The Iron Rule and the Mystery of B345/S456

There is a very simple “Iron Rule” of cellular automaton behavior: more local growth = more global growth. Generally, adding additional birth or survival conditions increases the “aggressiveness” of the automaton. This means most or all of at least the following parameters:

  • increases the growth rate of exploding CA
    (decreases the decay rate of stabilizing CA);
  • increases seed lifetime or likelihood of seed explosion;
  • increases the equilibrium density (whether ash, glass or expanding chaos).

Going from any given random rule like B356/S247 to a neighboring one like B356/S2467, it is thus immediately clear what kind of a change is expected. In this case it turns out the addition of S6 is sufficient for turning a relatively fast-stabilizing “class 2” rule into a relatively slowly-exploding “class 3” one. And the same relationship holds further also with finer, non-totalistic detail in transition rules. Using Golly’s notation, if we subtract the S6k condition, the rule remains explosive but grows now at only maybe 40% the same rate. If we subtract also S6a, we will be right back to decaying rather than growing; if we then add also B4e, the decay rate will be slower; etc.

A lot of the typology of cellular automata could be outlined already by surveying the relative strengths of each individual transition condition. E.g. as is known to most aficionados of life-like CA, the condition B2 is alone sufficient to ensure “class 3” overall exploding behavior; or, as is known to people studying also the finer non-totalistic detail, this fact results purely from the life-speed “domino engine” produced by the B2a transition; and upon its subtraction, plenty of B2-a rules will prove to be stabilizing instead. (My username here on WordPress comes from making this observation in general form back in 2012, though it had been nascently there already in the context of the Just Friends rule explored since 2000.)

The Iron Rule is however not actually watertight. E.g. experiments starting from Conway’s Life, right on the boundary of chaos, will indeed show rapid explosion for most “just upward” rules like B34/S23 or B3/S236, or slightly slower explosion for e.g. B3/S237. And the three “just downward” rules — B/S23, B3/S2, B3/S3 — are all in turn strongly stabilizing. For some reason or the other, however, the rule B35/S23 (it seems to have been named “Grounded Life” since I last checked) quite prominently backtracks on a few metrics. Sure enough, the rule still creates denser puffs of chaos, but at the same time they stabilize faster, and to sparser ash, than does regular Life. Some of this can be, it seems, attributed to a balance between stable vs. oscillating ash, as B5 suffices to destabilize most still lifes from Life; and all of this also seems to be linked to the kind of unusual “many-cloud” texture of Life and many of its immediate neighbors, which may be itself a topic worth exploring on this blog eventually.


I find the most spectacular failure of the Iron Rule to be however the rule B345/S456 — no common name that I know of, but the largely similar B345/S4567 has been called “Assimilation“. (I could call them respectively perhaps “Low Assimilation” and “High Assimilation“.) Three of the six only weakly downward neighbor rules: B34/S456, B345/S45, B345/S46 (all likewise with the addition of S7) already form explosive messes, the first one outputting stable glass (density about 69%), the latter two active chaos (densities around 52%–51%). This rule however stabilizes fast into diamond patterns with a mostly stable glassy interior (density slightly below 72%), surrounded by a mostly one-cell-thin period-2 boundary. Furthermore, despite extruding orthogonal growth with ease, the rule seems to be nearly incapable of diagonal growth! Most starting soups, even quite large, will not grow more than a few cells beyond their initial bounding diamond before stabilizing.

An about 250 by 250 stabilized diamond in B345/S456

The appearence of a stable interior is not a mystery; after all it’s already there in B34/S456, and the weaker S45 or S46 are simply not sufficient to support any stable glass texture (even relatively few agars that would be stable under B45). By the addition of birth rules that disrupt this texture (B6, B7, B8) we would also obtain chaotic interiors again, which suffice to also generate explosive growth again. Likewise, the bimodal texture as such is also not a problem. All glass-producing rules must follow something of the sort, with boundary formation distinct from the interior. In the absense of S0–3, it also easily follows that the boundary region must be chaotic or oscillating rather than stable. It is also not too difficult to prove that the typical makeup of the resulting P2 boundary is, indeed, stable.

