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Chapter 6 > Section 7 > Page 277 Previous page-----Next page
Starting from Randomness | The Notion of Attractors



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any number of white cells. At any point one can follow the arrow to the left to get a black cell, but the form of the network implies that this black cell must always be followed by at least one white cell.

The pictures on the next page show more examples of class 1 and 2 cellular automata. Unlike in the picture above, these rules do not reach their final states after one step, but instead just progressively evolve towards these states. And in the course of this evolution, the set of sequences that can occur becomes progressively smaller.

In rule 128, for example, the fact that regions of black shrink by one cell on each side at each step means that any region of black that exists after t steps must have at least t white cells on either side of it.

The networks shown on the next page capture all effects like this. And to do this we see that on successive steps they become somewhat more complicated. But at least for these class 1 and 2 examples, the progression of networks always continues to have a fairly simple form.



Captions on this page:

Networks representing possible sequences of black and white cells that can occur at successive steps in the evolution of the two cellular automata shown on the left. In each case the possible sequences correspond to possible paths through the network. Both rules start on step 1 from random initial conditions in which all sequences of black and white cells are allowed. On subsequent steps, rule 255 allows only sequences containing just black cells, while rule 4 allows sequences that contain both black and white cells, but requires that every black cell be surrounded by white cells.





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Notes related to this page:

* Implementation [of cellular automaton state networks]
* Finite automata
* Regular languages
* Regular expressions
* Generating functions [for regular languages]
* History [of finite automata]
* Excluded blocks [in cellular automaton evolution]
* Entropies and dimensions [in cellular automata]
* Cycles and zeta functions
* 2D generalizations [of entropies]
* Probability-based entropies
* Entropy estimates
* Nested structure of attractors
* Surjectivity and injectivity [of cellular automaton maps]
* Temporal sequences [in cellular automata]
* Spacetime patches [in cellular automata]
* History [of dynamical systems approaches]
* [State networks for] systems of limited size
* Symmetries [and state networks]
* Mathematical interpretation of cellular automata
* Emergence of reversibility
* Markov processes
* Entropy estimates [for sequences]
* All notes for this section
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From Stephen Wolfram: A New Kind of Science [citation] Previous page-----Next page





 
 
 
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