Concept

Locality

The restriction that each part of a system is affected only by what is immediately next to it. It is what makes a cellular automaton's global behaviour surprising, since every rule in it sees only its immediate surroundings.

Named by 8 essays across 3 fields — each of them below, with the objects they name alongside it.

Elementary cellular automaton, rule 90. A row of cells evolving downward, each cell decided by the three above it.

Eight rules and a triangle

A row of cells, each one deciding its next state from the three above it. Eight cases, one bit of output each — a rule that fits in a byte, and 256 of them in total. One of those bytes draws Pascal's triangle.

dynamics · Cellular automata
Elementary cellular automaton, rule 110. A row of cells evolving downward, each cell decided by the three above it.

The rule that computes

One of the 256 elementary rules can run any program. Not simulate one, not approximate one — a machine that can compute anything computable, built from a lookup table with eight rows and nothing else.

dynamics · Cellular automata
What a sentence of depth 2 can reach. Two rings of points, of 14 and 19 points, each with a run of 9 consecutive points marked as the neighbourhood a sentence of depth 2 can inspect.

The distance a sentence can see

A first-order sentence with three quantifiers cannot notice anything about a graph beyond a fixed distance from the points it names. That single limitation is why it cannot say connected, and why the failure survives every attempt to add more quantifiers.

logic · Ehrenfeucht–Fraïssé games
One threshold narrowing, one staying wide. Probability curves for connectivity and for containing a triangle, plotted against the edge probability as a multiple of each property's own threshold, at several graph sizes.

Sharp, or merely a threshold

Every monotone property of a random graph has a threshold. Some of them turn on over a range that shrinks relative to the threshold as the graph grows, and some do not — and which kind a property is turns out to be decided by whether it is about a local structure or about the whole graph.

probability · Random graphs
Rule 184 at density 0.30. A space-time diagram of rule 184 on a ring of 120 cells, 80 steps down the page, starting from a random row with 36 cars. The diagonal stripes are free-moving cars; the jams dissolve.

A road where nobody overtakes

Rule 184 moves every 1 one cell to the right whenever the cell ahead is empty. It is one of only five elementary rules that never change the number of 1s, and that single property turns it into a model of traffic with an exact transition: below half density every jam dissolves, above it jams can never all clear and drift backwards against the flow.

dynamics · Cellular automata
A local rule taking a vote. A space-time diagram of the GKL rule on 149 cells from a random row with 69 ones. Black and white regions grow and meet along slanting boundaries, and after 69 steps the whole ring is 0.

No local rule can count the votes

A ring of cells, each holding 0 or 1, has to agree on whichever value is in the majority — every cell seeing only its neighbours. The best-known rule gets it right most of the time and wrong near a tie; no rule of any radius gets it right always. Yet two rules run one after the other do, on every ring, and the first of them is the traffic rule.

dynamics · Cellular automata
Traffic with random dawdling: jams from nowhere at density 0.18. Nagel–Schreckenberg traffic, 29 cars on 160 cells, top speed 5, dawdling probability 0.25; up to 11 cars stopped at once.

A jam that comes from nowhere

Give cars on a ring road a top speed of five cells a step, let each slow to the gap ahead, and add one more rule: now and then, at random, a driver eases off by one. That is the whole of the Nagel–Schreckenberg model, and it produces what the exactly solvable rule 184 could not — jams that form in free traffic with no obstacle, drift backwards against the flow, and cost the road more than a third of its capacity. Set the top speed to one and remove the chance, and it is rule 184 again, cell for cell.

dynamics · Cellular automata
Rule 30 made reversible: forward 60 steps, then back to the start. Second-order rule 30 on 101 cells: 60 steps forward, then the same rule from the swapped last two rows returns the starting row exactly.

A rule that remembers one row back

Only six of the 256 elementary cellular automata can be run backwards, and all six are trivial: shifts, copies and complements. Every interesting rule forgets. But make the new row depend on the row before the current one as well — combine the rule's output with it cell by cell — and every rule, rule 30 included, becomes exactly reversible: run forwards, swap the last two rows, run the same rule again, and the starting row comes back cell for cell. Nothing is lost, and yet the patterns still look as disordered as ever.

dynamics · Cellular automata

Named alongside it

The objects these essays reach for when they reach for this one.

Cellular automatonDeterminismInvariantIterationPhase transitionState spaceComputationExhaustive searchRandom walkBinaryConnectivityCounterexample

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