The mystery is, rather, why does this boundary tend towards a thickness of 1 along all edges, especially diagonal? (Pattern corners may show localized larger patches, often with higher periodicity — my example pattern here in fact has a p16 upper tip with periodic bounding box decay.) There is no general rule about this for B345 rules lacking low survival conditions. This is well demonstrated in one form already by the growing chaos of B34/S456; in another by the twice downwards neighbor B345/5 (a.k.a. LongLife), which forms large oscillating phoenix regions with ease (many, but by no means all, of them with similar p2 boundary formation); and in a third form, by the fact that soups can still show just a cell or two of initial diagonal growth. Indeed, upward neighboring chaotic rules, say B3458/S456, still maintain the diamond shape, and grow mainly due to collisions of diamond-internal patches of chaos with the boundary, which will trigger occasional diagonal growth events (which then propagate at lightspeed down the diamond edges).

An attempt to sketch an explanation could perhaps begin from considering the Assimilation rules as supporting two distinct live phases — the stable glass phase and a less dense dynamic boundary phase. We can then argue that the former arises easily within the latter, and also grows rapidly within it, hence generally all the way out to the boundary of the pattern in general. But why should this process lead to the boundary stabilizing at p2? Why does the presence of the glass phase not perturb the dynamic phase to show additional diagonal growth? It seems this should be possible: this is shown e.g. by the just slightly downward non-totalistic neighbor rule B345/S4-q56, which produces not just similar glassy interior but also many identical thin p2 boundaries. And yet, enough pockets of boundary chaos remain in it, such that larger seeds will show slow explosive diagonal growth as well. Worse yet, how would the specifically diamondoid growth habit drop out of any consideration of this sort, which seems to predict nothing about a particular shape?

Should we perhaps try a deeper dive to diagnose not two, but three phases — both a semi-chaotic phoenix phase and a denser true chaotic phase? This is testable; batches of the phoenix phase can be generated easily by running soups in LongLife (or also e.g. B345/S56), and switching them over to Low Assimilation after they’ve stabilized “in texture”. Glass phase will then arise rapidly at boundaries of differently-phased phoenix phases (if you’ll excuse slightly overloading the term “phase”) or, in their absense, from hand-placed seeds. Observing this process, it is not clear to me at all however if there is much of a “true chaos phase” active at their boundary. If there is one, it does not seem to spread into the phoenix phase much faster at all than the glass phase does. Or does the glass phase actually expand, or is it simply repeatedly re-seeded by the chaos phase itself? Perhaps this angle of approach will require first of all some ability to actually delineate phases better than can be done just by visual inspection … already itself an interesting question, perhaps tractable e.g. by custom rules with sub-colorization, but probably requiring further tools and theoretical development.

Fates of Finite Chaos

It’s been a slow start on this blog. There was at a point a plan I had for a series of articles, but perhaps it will be more productive if I simply start writing about topics in no especial order.

So, an observation on “chaotic” cellular automata. These are some of the less understood CA I think, for the simple fact that there’s no fixation point, no attractor pattern types that we can see emerging (and then catalogue and perhaps manipulate). The basic property that gives them this power is however the fact that patterns keep growing — faster than any pseudorandom churn within their active region manages to end up at any kind of stable points. But this does not mean that the rule is incapable of containing any stable points. Quite a few chaotic rules after all allow for some still lifes, oscillators or spaceships. Those that don’t, such as B1 rules, will be still likely to at least allow for agars, stable or oscillating.

Agars in mind, we can in particular consider, instead of patterns’ behavior in infinite space, also patterns’ behavior in finite space. This will necessarily tame any pattern into at least an oscillator. In principle, such an oscillator could be a chaotic loop around an astronominally large phase space (even something quite small like a 10×10 box will allow circa 2^100 different patterns). In practice, this does not seem to be what happens, though.

Consider the following 12×12 seed and its fate in some well-known chaotic rules:

x = 12, y = 12, rule = B2/S:T12,12
2obob3o2b2o$3bob2obo2bo$2bobo2b2o2bo$3bob
ob2o2bo$2o6b4o$2obobobo$obobob2obobo$bo2b
ob3o$b4o2b2o$3o2bo4b2o$bo3b3o2b2o$b4ob5o!

Seeds (B2/S): Stabilizes into 3 duoplet oscillators at gen 458,748.

x = 12, y = 12, rule = B2/S0:T12,12
2obob3o2b2o$3bob2obo2bo$2bobo2b2o2bo$3bob
ob2o2bo$2o6b4o$2obobobo$obobob2obobo$bo2b
ob3o$b4o2b2o$3o2bo4b2o$bo3b3o2b2o$b4ob5o!

