Dimensional decoupling

[-]sapphire8y50

Great post. Decoupling nice/generous is extremely useful. Lots of people look for signs of being 'nice' when they really want someone generous.

[-]Said Achmiz8y40

This post seems to be pointing at something interesting and useful, but it seems to me that (apart from any criticisms such as this one) the main problem is that you don’t actually tell us how to do dimensional decoupling!

Yes, in a case where we already know that concept X actually decomposes into two orthogonal concepts Y and Z[1], then we can go ahead and draw the graph, identify what point we want to occupy, and proceed thereto with all haste. But isn’t the whole problem that we often don’t know that some concept X which we are using may be usefully decomposed—and that even if we do consider the possibility, we still do not know what orthogonal concepts Y and Z (if any!) our concept X decomposes into?

So I think the meat of this technique is missing; were it present, it would provide us with a systematic way of answering these two questions:

How do we identify situations where we are using some concept which may be usefully decoupled? Or, alternatively: which of our concepts in fact constitute couplings of two (or more?!) orthogonal[2] concepts—and how do we tell?
Having identified a concept which is, in fact, a coupling, just how do we decouple it? Ok, so we have some concept X which we have (somehow) decided may be decoupled into two (or more!) orthogonal concepts Y and Z. Now, how do we identify Y and Z? (And how do we verify that Y plus Z is, in fact, what we originally thought of as X?)

[1] Where either Y or Z may have the same label as the label we were using for X.

[2] Note that some pairs { Y, Z } in such situations may not be entirely orthogonal, i.e. there may be a systematic (perhaps because causal) correlation between them. For example, is niceness correlated with generosity? To my naive intuition, it seems plausible that there should be some non-zero correlation. Answering this sort of question seems to me to be critical to any attempt at “decoupling”, lest we delude ourselves into imagining a world where all points on the supposed (Y,Z) plane are available for occupancy, when in fact nothing like that may be true at all.

[-]brianlui8y20

How do we identify situations where we are using some concept which may be usefully decoupled? Or, alternatively: which of our concepts in fact constitute couplings of two (or more?!) orthogonal[2] concepts—and how do we tell?

Great catch. This is something I didn't mention in the article because I typical-minded. Here's a description, which I will add back to the article later probably:

1. Whenever you come across something that seems like it is logical, but violates your intuitions, then there's a high chance that this technique can help. This is an easy situation to use dimensional decoupling and it comes naturally, because we are already in 'interrogative' mode.

2. When you're stuck on a problem, go through your assumptions and try to decouple them one by one. Often you will find that some assumptions can be decoupled and then one of the resulting parts can be relaxed. This is relatively harder and needs practice, because it's not natural to examine our assumptions like this.

Having identified a concept which is, in fact, a coupling, just how do we decouple it? Ok, so we have some concept X which we have (somehow) decided may be decoupled into two (or more!) orthogonal concepts Y and Z. Now, how do we identify Y and Z? (And how do we verify that Y plus Z is, in fact, what we originally thought of as X?)

I believe that the hard work is in identifying the object that needs decoupling. Once it's identified, the decoupling method is relatively simpler.

1. The easiest one is with opposites. Happy vs sad, masculine vs feminine, straight vs gay. These are really easy to decouple. To verify them, we just see whether the two new "corners" make sense. E.g. Is it possible for someone to be interested same-sex and opposite-sex people simultaneously? Is it possible for someone to be interested in neither? Y and Z are just the two poles of the spectrum.

2. For non-opposites, make them into poles. Bias vs accuracy. Bias is one pole, so the other pole is "unbiased". Accurate is one pole, so the other pole is "inaccurate". To verify them, again we see whether the two new "corners" make sense.

Note that some pairs { Y, Z } in such situations may not be entirely orthogonal, i.e. there may be a systematic(perhaps because causal) correlation between them.

Yes! They are almost certainly correlated - that's the entire reason that they are so often seen as entwined. Counterintuitively, higher correlations are often more valuable to decouple. On the last graph, we can also think of it in terms of correlations:

1.0 correlation - this is the 'red zone' where we say crazy things like "loud isn't high volume". The correlation is so high that they shouldn't be decoupled.

0.5-0.9 correlation (roughly) - this is the valuable area. The high correlations means that the two concepts frequently go together. But in the situations where they differ, it's super easy to miss.

0.0-0.5 correlation (roughly) - this is not as valuable. Because the correlation is low, it means that we wouldn't naturally think of them as going together. Therefore, there is low risk that we are incorrectly coupling them.

[-]CronoDAS8y30

A cat-dog? What is even the use of this classification?

Challenge Accepted! ;)

https://youtu.be/OztGJhiBQTo

[-]clone of saturn8y30

The most valuable decouplings are the ones which are really similar, but not identical.

It seems like the most valuable decouplings would actually be the ones we think are similar or identical, but turn out not to be.

Also, how is this concept different from bucket errors?

[-]brianlui8y40

You're right, bucket errors are the result of entwining things. Dimensional decoupling is a way of reducing bucket errors. In my personal experience, once I used dimensional decoupling regularly, it became second nature and automatic. I think it's important to have low-friction ways of reducing bucket errors.

And yes, the most valuable decouplings are ones where they aren't identical but we think they are. But until we try to decouple them, we don't know whether they are or not!

[-]TAG8y20

Can dimensional decoupling fail?

[-]brianlui8y20

Yes, the failure modes are mentioned in the last part of the post: trying to decouple identical things, and trying to decouple unrelated things.

[-]Said Achmiz8y20

Unrelatedly to my other comment: I don’t understand what is being depicted on the last chart. What is the horizontal axis? What is the blue gradient? What are the call-outs referring to…?

[-]Dacyn8y10

The Cornelius/Emma anecdote seems like a red herring... my impression of Cornelius isn't that he was having a hard time making a conceptual distinction between "seems nice" and "is likely to take advantage of me", he just didn't know how to measure the latter...

[-]Elo8y10

Diagrams disagree with each other and don't seem to follow a pattern.

Sad and not happy. Happy and not sad.

Should exist on that diagram.

[-]Said Achmiz8y20

For what it’s worth, the diagrams look fine to me. I didn’t spot any see any inconsistencies, (though I may’ve missed something). Certainly the happy/sad diagram looks just fine.