Phase transition in a sequential assignment problem on graphs

We study the following sequential assignment problem on a finite graph G = (V ,E). Each edge e ∈ E starts with an integer value n e ≥ 0, and we write n =∑ e∈En e. At time t, 1 ≤ t ≤ n, a uniformly random vertex v ∈ V is generated, and one of the edges f incident with v must be selected. The value of f is then decreased by 1. There is a unit final reward if the configuration (0, . . . , 0) is reached. Our main result is that there is a phase transition: as n←∞, the expected reward under the optimal policy approaches a constant c G &gt; 0 when (n e/n : e ∈ E) converges to a point in the interior of a certain convex set R G, and goes to 0 exponentially when (n e/n : e ∈ E) is bounded away from R G. We also obtain estimates in the near-critical region, that is when (n e/n : e ∈ E) lies close to ∂R G. We supply quantitative error bounds in our arguments. </p

Sandpile models

This survey is an extended version of lectures given at the Cornell Probability Summer School 2013. The fundamental facts about the Abelian sandpile model on a finite graph and its connections to related models are presented. We discuss exactly computable results via Majumdar and Dhar's method. The main ideas of Priezzhev's computation of the height probabilities in 2D are also presented, including explicit error estimates involved in passing to the limit of the infinite lattice. We also discuss various questions arising on infinite graphs, such as convergence to a sandpile measure, and stabilizability of infinite configurations

Toppling and height probabilities in sandpiles

We study Abelian sandpiles numerically, using exact sampling. Our method uses a combination of Wilson's algorithm to generate uniformly distributed spanning trees, and Majumdar and Dhar's bijection with sandpiles. We study the probability of topplings of individual vertices in avalanches initiated at the centre of large cubic lattices in dimensions d = 2, 3 and 5. Based on these, we estimate the values of the toppling probability exponent in the infinite volume limit in dimensions d = 2, 3, and find good agreement with theoretical results on the mean-field value of the exponent in d ≥ 5. We also study the distribution of the number of waves in 2D avalanches. Our simulation method, combined with a variance reduction idea, lends itself well to studying some problems even in very high dimensions. We illustrate this with an estimation of the single site height probability distribution in d = 32, and compare this to the asymptotic behaviour as d → ∞

Geometry of uniform spanning forest components in high dimensions

We study the geometry of the component of the origin in the uniform spanning forest of Z^d and give bounds on the size of balls in the intrinsic metric.<br/

Geometry of uniform spanning forest components in high dimensions

We study the geometry of the component of the origin in the uniform spanning forest of Z^d and give bounds on the size of balls in the intrinsic metric.<br/

Toppling and height probabilities in sandpiles

We study Abelian sandpiles numerically, using exact sampling. Our method uses a combination of Wilson's algorithm to generate uniformly distributed spanning trees, and Majumdar and Dhar's bijection with sandpiles. We study the probability of topplings of individual vertices in avalanches initiated at the centre of large cubic lattices in dimensions d = 2, 3 and 5. Based on these, we estimate the values of the toppling probability exponent in the infinite volume limit in dimensions d = 2, 3, and find good agreement with theoretical results on the mean-field value of the exponent in d ≥ 5. We also study the distribution of the number of waves in 2D avalanches. Our simulation method, combined with a variance reduction idea, lends itself well to studying some problems even in very high dimensions. We illustrate this with an estimation of the single site height probability distribution in d = 32, and compare this to the asymptotic behaviour as d → ∞