Is anyone studying the active part of soups?
Posted: March 9th, 2019, 2:25 pm
I can't find an existing topic, at least scanning over anything recent. One thing that has piqued my curiosity off and on is what we can say about the part of the Life pattern that is acting like a "Wolfram class 3" CA locally before it settles down into oscillators and spaceships. I don't think we can say very much, but there might be some interesting statistical analyses looking at cell state as a time signal.
I once added a custom hack (since lost) to either Xlife or something I had written that could store and match "time signatures", which were 32-bit sequences of consecutive live/dead values of a given cell position. It is a quick and dirty way to find things like gliders, pulsars, stabilized shuttles, etc. False positives can happen, but generally with a specific enough sequence, you get what you're looking for. The nice thing about this is you don't have to worry about all the symmetries. A passing glider is going to assign cells a certain way (after the leading 1) no matter what direction it's coming from.
With longer sequences, you might be able to make a qualitative judgments. Clearly, if something is settled and periodic, it is recognizable, likewise if it is empty space with an occasional passing glider. Intuitively, some regions look hot or cold depending on activity level (and that can be captured in graphics). Actually, though, a p46 shuttle is much "colder" than it looks in the sense that all of the activity is repeated. The active part of a methuselah will produce many more distinct time signals over thousands of steps.
I see a lot of interest in soup experiments, mostly (I think), in the interest of classifying identifiable patterns. My question is whether anyone is trying to analyze something like the "thermodynamics" of the hot part of the soup. It's unclear where this will go except for a fairly arbitrary classification of regions of a pattern, but it is more a question of how to nail down an intuitive notion of "active part." I am also a little curious what these signals look like. I imagine they could be modeled (approximately) as a markov process with just a small amount of previous history.
ADDED: one thing we will find besides the statistics is recurring "motifs", with b-heptomino and pi-heptomino predominating I think. So another question is with all the interest in tracking the stable components, is anyone tracking the frequency of these short-lived but often useful sequences?
I once added a custom hack (since lost) to either Xlife or something I had written that could store and match "time signatures", which were 32-bit sequences of consecutive live/dead values of a given cell position. It is a quick and dirty way to find things like gliders, pulsars, stabilized shuttles, etc. False positives can happen, but generally with a specific enough sequence, you get what you're looking for. The nice thing about this is you don't have to worry about all the symmetries. A passing glider is going to assign cells a certain way (after the leading 1) no matter what direction it's coming from.
With longer sequences, you might be able to make a qualitative judgments. Clearly, if something is settled and periodic, it is recognizable, likewise if it is empty space with an occasional passing glider. Intuitively, some regions look hot or cold depending on activity level (and that can be captured in graphics). Actually, though, a p46 shuttle is much "colder" than it looks in the sense that all of the activity is repeated. The active part of a methuselah will produce many more distinct time signals over thousands of steps.
I see a lot of interest in soup experiments, mostly (I think), in the interest of classifying identifiable patterns. My question is whether anyone is trying to analyze something like the "thermodynamics" of the hot part of the soup. It's unclear where this will go except for a fairly arbitrary classification of regions of a pattern, but it is more a question of how to nail down an intuitive notion of "active part." I am also a little curious what these signals look like. I imagine they could be modeled (approximately) as a markov process with just a small amount of previous history.
ADDED: one thing we will find besides the statistics is recurring "motifs", with b-heptomino and pi-heptomino predominating I think. So another question is with all the interest in tracking the stable components, is anyone tracking the frequency of these short-lived but often useful sequences?