Live Free Or Die (B2/S0): Completely dies off by gen 4. (Possibly a fluke — gen 1 already leads to the rule evolving into just a population of 6, in 2 clusters which both happen to be plus spark precedessors.)

x = 12, y = 12, rule = B234/S:T12,12
2obob3o2b2o$3bob2obo2bo$2bobo2b2o2bo$3bob
ob2o2bo$2o6b4o$2obobobo$obobob2obobo$bo2b
ob3o$b4o2b2o$3o2bo4b2o$bo3b3o2b2o$b4ob5o!

Serviettes (B234/S): Population hits a minimum of 5 at gen 54,501, evolution follows S2 symmetry from there on. Stabilizes as a mostly line-based p2 agar relatively soon afterwards, at gen 55,801.

x = 12, y = 12, rule = B3/S023:T12,12
2obob3o2b2o$3bob2obo2bo$2bobo2b2o2bo$3bob
ob2o2bo$2o6b4o$2obobobo$obobob2obobo$bo2b
ob3o$b4o2b2o$3o2bo4b2o$bo3b3o2b2o$b4ob5o!

DotLife (B3/S023): Stabilizes as two dots, dot on preblock, tub, and beehive at gen 313 (final pop. 16).

x = 12, y = 12, rule = B37/S23:T12,12
2obob3o2b2o$3bob2obo2bo$2bobo2b2o2bo$3bob
ob2o2bo$2o6b4o$2obobobo$obobob2obobo$bo2b
ob3o$b4o2b2o$3o2bo4b2o$bo3b3o2b2o$b4ob5o!

DryLife (B37/S23): Stabilizes as four blinkers at gen 144 (final pop. 12).

x = 12, y = 12, rule = B3/S45678:T12,12
2obob3o2b2o$3bob2obo2bo$2bobo2b2o2bo$3bob
ob2o2bo$2o6b4o$2obobobo$obobob2obobo$bo2b
ob3o$b4o2b2o$3o2bo4b2o$bo3b3o2b2o$b4ob5o!

Coral (B3/S45678): Completely dies off by gen 10.

x = 12, y = 12, rule = B34/S34:T12,12
2obob3o2b2o$3bob2obo2bo$2bobo2b2o2bo$3bob
ob2o2bo$2o6b4o$2obobobo$obobob2obobo$bo2b
ob3o$b4o2b2o$3o2bo4b2o$bo3b3o2b2o$b4ob5o!

34 Life (B34/S34): Pattern loses toroidal connectivity at gen 7,346 and evolves from there as a single cluster, which rapidly stabilizes as one p2 oscillator and one p4 oscillator by gen 7,363.

We see here some suggestions of trends already — highly aggressive B2 growth takes a lot longer to stabilize than any of our B3-based examples; B34 growth also takes longer to stabilize than growth based on B3.

(I do not count B7 as a “growth” condition since it is incapable of extending a pattern outside its initial bounds. B4, though insufficient for growth alone, by contrast does achieve this in tertiary / inter-intercardinal directions, as best demonstrated by the octagonal growth envelope of rules like B34/S3456, where B3 only creates new cells in the eight primary and secondary directions, and most of the pattern’s growth is driven by B4.)

Let’s consider increasing the playfield size. Here are the four last rules again, from the same seed and on an arena of 24×24 (for brevity I will only give the RLE once):

x = 24, y = 24, rule = B3/S023:T24,24
bob2o3bobob2o2bobo2b2o$b2ob3ob2o2bo3b2obob3o$3b3o2b4ob3o4b2o
$5obo3b3ob2ob2o4bo$3bo2b3obob2o2b2o2b4o$3obo3bob4o4b3o2bo$2o
bo2b3o2bo2b2ob2o2b2o$o3b2o3bo3bob2o3b3o$2o7bo2b3o2b2o3b2o$b3
ob2obobob2ob2o4b2o$2obob2o2bo2bo2b2obo3b2o$bobobo5bob2o2b2o2
b3o$o2b2ob3ob2o4bo2bo3bo$o2bobo3b2o2b3obo3b3o$2ob2ob3obo3bob
o6bo$3bobo3bob6ob2obo$4b2obob2o2bobobobo$b5ob5o2b2o3bob3o$2b
o3b2ob3obo4b2ob2o$3b3o2bo2bobob3obob2o$o2b5o4bobobobo4bo$o4b
2ob3obo2bo3bobo$4bo2bob3ob3ob4obo$b4obo2bob2obobo3b2o!

