[analog31]

If it's a legacy system, then it lives at the edges. The edges are everything.

I wish I could remember or find the proof, but in a multi-dimensional space, as the number of dimensions rise, the highest probability is for points to be located near the edges of the system -- with the limit being that they can be treated as if they all live at the edges. This is true for real systems too -- the users have found all of the limits but avoid working past them.

The system that optimally accommodates all of the edges at once is the old system.

[CuriouslyC]

You don't need a complicated proof, just assume a distribution in some very high number of dimensions, with samples from that distribution having randomly generated values from the distribution for each dimension. If you have if you have ~300 dimensions then statistically at least one dimension will be ~3SD from the mean, i.e. "on the edge," and as long as any one dimension is close to an edge, we define a point as being "near the edge."

It's not really meaningful though, at high dimensions you want to consider centrality metrics.