The list

I wrote these down at the start of a learning stretch, to find real uses for later, not to apply straight away:

  • Parkinson’s law: work expands to fill the time given it.
  • Hofstadter’s law: it takes longer than you expect, even when you expect that.
  • Hanlon’s razor: don’t assume malice where carelessness explains it.
  • The Pareto principle: most results come from a small share of the effort.
  • The Peter principle: people rise until they reach a job they’re bad at.
  • Hick’s law: more options, slower decisions.
  • Goodhart’s law: when a measure becomes a target, it stops being a good measure.
  • The Dunning-Kruger effect: the less you know, the more you overestimate what you know.
  • Occam’s razor: the simplest explanation is usually right.
  • Chesterton’s fence: don’t remove something until you know why it’s there.
  • Brooks’s law: adding people to a late project makes it later.

The two that stood out

Chesterton’s fence is close to the instruction I’d already given myself for restudying the settlement engine: understand it before touching it. Goodhart’s law is one to watch once Routine Machine’s streaks and completion rates fill up with real data. The system exists to serve the goals behind it, not to make its own numbers look good.

What I took from it

  1. Collect principles before I need them, and wait for real uses.
  2. Understand a thing before changing it.
  3. Watch my own metrics for the moment they turn into targets.