DotLife: Stabilizes at gen 789 (final pop. 35).
DryLife: Stabilizes at gen 665 (final pop. 23).
Coral: Stabilizes as a dense agar at gen 120 (final pop. 473).
34 Life: No stabilization after 8,000,000 generations.

Note the vast differences in what happens! The two Life variants take just 2–3× longer to stabilize and yield ash of about the same density. Coral stabilizes again fast, but now reaches the high-population attractor phase. 34 Life might be again able to stabilize eventually but it’s, off the cuff, anyone’s guess if it would do so in a couple million or couple billion generations more.

For some degree of a handle on the latter, I checked one 15×15 seed and found that it stabilizes into a single p2 at gen 3,090,990; again after loosing toroidal connectivity to overcrowding shortly before, at gen …944. About 500× longer for a playfield 3 cells wider in each direction, so very conservatively, perhaps we might be able to expect a 24×24 seed to stabilize by a couple hundred trillion gens at least? (Or maybe not.)

This result is also not at all related to growth rates. DotLife chaos eventually grows in cardinal directions at a rate of c/2 and diagonally at c/4, thanks to stable B-heptomino puffers, rather faster than 34 Life does. The difference we are seeing is in the internal structure of chaos. Zooming into DotLife or DryLife chaos, indeed, shows small voids, oscillators, still lifes persisting for many generations before some cloud of activity consumes them again and maybe soon recreates a different arrangement of ash.

In any case, this suggests some metrics that might be usable for characterizing chaotic rules:

  • Typical finite seed lifetime as a function of seed size.
  • Typical ash density.
  • Typical stabilization mechanisms.

The first two are of course also applicable to non-chaotic rules; this time just also when let play out on an infinite empty field. Yet other metrics would be also possible, e.g. on a closer look into a rule’s dynamics rather than its ultimate evolution.

There is, lastly, I think, an interesting question this raises on the ultimate evolution of chaotic rules in open space. Is it possible that a rule that looks like it is producing infinite growth, but appears to not be amenable to being proven to create infinite growth… actually isn’t? Perhaps, after some truly Googological amount of evolution, a growing splotch of chaos in e.g. 34 Life still breaks apart into a group of spaceships and oscillators. (Perhaps a puffer or gun in there too among bazillions of decay results.) Sounds very improbable, but given infinite time, any possible improbability would be expected to still happen. And these finite experiments do seem to show that in many rules, “chaos” does not actually have infinite lifespan, typically eventually reducing to small objects, rather than loops-within-itself. It does feel there are heuristic reasons to predict this not to happen; but they are, indeed, heuristics, grown in a small human brain not practically accustomed to dealing with infinities.

I’m also inclined to think back to Conway’s original Game of Life group a bit here. If you have been tracking the evolution of the R-pentomino by hand for ca. 800 generations, the longest-living pattern you’ve found yet, would you have any reasons to think that the pattern will stabilize in just a few hundred more? As it turns out, that it does, though very little even hints at this fate until ca. generation 1000, when two major areas of activity suddedly die off, leaving a pattern with more ash than activity. Even then it takes an additional 100 generations before the third remaining area also narrows down towards stabilization. This was always a mathematical fact, even before anyone calculated it, not an experiment that could have gone otherwise. But there would be a universe where the late Conway begun to investigate some slightly different iterative system, and where some analogue of the R-pentomino, turns out to live not for a thousand generations, but much longer, maybe hundreds of thousands of gens; in any case some count not really calculable by either reasonable human effort or by the computers of the day for quite some while. From that PoV the automaton in question might look as immortal as some other rules look right now. Is there any way to tell the difference in advance, before we have the computing power to follow a sufficiently chaotic pattern for one googol or one googolplex generations? Sometimes clever proofs might be available to show that growth must be infinite for non-straightforward reasons, but at other times, perhaps not.

Type 1: Immediate, Eventual or Statistical Death?

The most challenging topic in CA classification is probably developing clear definitions of “chaotic” and “complex” behavior — the two are very intertwined if looked at closely. However, the other two classes of the 1983 Wolfram classification, the “dying” and “shrinking” behaviors, have ambiguities to them as well. Some discussion of these probably makes for a good smaller starting case study. — As a general note before continuing further, I will be generally discussing Life-like CA on this blog. This well-studied space of 218 CA still has some some limits on its details of behavior, but seems to readily provide examples of all relevant large-scale phenomena.

