English

Dictator Functions Maximize Mutual Information

Information Theory 2018-02-05 v3 math.IT Probability

Abstract

Let (X,Y)(\mathbf X, \mathbf Y) denote nn independent, identically distributed copies of two arbitrarily correlated Rademacher random variables (X,Y)(X, Y). We prove that the inequality I(f(X);g(Y))I(X;Y)I(f(\mathbf X); g(\mathbf Y)) \le I(X; Y) holds for any two Boolean functions: f,g ⁣:{1,1}n{1,1}f,g \colon \{-1,1\}^n \to \{-1,1\} (I(;)I(\cdot; \cdot) denotes mutual information). We further show that equality in general is achieved only by the dictator functions f(x)=±g(x)=±xif(\mathbf x)=\pm g(\mathbf x)=\pm x_i, i{1,2,,n}i \in \{1,2,\dots,n\}.

Cite

@article{arxiv.1604.02109,
  title  = {Dictator Functions Maximize Mutual Information},
  author = {Georg Pichler and Pablo Piantanida and Gerald Matz},
  journal= {arXiv preprint arXiv:1604.02109},
  year   = {2018}
}

Comments

accepted for publication in the Annals of Applied Probability; 8 pages, 1 figure

R2 v1 2026-06-22T13:27:39.167Z