Phase transition
Named by 10 essays across 2 fields — each of them below, with the objects they name alongside it.
The moment everything joins up
Add edges to a set of points one chance at a time and the graph goes from dust to a single piece — not gradually, but over a window that narrows as the point count grows. The last obstacle is almost always a single point with no edge at all, and that is what fixes where the change happens.
The moment a giant appears
Raise the chance of an edge slowly and a random graph does nothing for a long time, then in a narrow window acquires a component holding a definite fraction of everything. The fraction is the root of an equation, and the equation says why the transition is where it is.
Two thresholds, not one
A random graph acquires a piece holding most of its points at average degree one, and is still not connected. Connectivity waits until the average degree reaches the logarithm of the size, and what holds it up is the very last isolated point.
The window where the giant is born
Below the threshold the largest piece is a few dozen points, above it a definite fraction of everything. At the threshold it is neither, and the size it does take — the two-thirds power — is an exponent with no elementary derivation that a measurement finds immediately.
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.
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.
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.
A crowd of clocks that falls into step
Give a thousand oscillators a thousand different natural rhythms and let each be pulled towards the average phase. Below a definite pull nothing happens; above it a cluster forms and grows, and Kuramoto's 1975 calculation says exactly where: at twice the spread of the frequencies, with the order growing as the square root of one minus the threshold over the pull. A simulation lands on that curve to the third decimal.
A long enough chain never locks
Line oscillators with different natural rhythms up in a row and let each feel only its two neighbours. The chain locks into one frequency exactly when the coupling exceeds the largest running total of their frequency differences — a random walk tied down at both ends — so the coupling needed grows like √N, and its law is Kolmogorov's, the same distribution that tests whether a sample fits a curve.
A threshold nobody can place
Random three-literal clauses on n variables: below about 4.27 clauses a variable a formula can almost always be satisfied, above it almost never. The change is proved to be sharp and nobody has proved where it is. Counting solutions overshoots to 5.19, counting pairs of them fails outright, and every algorithm with a proof stops well short.
Named alongside it
The objects these essays reach for when they reach for this one.
ThresholdRandom graphComponentExpectationLocalityRandom walkCellular automatonCentral limit theoremConnectivityFirst momentInvariantIsolated vertex