Noise Sensitivity of Boolean Functions And Applications to Percolation
نویسندگان
چکیده
It is shown that a large class of events in a product probability space are highly sensitive to noise, in the sense that with high probability, the configuration with an arbitrary small percent of random errors gives almost no prediction whether the event occurs. On the other hand, weighted majority functions are shown to be noise-stable. Several necessary and sufficient conditions for noise sensitivity and stability are given. Consider, for example, bond percolation on an n+ 1 by n grid. A configuration is a function that assigns to every edge the value 0 or 1. Let ω be a random configuration, selected according to the uniform measure. A crossing is a path that joins the left and right sides of the rectangle, and consists entirely of edges e with ω(e) = 1. By duality, the probability for having a crossing is 1/2. Fix an ǫ ∈ (0, 1). For each edge e, let ω′(e) = ω(e) with probability 1 − ǫ, and ω′(e) = 1 − ω(e) with probability ǫ, independently of the other edges. Let p(τ) be the probability for having a crossing in ω, conditioned on ω′ = τ . Then for all n sufficiently large, P { τ : |p(τ)− 1/2| > ǫ } < ǫ.
منابع مشابه
Lectures on noise sensitivity and percolation
The goal of this set of lectures is to combine two seemingly unrelated topics: • The study of Boolean functions, a field particularly active in computer science • Some models in statistical physics, mostly percolation The link between these two fields can be loosely explained as follows: a percolation configuration is built out of a collection of i.i.d " bits " which determines whether the corr...
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The notion of noise sensitivity was introduced by Benjamini, Kalai, and Schramm in 1998 [3], in the context of percolation theory. Since then noise sensitivity has found applications in many fields, including concentration of measure, social choice theory, and theoretical computer science. Say we have n people voting for one of two candidates, D or R. One can write their choices as ω = (ω(1), ....
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