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This is generally true but I will add one wrinkle.

And there are three kinds of explanation:

1. visual

2. mathematical

3. linguistic

So sometimes, you understand something visually, or mathematically, but you are forced to put it into verbal terms (say, over a text only channel, or voice), and then you may seem not to be able to explain it even though you understand it.



I first learned about The Big Triangle from Scott McCloud's "Understanding Comics".

http://scottmccloud.com/4-inventions/triangle/index.html

http://homes.chass.utoronto.ca/~mfram/Media/0505-UC-triangle...

Filed under "I believe, but cannot prove": The three-way tension is a recurring pattern.

When trying to understand new things, I often try to reframe things as a triangle. When pondering intractable problems, I favor three-way solutions. Some quick examples from memory...

Project management: time, money, scope.

aka Quality: Fast, good, cheap.

US Govt balance of power: Executive, Congress, Judiciary

Language design: imparative, declarative, functional

Pop music: harmony, melody, lyrics


(A name for the three-way tension you will find a lot: trilemma.)

If you have to budget something, it becomes a soft trilemma: I have 24 hours for work + leisure + sleep, so any given combination will be a point inside a triangle whose vertices are (0, work), (0,leisure), (0, sleep). This structure is called a (2-)simplex.

BTW: a regular dilemma (should I spend or save) is a 1-simplex, and is a simple linear combination asave + (1-a)spend.

Now, this is fun for two reasons:

- You can have (n)-lemmas (i.e. n-fold tradeoff structures) that are modeled as (n-1)-simplices. Actually useful: you can do statistical inference on simplices using the Dirichlet distribution.

- A simplicial complex (basically, a set of simplices) can be used to build topological spaces part by part. This kind of maths (algebraic topology) is a whole "south part of the mountain" climb towards abstract mathematics that bypasses a lot of Cantorian handwringing on the ultra-local structure of topology. Instead, you're computing stuff from the get go -- and indeed one of the emerging machine learning techniques goes precisely from building a simplicial-like complex from data and computing characteristics of its topology.


I love this distinction. I've heard the headline stated before, and even if not directed at me, I find the very idea irritating and even a bit offensive. It's not necessarily that I don't understand it, it's that I can't prove to you that I understand it.

Next time I see this concept in action, maybe I'll suggest alternate methods of "explaining".




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