A stochastic process where a system's state evolves through random steps, often used to model diffusion.A random walk is a mathematical model of a path that consists of a sequence of random steps. It is the simplest and most universal description of diffusion: a particle (or a molecule, an animal, or even a stock price) that moves in such a way that each step is independent of the previous ones. The random walk explains why a drop of ink spreads in water, how heat conducts through a solid, and how the path of a drunkard wandering from a lamppost ends up, on average, at a distance proportional to the square root of the number of steps.
The random walk was first formalized by Karl Pearson in 1905, who posed it as a problem in Nature, and independently by Albert Einstein in his 1905 paper on Brownian motion, where he derived the diffusion equation from random molecular collisions. The mathematical theory was later developed by George Pólya and William Feller.
In the simplest case, a walker on a line takes a step left or right with equal probability. After N steps, the expected position is zero, but the expected distance from the start grows as √N. This square-root scaling is the hallmark of diffusion: the mean squared displacement grows linearly with time. In more complex settings, the steps can have different lengths, directions, or probabilities, but the central limit theorem ensures that many random walks converge to the same diffusive behavior. In quantum mechanics, the same idea applies to the evolution of a wavefunction under random perturbations, leading to phenomena like Anderson localization.
Random walks model diffusion in physics and chemistry, stock price fluctuations in finance, animal foraging patterns in biology, and the spread of rumors or diseases in networks.
In a 2D random walk, the walker returns to the origin with probability 1, but in 3D, the probability is only about 0.34 — meaning a drunk ant on a plane will always come home, but a drunk bird might get lost forever.










