Note that the title is misleading. This is really countable dimension factored spaces, which is much better, since it allows for the possibility of something kind of like continuous time, where between any two points in time, you can specify a time strictly between them.

Fixed, Thanks.

Yeah, also note that the history of $X$ given $Y$ is not actually a well defined concept. There is only the history of $X$ given $y$ for $y\in Y$. You could define it to be the union of all of those, but that would not actually be used in the definition of orthogonality. In this case $h^F\left(X|y\right)$, $h^F\left(V|y\right)$, and $h^F\left(Z|y\right)$ are all independent of choice of $y\in Y$, but in general, you should be careful about that.

I think that works, I didn't look very hard. Yore histories of X given Y and V given Y are wrong, but it doesn't change the conclusion.

I could do that. I think it wouldn't be useful, and wouldn't generalize to sub partitions.

I don't know, the negation of the first thing? A system that can freely model humans, or at least perform computation indistinguishable from modeling humans.

Fixed, Thanks.

1. Given a finite set $\Omega$ of cardinality $n$, find a computable upper bound on the largest finite factored set model that is combinatorially different from all smaller finite factored set models. (We say that two FFS models are combinatorially different if they say the same thing about the emptiness of all boolean combinations of histories and conditional histories of partitions of $\Omega$.) (Such an upper bound must exist because there are only finitely many combinatorially distinct FFS models, but a computable upper bound, would tell us that temporal inference is computable.)
2. Prove the fundamental theorem for finite dimensional factored sets. (Seems likely combinatorial-ish, but I am not sure.)
3. Figure out how to write a program to do efficient temporal inference on small examples. (I suspect this requires a bunch of combinatorial insights. By default this is very intractable, but we might be able to use math to make it easier.)
4. Axiomatize complete consistent orthogonality databases (consistent means consistent with some model, complete means has an opinion on every possible conditional orthogonality) (To start, is it the case that compositional semigraphoid axioms already work?)

If by "pure" you mean "not related to history/orthogonality/time," then no, the structure is simple, and I don't have much to ask about it.

Yeah, this is the point that orthogonality is a stronger notion than just all values being mutually compatible. Any x1 and x2 values are mutually compatible, but we don't call them orthogonal. This is similar to how we don't want to say that x1 and (the level sets of) x1+x2 are compatible. 

The coordinate system has a collection of surgeries, you can take a point and change the x1 value without changing the other values. When you condition on E, that surgery is no longer well defined. However the surgery of only changing the x4 value is still well defined, and the surgery of changing x1 x2 and x3 simultaneously is still well defined (provided you change them to something compatible with E).

We could define a surgery that says that when you increase x1, you decrease x2 by the same amount, but that is a new surgery that we invented, not one that comes from the original coordinate system.

Thanks for writing this.

On the finiteness point, I conjecture that "finite dimensional" (|B| is finite) is sufficient for all of my results so far, although some of my proofs actually use "finite" (|S| is finite). The example with real numbers is still finite dimensional, so I don't expect any problems.