"Striving to find it" and "moving closer to it at every opportunity" can be very different things.
When the "perfection" in question is something that you know is impossible to achieve (and in any given nontrivial case, you know you'll be unable to establish you've achieved it even if by chance you did), establishing it as your goal - which is what "striving to find it" is - is foolish.
On the other hand, finding simpler models certainly is a good idea. But it's good not because it gets us "closer at every opportunity" to the Kolmogorov-simplest model, for two reasons. One is stated in the parentheses above, and the second is that "closer" is almost meaningless, when you know that you not only cannot compute K, you can't in general put upper bounds on it either (by Chaitin's Incompleteness), which means that you have no idea how closer you're getting to the ideal value with every particular simplification.
Is it known what is the highest complexity, beyond which the Chaitin's Incompleteness applies? If it is relatively large, it is possible that all hypotheses interesting for humans have complexity lower than that...
Today's post, The Dilemma: Science or Bayes? was originally published on 13 May 2008. A summary (taken from the LW wiki):
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This post is part of the Rerunning the Sequences series, where we'll be going through Eliezer Yudkowsky's old posts in order so that people who are interested can (re-)read and discuss them. The previous post was The Failures of Eld Science, and you can use the sequence_reruns tag or rss feed to follow the rest of the series.
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