Sublime

come from the Karl Popper/Imre Lakatos school of falsificationism. Like them, I don’t believe there are right answers or wrong answers, just better ones and worse ones. One should always use the best model available, but watch closely to see whether it produces the outcomes that it promised. If it does, keep using it. If it doesn’t, then you should

As it is written in Twelve Virtues: If in your heart you believe you already know, or if in your heart you do not wish to know, then your questioning will be purposeless and your skills without direction. Curiosity seeks to annihilate itself; there is no curiosity that does not want an answer.

Suspend your disbelief and start by assuming that all matches are good. Then try excluding all matches that don’t have some attribute. Repeat this for each attribute, and choose the one that excludes the most bad matches and the fewest good ones.

You should suspect motivated continuation when some evidence is leaning in a way you don’t like, but you decide that more evidence is needed—expensive evidence that you know you can’t gather anytime soon, as opposed to something you’re going to look up on Google in thirty minutes—before you’ll have to do anything uncomfortable.

Among the topics I haven’t delved into here is the notion of an optimization process. Roughly, this is the idea that your power as a mind is your ability to hit small targets in a large search space—this can be either the space of possible futures (planning) or the space of possible designs (invention).

It’s called the description-experience gap. In study after study, people fail to internalize numeric rules, making decisions based on things like “gut feeling” and “intuition” and “what feels right” rather than based on the data they are shown. We need to train ourselves to see the world in a probabilistic light—and even then, we often ignore the n

Prioritizing long-term interests over immediate gains Constructing effective arguments Second-order thinking and realizing long-term interests:

That sort of error is called “statistical bias.” When your method of learning about the world is biased, learning more may not help. Acquiring more data can even consistently worsen a biased prediction.