Systems Thinkers and Powerful Patterns

Paul Graham says that the best preparation to being a start-up founder is to just learn about things you find interesting, and to be naturally interested in valuable systems.

How To Learn as a Systems Thinker: dev bootcamp’s theory of learning
Jesse Farmer thinks that the most effective students have a clear model of how the world works and are able to quickly integrate new information into that model.

A clear model isn’t necessarily accurate (unbiased), but it is precise (low variance)


precision makes for easy incorporation of new information, regardless of its contradiction to the existing model. someone with a muddled mental model may not even realize when a piece of information is contradictory.

“If a student is a savings account and the thing being deposited is knowledge, we have two parameters: the knowledge-balance and the knowledge-APY.” Most evaluations of education focus on knowledge-balance. But the knowledge-APY — the return on every knowledge-dollar invested — is a much stronger sign of potential ability. (Farmer)

Exponential Learning
A theory: knowledge-APY is gained by creating hooks for new facts to stick onto. Chess grand-masters can play games in their head because of these relational hooks. When applied to the world, developing these hooks allows one to accumulate knowledge at what is colloquially called an exponential rate.

“As our knowledge grows, so does the shore of our ignorance” (Marcelo Gleiser). Could this quote be an interpretation of the bias-variance trade off, which may support an optimal balance between epistemic rationality (low bias) and operational rationality (low variance, i.e. more interpretable)?

A practice: “The knowledge whose utility drops sharply is the kind that has little relation to other knowledge.” (Paul Graham)

Flow is a single-minded immersion and represents perhaps the ultimate experience in harnessing the emotions in the service of performing and learning. a perceived fit of skills and task demands could be the central precondition of flow experiences. Some of the challenges to staying in flow include states of apathy, boredom, and anxiety. personality traits that may lead to more frequent states of flow include curiosity, persistence, low self-centeredness, and a high rate of performing activities for intrinsic reasons only. (Wikipedia)

“do easy” is the practice of applying flow to everyday life. subtle and pervasive optimizations.

growth mindset removes the inhibitors within personal narratives

turbocharged training is the process of increasing difficulty after passing the optimal ratio of difficulty/skill. (CFAR)

A scope: powerful patterns
“Great cities attract ambitious people. You can sense it when you walk around one. In a hundred subtle ways, the city sends you a message: you could do more; you should try harder. The surprising thing is how different these messages can be. New York’s dominant message is to make more money. Boston’s is to be smarter. And as much as they respect brains in Silicon Valley, the message the Valley sends is: you should be more powerful. What matters in Silicon Valley is how much effect you have on the world.” (Paul Graham)

What systems are best to cultivate into your intuition, and to increase your chances of having a larger impact on the world?

an attempt at a progression:

1) fundamentalist systems (like physics and economics) contain many of the most influential models, and are appropriate to the style of academia.
2) engineering systems are best in practice, since it is a break from ideals towards managing trade-offs. Programming (for its feedback mechanisms, computational abilities, networks, and existence as pure text) allows you to reify solutions across domains.
3) The most successful future software/APIs won’t attempt to replace human functions, but complement them. This kind of software is inherently data-driven, because of the complimentary strengths of silicon technology. Therefor our interface will increasingly demand an understanding of statistics.
4) what knowledge and technology can augment specific applications of experimental design? How can this help us better find the largest unknown needs/interventions in the world, and calibrate the process of scaling in irregular contexts? The hidden existence, and future influence, of these secret models is an effective truth.


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