Ways of Defining Music

The "is-ought" problem is common to meta-ethics and linguistics. In meta-ethics: are we describing how people act, or how they ought to act? In linguistics, some people describe grammar while other prescribe grammar. I imagine the same approaches to understanding music:

1. Descriptive definitions of music, which take into account various musical traditions and extrapolate common themes within and between them.
2. Prescriptive definitions of music, which involve reflection and thought-experiments — philosophy, really — sometimes accompanied by experimental compositions.

To explore the first without the second is superficial, and to philosophize without context is unrealistic.

1/26/08: "Noise" has the same issue, describing how the word "noise" is used is very different from prescribing definitions. Describing things can be difficult, but prescriptions can get really messy. It's probably best to set out a goal for a prescriptive definition before seeking/giving one. Prescriptions might be about: unifying themes of descriptions, offering a new definition that illuminates others, expanding or restricting the dominant contemporary definition...

Compression as a Learning Problem

Humans are pretty good at efficiently communicating information to each other. One of the reasons for this is that we can model the listener's expectations (their interpretation) of what we are saying. If I say "Can I have that?", pointing towards the table, you will hand me the pen because you can see I just pulled out my checkbook. We have long-term expectations, a learned pragmatics to conversation (like you knowing I want to write a check), and short-term expectations, like learning the meaning of pronouns.

If we apply this to a computational context, we get an interesting compression algorithm. Imagine computer A trying to send a file to computer B. It's a binary file, and because it's not noise, there are short patterns here and there. A and B both have the same predictive capabilities — let's say they're both using Markov models — so they can guess what the next bit is with a certain confidence. So here's what happens: A starts sending B bits, and B starts learning patterns in the bits being sent. A is modeling B's mind, so it knows what B is expecting. Now if A knows that B is very confident about the next bit, A doesn't even bother to send it, it just moves on. Thus you only transfer a portion of the information, and the rest is implied.

The problem, of course, is how expensive it is for A to correct B if it makes a wrong prediction. Bit-by-bit would have a lot of overhead, so it would probably be best for A to send a long sequence at a time, coupled with a note about any bits B guessed wrong.

Extreme Pragmatism

I've been skimming through Saying and Meaning in Puerto Rico: Some Problems in the Ethnography of Discourse by Marshall Morris. The majority of the book is focused on "episodes", each documenting a common oddity of the Spanish used in Puerto Rico. It seems like language has become more of a social lubricant than a tool:

The glossing over of distinctions appears particularly in giving reasons or excuses: "A man and his wife are invited to a dinner party. They do not appear. When their would-be hosts next see them, they give their excuses, he to the man, she to the woman, separately. When the reasons are compared, they bear little or no relation to one another, though they seem genuine." Though in the end it appears that the reasons given were not literally true, being in contradiction, they were of the same value: they had the effect of saying that their failure to attend was due to things beyond their control, consideration for other people, and so on. They had the same effect as literal truth, or better. Literal answers are often more than people want or require, and they are resisted, over-looked and sometimes resented. The effect is what counts.

I'm reminded of the phrase "hung up" from Del Close's classic "How to Speak Hip".