Cognitive Enhancers: Mechanisms and Tradeoffs
A classic from the Slate Star Codex archive. As the author notes, the medical component of the article is speculative. However its useful from a mental model perspective. It discusses how people adjust their model of reality, and how “smart drugs” alter this process.

In the predictive coding model, perception (maybe also everything else?) is a balance between top-down processes that determine what you should be expecting to see, and bottom-up processes that determine what you’re actually seeing. This is faster than just determining what you’re actually seeing without reference to top-down processes, because sensation is noisy and if you don’t have some boxes to categorize things in then it takes forever to figure out what’s actually going on. In this model, acetylcholine is a neuromodulator that indicates increased sensory precision – ie a bias towards expecting sensation to be signal rather than noise – ie a bias towards trusting bottom-up evidence rather than top-down expectations.

Learning rate” is a technical term often used in machine learning, and I got a friend who is studying the field to explain it to me (all mistakes here are mine, not hers). Suppose that you have a neural net trying to classify cats vs. dogs. It’s already pretty well-trained, but it still makes some mistakes. Maybe it’s never seen a Chihuahua before and doesn’t know dogs can get that small, so it thinks “cat”. A good neural network will learn from that mistake, but the amount it learns will depend on a parameter called learning rate:

If learning rate is 0, it will learn nothing. The weights won’t change, and the next time it sees a Chihuahua it will make the exact same mistake.

If learning rate is very high, it will overfit. It will change everything to maximize the likelihood of getting that one picture of a Chihuahua right the next time, even if this requires erasing everything it has learned before, or dropping all “common sense” notions of dog and cat. It is now a “that one picture of a Chihuahua vs. everything else” classifier.

If learning rate is a little on the low side, the model will be very slow to learn, though it will eventually converge on a good understanding of its topic.

If learning rate is a little on the high side, the model will learn very quickly, but “jump around” between different understandings heavily weighted toward what best fits the last case it has worked on.

On many problems, it’s a good idea to start with a high learning rate in order to get a basic idea what’s going on first, then gradually lower it so you can make smaller jumps through the area near the right answer without overshooting.

Parallelisms for the future
I’m always fascinated with Chinese Communist Party propaganda:

Parallelism,” or paibi (排比), is a rhetorical method that when used with appropriate measure can strengthen an article, but when used carelessly can have exactly the opposite effect.

What the hell is going on?
A mini book length blog post that explains how the change in media has altered society.

The striking parallels between commerce, education, and politics isn’t a coincidence. In fact, it’s inevitable. In the past decade, the information environment has inverted from information scarcity to information abundance, and the effects are evident in every corner of society….

….
The rise and upcoming fall of commerce and universities frame the context for two big shifts, which account for the weirdness of contemporary society: (1) how information scarcity creates authority, and (2) the transition from one-way communication to two-way communication.

The Unwinding of Globalization: Fallen Angels & Behavioral Alpha
Excellent investment letter focusing on the movement towards regionalism, and the breakdown of historical correlations between asset classes.

Confusion creates anxiety but importantly offers opportunities for those able to control their emotions.