In my view, all results should be expressible in the form: because the real world data exhibits empirical structure X, algorithm Y succeeds in describing/predicting it.
I think you are restating the No Free Lunch theorem but that isn't a rare belief is it?
Sure, many people are aware of the NFL theorem, but they don't take it seriously. If you don't believe me, read almost any computer vision paper. Vision researchers study algorithms, not images.
I searched the posts but didn't find a great deal of relevant information. Has anyone taken a serious crack at it, preferably someone who would like to share their thoughts? Is the material worthwhile? Are there any dubious portions or any sections one might want to avoid reading (either due to bad ideas or for time saving reasons)? I'm considering investing a chunk of time into investigating Legg's work so any feedback would be much appreciated, and it seems likely that there might be others who would like some perspective on it as well.