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Bootstrap percolation in three dimensions. (English) Zbl 1187.60082

Author’s abstract: By bootstrap percolation we mean the following deterministic process on a graph \(G\). Given a set \(A\) of vertices “infected” at time 0, new vertices are subsequently infected, at each time step, if they have at least \(r\in \mathbb N\) previously infected neighbors. When the set \(A\) is chosen at random, the main aim is to determine the critical probability \(p_c(G,r)\) at which percolation (infection of the entire graph) becomes likely to occur. This bootstrap process has been extensively studied on the \(d\)-dimensional grid \([n]^d\): with \(2\leq r\leq d\) fixed, it was proved by R. Cerf and E. N. M. Cirillo [Ann. Probab. 27, No. 4, 1837–1850 (1999; Zbl 0960.60088)] (for \(d=r=3\)), and by R. Cerf and F. Manzo [Stochastic Processes Appl. 101, No. 1, 69-82 (2002; Zbl 1075.82010)] (in general), that \[ p_c([n]^d,r) = \Theta \left(\frac {1}{\log _{r-1}^n}\right)^{d-r+1}, \] where \(\log _{(r)}\) is an \(r\)-times iterated logarithm. However, the exact threshold function is only known in the case \(d=r=2\), where it was shown by Holroyd to be \((1+o(1))\frac{\pi ^2}{18\log n}\). In this paper we shall determine the exact threshold in the crucial case \(d=r=3\), and lay the groundwork for solving the problem for all fixed \(d\) and \(r\).

MSC:

60K35 Interacting random processes; statistical mechanics type models; percolation theory
60C05 Combinatorial probability
82B43 Percolation

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