a love story between two people, a love story between three people, the struggle for power and the voyage. All of us writers rewrite these same stories ad infinitum.”

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Laszlo Polgar and his effort to systemically cultivate genius

If you’ve ever read books about learning or chess history, you’ve likely heard the name Laszlo Polgar before. Polgar was a Hungarian psychologist who dedicated his life to training his three daughters to become chess champions (he was interested in studying the creation of genius, as to whether it was innate or cultivated).

He chose chess for his genius-cultivating endeavor because it was something in which was fairly structured and systematic. In the end, he succeeded quite dramatically, with two of the sisters becoming the best female chess players ever and the other sister becoming a “mere” International Master (one step removed from the highest Grandmaster level).

One fun fact is that in the course of training his daughters in chess, he single-handedly created one of the largest chess databases in the world (perhaps only rivaled by the Soviet secret archives!)

Tiling-tree problem decomposition method for research

I was listening to a podcast where Tyler Cowen interviewed Ed Boyden (linked here), a fascinating professor at MIT. Boyden presents the “tiling-tree” problem-solving technique, which is about thinking backwards from a problem and enumerating through all possible tools for solving the problem. I thought it was a really fascinating and systematic research approach.

Boyden gives the example of optogenetics (his research field), where he framed the problem as: how can you control the brain to repair it? The group began thinking about how you could control the brain and realized there are only so many different kinds of energy: mechanical force, magnetism, electricity, light. Then from each of these ideas, he would break them down further into discrete “nodes,” and then he would test the bottom nodes with a literature search or doing an experiment.

Apparently, Fritz Zwicky was the pioneer of this thinking (Zwicky called it “morphological analysis”) in the 1930’s. He used this method successfully to imagine (and then discover!) dark matter and a bunch of other ideas in astrophysics.

In essence, Boyden is reasoning from first principles in this problem-solving approach.

First principles

Many investors and thoughtful decision-makers will often reference the fact that they made a decision based on first principles. What exactly are first principles?

First principles are the foundational facts upon which knowledge in a certain field is built upon. For example, first principles in physics: inertia, momentum, gravity, etc. Often you will hear people refer to an “a priori” or “ex ante” statement. Generally speaking, this just means that it is something deduced from first principles.

Elon Musk is often touted for his ability to reason from first principles. Another (less-known) thinker who is also quite good at reasoning from first principles is Nick Kokonas, as evidenced through his reasoning about the restaurant industry.

However, reasoning from first principles isn’t necessarily a free lunch. As Cedric Chin from Commonplace writes, reasoning from first principles may fail if:

  • you have flawed assumptions
  • you make an incorrect inference step
  • you start from the wrong set of principles/axioms/base facts (or an incomplete set of assumptions) (this is probably the most common mistake)

It’s always an enlightening exercise to try to think through the first principles of any given field. Often, you’ll find that a “foundational principle” that you held to be true was merely an accepted assumption. These are the areas that are very interesting to think further about.

The Evergreen note-taking process mirrors how we want to actually learn

Upon encountering a new piece of interesting information, I want to be able to understand how it relates with my current ideas. What ideas does it change? What ideas does it agree with? Which ideas does it contradict? How does it fit into my general framework of understanding?

When I encounter something new (even assuming that I’m able to make a connection to something), it generally is a very superficial connection. For example, if I was learning about Nick Sleep’s investment process and how it’s long-term oriented, I might find that it is very similar to Buffett’s process; at most, I’d make a quick mental note of that, and move on. What connection that might exist is a weak, ephemeral first-order connection. However, what I actually want is to not only realize that Nick Sleep’s investment process is similar to Buffett’s (the first person who popped into my mind), but also see what other individuals have had similar ideas? What are the similarities? What are the differences? Furthermore, I want to generate understanding of second-order connections, and an existing linked structure of notes makes this discovery much easier.

The evergreen note-taking method (borrowed this name from Andy Matuschak) takes this implicit process and makes it explicit, while also furthering the reach. Effectively, you are forced to integrate each new piece of information immediately, and it lives on.

It takes effort (it’s simple, but not easy), but it is the closest match of any system that I’ve seen to the natural learning process.

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