Note that each of them in effect yields a single sequence that gets progressively longer at each step; other rules make the colors of elements alternate on successive steps. … The first 2 m elements in the sequence can be obtained from (see page 1081 ) (CoefficientList[Product[1 - z 2 s , {s, 0, m - 1}], z] + 1)/2 The first n elements can also be obtained from (see page 1092 ) Mod[CoefficientList[Series[(1 + Sqrt[(1 - 3x)/(1 + x)])/ (2(1 + x)), {x, 0, n - 1}], x], 2] The sequence occurs many times in this book; it can for example be derived from a column of values in the rule 150 cellular automaton pattern discussed on page 885 .

But despite hopes on the part of René Descartes , Galileo and others that biological processes might follow the same kind of rigid clockwork rules that were beginning to emerge in physics, no general principles were forthcoming. … In the late 1970s, however, fractals and L systems (see below ) began to provide examples where simple rules could be seen to yield biological-like branching behavior.

During the evolution the rule can apply only to the inner part FixedPoint[Replace[#, ℯ [x_]  x] &, expr] of an expression.

Note that if one uses base 6 rather than base 2, then as shown on page 614 powers of 3 still yield a complicated pattern, but all operations are strictly local, and the system corresponds to a cellular automaton with 6 possible colors for each cell and rule {a_, b_, c_}  3 Mod[b, 2] + Floor[c/2] (see page 1093 ).

Register machines [from cellular automata] Given the program for a register machine in the form used on page 896 , the rules for a cellular automaton that emulates it can be obtained from g[i[1], p_, m_] := {{_, p, _}  p + 1, {_, 0, p}  m + 2, {_, _, p}  m + 3} g[i[2], p_, m_] := {{_, p, _}  p + 1, {p, 0, _}  m + 5, {p, _, _}  m + 6} g[d[1, q_], p_, m_] := {{m + 2 | m + 3, p, _}  q, {m + 1, p, _}  p, {0, p, _}  p + 1, {_, m + 2 | m + 3, p}  m + 1} g[d[2, q_], p_, m_] := {{_, p, m + 5 | m + 6}  q, {_, p, m + 4}  p, {_, p, 0}  p + 1, {p, m + 5 | m + 6, _}  m + 4} RMToCA[prog_] := With[{m = Length[prog]}, Flatten[ {MapIndexed[g[#1, First[#2], m] &, prog], {{0, 0 | m + 1, m + 3}  m + 2, {0, m + 1, _}  0, {0, 0, m + 1}  0, {_, _, x : (m + 1 | m + 3)}  x, {_, m + 1 | m + 3, _}  m + 2, {m + 6, 0 | m + 4, 0}  m + 5, {_, m + 4, 0}  0, {m + 4, 0, 0}  0, {x : (m + 4 | m + 6), _, _}  x, {_, m + 4 | m + 6, _}  m + 5, {_, x_ , _}  x}}]] If m is the length of the register machine program, then the resulting cellular automaton has m + 7 possible colors for each cell.

This emphasis on theorems has also led to a focus on equations that statically state facts rather than on rules that define actions, as in most of the systems in this book.

Human languages always seem to have fairly definite rules for what is grammatically correct. And in a first approximation these rules can usually be thought of as specifying that every sentence must be constructed from various independent nested phrases, much as in a context-free grammar (see above ).

The number of steps before a machine with given rule halts can be computed from (see page 888 ) Module[{s = 1, a, i = 1, d}, a[_] = 0; MapIndexed[a[#2 〚 1 〛 ] = #1 &, Reverse[IntegerDigits[x, 2]]]; Do[{s, a[i], d} = {s, a[i]} /. rule; i -= d; If[i  0, Return[t]], {t, tmax}]] Of the 4096 Turing machines with s = 2 , k = 2 , 748 never halt, 3348 sometimes halt and 1683 always halt.

The idea of constructing abstract trees such as family trees according to definite rules presumably goes back to antiquity. The tree representation of rule (c) from page 83 was for example probably drawn by Leonardo Fibonacci in 1202.

The weather Almost all the intricate variations of atmospheric temperature, pressure, velocity and humidity that define the weather we see are in the end determined by fairly simple underlying rules for fluid behavior.