⁓ SR algos are incentivized to predict retention, not to optimize retention

→ everyone wants their ◊ spaced repetition algorithm to predict ⌣ retention. But as a learner, I want one that makes me learn as fast as possible.

• including learn rate and ⌣ cost per trial

bad incentives:

• imagine you randomly prompt users to make up a ◊ mnemonic and say that is a coin flip, it either doesn't work or gives 100% retention forever — universal metric would punish you for this, right?
  • more specific example: If you have two similar cards in your decks, essentially causing interference, do you trigger an leech prompt that may make it better or worse?
• also, it incentives you (kind of) to build NN algos based on existing/old data, instead of outdoing them
• funnily enough, ‣ Anki would probably "improve" if they wouldn't remove leeches from active learning (because predicting them is easy)

expression of I'm just doing the algo, you do the ⌣ user experience

this whole thing is neighboring ~ the standard button labels in spaced repetition are unscientific and likely terrible