Will the walker return?
The question
A walker tosses a fair coin every minute: heads, one step east; tails, one step west. Is she certain to come back to where she started? What if she can also step north and south — or up and down in space?
Try the experiment before reading on. There’s no wrong way to explore.
Experiment
Chance lab
Each walker steps left or right at random. The typical distance from the start grows like √n.
- 8 of the walks
- root-mean-square distance (200 walks)
- ±√n (exact)
Root-mean-square distance after 100 steps
Simulated10.94
Exact√100 ≈ 10
Simulation of 200 walks of 100 steps: root-mean-square distance 10.94. The exact value is √n, about 10.
Evidence, not proofThis is a simulation of 200 walks (run 1) using a seeded random number generator. It is evidence, not proof: the exact values come from mathematics, and a new run gives slightly different numbers.
Exactly: the average of (position)² after n steps is n, so the root-mean-square distance is √n. Going twice as far takes four times as many steps.
Hints, if you want them
What’s going on
What’s going on
George Pólya answered this in 1921. On a line (one dimension) or a grid of streets (two dimensions), the walker returns home with probability 1 — in fact infinitely often. In three dimensions she does not: the chance of ever coming home is only about . A remark often attributed to Shizuo Kakutani puts it this way: a lost person will find their way home, but a lost bird may not.
The reason is a sum. A walk returns home infinitely often exactly when the expected number of visits home is infinite, and that expected number is the sum, over all times, of the chance of being home then. After steps the chance of being home behaves roughly like
The first two sums diverge — the second only just, like the harmonic series — so the walker comes home again and again. The third converges, so she may wander off for ever.
There is a surprise even in one dimension: the walker is certain to return, yet the average time she takes to return is infinite. Random walks like these model the diffusion of molecules, stock prices, the spread of heat, and the search patterns of animals.
Note
The simulation shows a finite number of walks for a finite time — evidence. Pólya's theorem is a proof about every walk forever.
Where this leads
- Advanced undergraduate · Lesson availableMarkov chainsDiscrete-time Markov chains: transition matrices, classification of states, stationary distributions, convergence to equilibrium, hitting times and random walks.
- First-year graduate · Lesson availableStochastic processesContinuous-time processes: Poisson processes, continuous-time Markov chains, Brownian motion and its path properties, continuous-time martingales, and an introduction to Itô calculus and stochastic differential equations.
- Research pathways · Lesson availableRandom structures: walks, percolation and random geometryOptional research direction: randomness creating shape. Random walks on graphs and Pólya's recurrence theorem, electrical networks, percolation and phase transitions, random graphs, Brownian motion and its fractal paths, and a glimpse of Schramm–Loewner evolution and random planar geometry.