There are, of course, cellular automata that can be proven to evolve towards vacuum. As a trivial case, any rule with no birth and no survival (“B/S” in the usual notation), on any cell grid and any neighborhood, accomplishes this in one timestep. Rules with only very weak birth conditions exist also, which may drag this out to some finite number of additional timesteps but no more. Consider B8/S: all live cells immediately die off in this rule, and new cells will be only born in dead locations that were surrounded by eight live cells. In the next generation, all newborn cells will be thus surrounded by eight dead cells. They will of course themselves die off, but moreover, since none of these live cells can be adjacent to one another, no new cells will be born in the next generation. Therefore all patterns evolve to vacuum by the 3rd generation. ∎

B7/S and B78/S share the same property, though the proof is very slightly more complicated. The second generation can now contain also pairs of newborn cells. Triplets are, however, impossible. This suffices to again prove that no neighborhood sufficient for the birth of a yet new cell can arise in the 3rd generation, which is therefore again guaranteed to consist of vacuum.


The simplest rule with some arguable ambiguity in its behavior is B/S8, the rule with no birth and no survival, except of cells that are completely surrounded by other live cells. This rule allows exactly one pattern that does not evolve towards vacuum: the fully filled-in plane of live cells (I call this the antivacuum), which can be considered a stable agar. The presence of even a single dead cell yields a bubble of vacuum, growing at the rate of one cell per generation — the “speed of light”, as it is known in the field.

The behavior of any finite patterns in this rule is still simple to track or predict. “Landlocked” cells, those not exposed to vacuum, will survive, while all cells on the exterior will die. A cell therefore needs to be nth-degree landlocked to survive for n timesteps before dying. When using the Moore neighborhood, the degree of landlockedness of any given cell is easy to determine: if the largest square centered on the cell that only contains live cells has an edge length of 2n+1, then the cell has a landlockedness degree of n. A pattern containing a filled square 7 cells wide, for example, will have the central cell of this square survive at least three timesteps = to the 4th generation (though usually we start counting generations from zero and hence call the generation after three timesteps generation #3.)

Infinite patterns are however less obvious to characterize (is anything ever simpler with infinity?). Consider a pattern comprising a live halfplane and a dead halfplane, with a dividing line at any angle, though for simplicity we may consider an orthogonal line as the most basic example. The dead halfplane will advance by one cell every generation, clearly enough, and any given live cell will eventually die. On the other hand, our half-plane of live cells is still infinitely deep, and as such it contains cells that will remain alive for arbitrarily long. There is no generation at which the pattern will have evolved into pure vacuum.

For that matter, this pattern seems to fit a part of the definition of a spaceship or a wave! Individual cells may keep dying, but considered overall, the pattern is no smaller for this (it’s infinite after all) and rather, it appears to move away from the vacuum at the speed of light. If the dividing line is simple enough, orthogonal or at various (all?) rational slopes, the pattern also maintains its shape in each generation, and our “infinite spaceship” has a well-defined period of 1. We could conceive a research programme on what other periods of “halfplane spaceships” exist. We don’t even need an entire half a plane; e.g. any sector bounded by two lines will do just as well. Such patterns will now be definitely infinite spaceships, not waves: they have not just a velocity but also a well-defined direction, since the pattern has no translation symmetry. It seems that we could construct a spaceship with a wide range of rational slopes in this fashion. Two simple examples are the orthogonal quarterplane, with a slope (1,1), and the diagonal quarterplane, with a slope (1,0). I suspect that any rational slope is in fact possible. For that matter, sufficiently narrow sectors can prove to have superluminary speeds — the diagonal quarterplane already has speed 2c. A fairly interesting result for a mere “dying” CA (and we have not even begun to investigate what exact slope–speed–period combinations are possible), showing that even these rules can have behavior that is hardly trivial.

Further research potential yet can be identified too. We could consider patterns with more tattered decaying edges, perhaps giving rise to additional periodicity; or patterns with some generations of non-periodic evolution before an edge has been smoothed close enough to a line or some other stable shape (e.g. a parabola or hyperbola ought to work). And even, we can wonder if some kind of a 1D cellular automaton could be emulated with a suitable decaying edge. In some cases even universal computation could prove to exist within a strictly “dying” pattern, if we allow for patterns with infinitely many live cells.


