@chao Fun fact: deciduous trees evolved in the higher latitudes where shedding their leaves in winter gave them an advantage over evergreens, since they could then have big leaves that didn't need to survive winter. These trees then slowly started spreading back south until they more or less replaced the evergreens in the equatorial rainforests. They still shed their leaves, but because there's no winter there, they do it ALL THE TIME.

@Terrana

that's really cool! and I'm guessing that's not a complete disadvantage, as it helps plants around them and means that they benefit from keeping it around?

or, if they poison the leaves somehow, it keeps away dangers?

@chao This constant deposition of leaves keeps the soil there very fertile, but it's naturally of fairly poor quality without it, as farmers find to their detriment when they clear rainforest hoping for good arable farmland. So they have to keep clearing to get new areas of fertile soil so they can grow enough food to live. This is a significant cause of rainforest depletion, though less significant than logging.

@Terrana

ohh! I think I remember learning about that a little in geography class! though I didn't know it was because of the leaves!

that is extremely cool! :D

@chao I love learning and I love sharing what I've learned!

@Terrana

learning = awesome! :D

(mathematical /fact/!)

@chao Well, strictly speaking, (learning ∈ awesome). That is, learning is a member of the set of things which are awesome, but there are other awesome things besides learning.

@chao Expressed in terms of formal logic, it'd be learning ⇒ awesome, learning implies awesomeness. For true equivalence, it would also have to be true that awesome ⇒ learning, and not all awesomeness involves learning (∃awesome : ¬learning — there exists an awesome thing which is not learning). And now you're learning about set and logic notation!

@Terrana

hmm. the best way to consider the awesomeness of something would probably treat it as an n-dimensional set of results where each possible factor has a corresponding coefficient in their dimension

eg [learning, 1] ∈ awesome and [learning, 2] ∈ educational

so in the 2-plane of awesome-educational, learning is a vector of (1, 2)

and then you can add vectors of different factors to evaluate the outcome

@Terrana

(this method is similar to an extra-dimensional version of probability! just without constraining it to [0, 1] :D)

@chao This might just be the best conversation I've had all week.

@Terrana

*is proud*

and yeah, this conversations has been awesome! :D

(though without context, I can't determine how awesome!)

@Terrana

if we assume that every dimension of this is equivalent to a real number line and everything maintains relative size*, then we can determine if something appears to be a minimal dimension, too!

because if this doesn't work, clearly it's composed of multiple smaller dimensions! :D

*(eg it's well-ordered and the like, so things don't change just because you compare it to different things)

@Terrana

example:
if one cat has cuteness 1, and another has cuteness 2, and a third is cuter than the second, but you determine the first is cuter than the third

then you can be sure that cuteness as actually made up of smaller dimensions! like behavioural cuteness and aesthetic cuteness, for example!

this is also a good way of rating music and games, I suspect! :D