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imbatman comments on Evaluating Multiple Metrics (where not all are required) - Less Wrong Discussion
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Just thought I would try to make it easier to follow. An alternative would have been to declare my terms, I guess. I haven't really developed a strategy for that -- just thought I'd try this.
Bolding or italicizing each special term the first time it appears in the text and writing it in regular typeface afterwards would probably read better, while still drawing attention to the relevant special concept words. People can keep picking out the word better without the typeface once they've been primed by the first mention to assume the word denotes an important concept.
I liked this idea, which carried the added bonus of only taking a few second to implement. Better?
Looks good to me now.
Thanks. Do you think the vote downs have to do with the content? Is this not a relevant topic for this forum?
I guess the downvotes might be a combination of the thing post being a lot more in the idea stage than a worked out solution stage and it being about rating movies, which as itself isn't a very relevant topic.
The general idea of working out preferences using vectors instead of scalars does seem like a forum relevant topic to me, but your post leaves the details of making an actual working implementation, coming up with interesting use cases beyond movies and figuring out how the vector approach would be a significant improvement over a scalar approach in them up to the reader, so it's a bit thin as it stands.
Here are some possible definitions you might consider using.
Class: A concentration of unusually high probability density in Thingspace.
Type: A subclass. An even denser area of thingspace or conceptspace within a cluster of things.
Metric: a scale you use to measure a single trait of something. In humans, that could be height, weight, hair color, etc. In order to be useful, a metric must give you further information about that thing as opposed to other things in its class/type (there must be significantly more variability along that dimension than others, in terms of thingspace).
In regards to the article itself, it highlights the difficulty of projecting a multidimensional space (with the number of dimensions equal to the number of metrics you're using) and a complex distribution of "goodness" within that space to a single dimension of goodness with minimal complexity and minimal loss of information.
Why did you think that? Have you paid attention to your own experience reading things with bold? I recommend reading Razib Khan and paying attention. I find that his use of bold makes it more difficult to read the whole article, but easy to read just the bold passages, which is usually the right choice.