But really I digress. The Wolfram classification is, and any potential successor should be, of course more concerned with the typical behavior of a CA than with exceptional engineered patterns. Infinitely many “plane sector spaceships” might exist in B/S8, but they are a marginal case and they require the existence of filled-in areas of any arbitrarily large size. Not only is this not possible for finite patterns, this is not expected to occur either for any “random” starting pattern of a density less than 100%.

Still we can ask: is B/S8 a “dying” or a “shrinking” rule? These days the community standard software for operating Life-like CA is Golly, which if asked to “randomly fill” an area, defaults to a density of 50%. Running one of these at B/S8 indeed rapidly develops to a population of zero. Testing out twenty such soups of a size of about 2300 by 1300 cells, with a starting population of about 1.5 million, only one of them left any alive cells by generation 2; clearly “dying” behavior. But suppose we started instead with a much higher density, e.g. around 99.999%? Now we would expect to find only about 30 dead cells in our field of about 3 million cells, enough to leave quite large areas of antivacuum, and it would probably take several hundreds of generations to see such a seed shrink to oblivion entirely. Such long lifespans could be argued to be instead a sign of a “shrinking rule”.

The lesson is clear, I think. The way in which “random” patterns develop depends on their starting density. “Shrinking” and “dying” are strictly speaking different pattern behaviors, not necessarily characteristics of every or even “every random” pattern in the rule. Rules that are instable from all starting densities, the likes of B8/S, are very few indeed. For most we could at most define a range of densities that they will comfortably support. Such ranges can have both an upper and a lower limit, too. Whenever a rule does not feature survival-at-8-neighbors, sufficiently high densities will be increasingly more likely to die out from overpopulation, no matter how the rule might behave at lower densities. This could of course still leave behind a number of “seeds” next to dead cells, that perhaps could then proceed to sprout into areas of more “rule-typical” density.

Worth noting though is that there is still a reason to privilege a density of 50%. If we wish to rigorously define “a random pattern”, we could ask e.g. for the modal behavior among all patterns of a given size. The average density of all of these is indeed 50%. Secondly, it’s even the case that this will show the most diversity in local densities. Some other global density like 60% would be more likely to show patches of other still higher densities like 70%; but also much less likely to show lower densities like 10%. However, this is probably not the case if we consider densities on a logarithmic scale… Densities of 99% and 99.99% are much more likely to show different behavior than densities of 49% and 49.99%.

In any case, instead of lumping various different rules all as simply “dying” / “type 1”, we can hence already map even their typical behavior more closely: time to die off as a function of density. The first could be considered to additionally depend on soup size (other things being equal, a larger random soup has better chances to contain slow-dying regions); or possibly should be measured rather as a rate of dying off.

Introduction

This is the first post on Cellular Cosmology, a research diary of sorts on the long-term and macroscopic behavior of cellular automata.

As is often the case in the lands of cellular automata, I am a hobbyist rather than anything resembling a professional (though I do have some past studies in mathematics under my belt). It seems however that, in contrast to the median hobbyist, my interests are not limited to the discovery and/or construction of “interesting patterns” — objects like oscillators, spaceships, conduits, guns, rakes, as documented by now in great detail in places such as LifeWiki — but also on the underlying reasons of why certain cellular automata behave in some way and not in some other way.

Cellular automata as we know them are mathematical models, and their software implementations, developed by humans. As such we know their fundamental rules in full detail (and we can typically predict exactly the behavior of any given pattern in any given rule). Taking the primus inter pares of CA for example, that is Conway’s Game of Life, cells are born iff they have 3 living parents and survive iff they have 2 or 3 living parents — “B2/S23” for short in CA parlance. But it remains challenging to determine why, exactly, this specific rule leads to complex behavior that has captivated audiences for over fifty years by now. At the same time, already some very similar rules, of totalistic CA with the Moore neighborhood, lead to either much sparser results (say, B35/S23) or explosive chaos (say, B3/S013), of which neither lends itself to “engineering” work quite as easily. We are thus concerning ourselves here essentially with the chaos-theoretic problem of “emergent complexity”: what is it, how it happens and why?

An initial goal I will be occupied with will be the construction of a new typology of CA that aims to be be more fine-grained than the four-fold categorization proposed by Wolfram (“dying”, “stabilizing”, “chaotic” and “complex”).

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