tl;dr: The mix of jargon and abstraction, along with a rambling informal style, makes it hard to offer any constructive suggestions or even comments on the above. Perhaps being more explicit, and talking in terms of updating from evidence might help.

I really wanted to read this post and contribute to the discussion, as I think the main ideas resonate with things in my field: a set of potentially influential factors with complex and somewhat unknown correlational structure, but for which only a subset have been considered seriously by the dominant views in the field (until recently).

But I was unable to work out what you are aiming to do with the post, and I suspect the same is true of many other LW readers as no-one else has commented in the last couple of days either. After all, there are many commenters who tend to be quick to get involved in discussions related to probability and uncertainty; appropriate treatment of complex datasets to reveal underlying patterns and so on. You did get a couple of upvotes and no downvotes so far, so at least a couple of people see promise in the post. As you asked for metacomments about the post, I thought it worth doing so.

The first section (#1) aims to illustrate the problem but it is presented in a very jargon-heavy or field-specific manner, enough that I can't work out what your "simple question" actually is, or why you are asking it, or what the point about 4n is meant to illustrate. My interpretation after a lot of thought is that you might be talking about updating in a Bayesian sense: thinking about your current belief state and potentially adapting your views given the new evidence that (something = 4n in some circumstances), with greater updating distance for evidence that falls outside the expected range.

If this is the case, then part 2 is about priors ("dummy set of data I expect right now", "Our imaginary correlations") and then updating based on published research. But this section is presented in such abstract terms that it's entirely inaccessible to me, possibly even if I understood the issues about ploidy level from section 1. That is, labeling factors as A:H and then talking about their possible correlational structure, but without giving any clues about the questions you are trying to answer using such data, makes it seem rather hopeless. Are you trying to predict some kind of outcome measure(s)? Or find underlying factors responsible for a subset of the measures you already have? Or reject theories in the field & provide alternatives?

These seem like very relevant issues spanning a wide variety of research fields: how does one deal with a parameter space of high dimensionality (whether informally, as in the discussion in the OP, or formally, as in explicit modelling).