This is pretty cool. I have several points to make. 1. We all know that Cellular automata or more generally any dynamical system of sufficient complexity (and maybe not too much complexity) will be Turing complete, will have complicated "uncomputable" behavior, will have perhaps pattern formation, or gliders, solitons etc. So what is a valuable addition to this these computational investigations? I think when studying emergent computational behavior we really care about dynamics complexity / rules complexity. It's not impressive to get complicated dynamics out of a complicated system but the simplicity of game of life made it really impressive. I think in that regard LACE is pretty nice: the rule still feels very simple/natural and you can get much more structured/complex behavior with fewer cells.
2. Nevertheless in the end this blog shows mostly pretty pictures of computational, complex, emergent, chaotic behavior, which we've all seen before. And the key features that make the difference go something I would call physics-like are still missing. And I guess that would be complex stable patterns that can have complex stable interactions. Who knows maybe there are 10^16-celled patterns that have this but we don't know.
3. If I were you I would cut the whole preamble. It will make people take you less seriously than they should. You don't want to look like a crank.
+1 to this copy being a little bit over-the-top. This is neat, but, as you pointed out at the end of the day this is still computationally equivalent to normal 2d cellular automata. I suspect (not taking the time to prove this) that it's equal in a fairly obvious way, which is that you could just replace "links" with 8*<num link states> additional sub-states per cell. The only real difference is just in how it's visualized.
So, neat, but not exactly mindblowing.
In theory, if using a computationally universal CA, you can simulate any other CA with it. However it might require a lot of sub-steps to do so.
No claim is being made that this is a new kind of computation.
The observation that other CA can be equivalent is a weak critique at best, this CA may be a nice compact way of describing types of CA that have interesting properties. It is not terribly interesting that it may be subsumed by some other CA. It may be some interesting unstudied subset.
For instance the Game of Life is a subset of 2-d binary state CA, the rule only takes the totals of neighboring cells, and so is a subset of those CAs with rules that care about specific patterns of neighbors.
The question is really whether this class of rule is a subset of 2D binary state CA, or whether it is a superset in fact.
Good feedback -
These rules use very different principles than traditional cell-based rules - for example neighbor degree, number of connections, and eligibility criteria based on connectivity. So the cells are not becoming alive or dead based on the states of their neighbors, but rather on the topology of their neighborhoods.
The details are beyond the scope of a short write up, but are easy to explore in the rule-editor in the GUI of the code.
And preamble pruned of the historical anecdote behind this.
An interesting approach to characterize graph topology, both locally and globally is to use a graphlet transform, there some interesting research happening around these types of topology signals, here's one that takes a very algebraic approach