If we have the map be a list and the entries be probabilities, we’d need to somehow represent the difference between someone who believes in lots and someone who believes in very little, even if they have the same angle.
Something like chi-squared might work, but that would end up being dominated by the largest differences, which may be undesirable. But you can probably take roots until you get something that does what you want.
Not that you could compute it...
Maybe we should try to emulate what humans do when they assess how much they have in common with someone. Which is… uh… I don’t know what we do. Probably something like a chi squared, but starting with broad categories and stronger inferences, and including weaker terms rarely or not at all.
Hm, yeah that’s true, we can’t just represent probabilities. But also, we need a way to represent the emotional salience of the probabilities. That could probably be done by creating two new emotional salience vectors that correspond to each belief vector (appending the vectors might work, but would introduce complications in the metric calculations).
If we have the map be a list and the entries be probabilities, we’d need to somehow represent the difference between someone who believes in lots and someone who believes in very little, even if they have the same angle.
Something like chi-squared might work, but that would end up being dominated by the largest differences, which may be undesirable. But you can probably take roots until you get something that does what you want.
Not that you could compute it...
Maybe we should try to emulate what humans do when they assess how much they have in common with someone. Which is… uh… I don’t know what we do. Probably something like a chi squared, but starting with broad categories and stronger inferences, and including weaker terms rarely or not at all.
Very interesting replies.
Hm, yeah that’s true, we can’t just represent probabilities. But also, we need a way to represent the emotional salience of the probabilities. That could probably be done by creating two new emotional salience vectors that correspond to each belief vector (appending the vectors might work, but would introduce complications in the metric calculations).