I would probably argue that the complexity of explanations should match the complexity of the phenomenon you're trying to describe.
After a couple months more thought, I still feel as though there should be some more general sense in which simplicity is better. Maybe because it's easier to find simple explanations that approximately match complex truths than to find complex explanations that approximately match simple truths, so even when you're dealing with a domain filled with complex phenomena it's better to use simplicity. On the other hand, perhaps the notion that approximations matter or can be meaningfully compared across domains of different complexity is begging the question somehow.
This essay claims to refute a popularized understanding of Occam's Razor that I myself adhere to. It is confusing me, since I hold this belief at a very deep level that it's difficult for me to examine. Does anyone see any problems in its argument, or does it seem compelling? I specifically feel as though it might be summarizing the relevant Machine Learning research badly, but I'm not very familiar with the field. It also might be failing to give any credit to simplicity as a general heuristic when simplicity succeeds in a specific field, and it's unclear whether such credit would be justified. Finally, my intuition is that situations in nature where there is a steady bias towards growing complexity are more common than the author claims, and that such tendencies are stronger for longer. However, for all of this, I have no clear evidence to back up the ideas in my head, just vague notions that are difficult to examine. I'd appreciate someone else's perspective on this, as mine seems to be distorted.
Essay: http://bruce.edmonds.name/sinti/