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Information percolation is a new method for analyzing stochastic spin systemsthrough classifying and controlling the clusters of information-flow in thespace-time slab. It yielded sharp mixing estimates (cutoff with an$O(1)$-window) for the Ising model on $Z^d$ up to the critical temperature, aswell as results on the effect of initial conditions on mixing. In thisexpository note we demonstrate the method on lattices (more generally, on anylocally-finite transitive graph) at very high temperatures.

An exposition to information percolation for the Ising model