Volatility of Boolean functions

TL;DR

This paper investigates the dynamic behavior of Boolean functions under stochastic input changes, focusing on their volatility, tameness, noise sensitivity, and stability, with applications to classical functions and percolation models.

Contribution

It introduces a comprehensive framework for analyzing the volatility and related properties of Boolean functions under Markovian input dynamics, connecting these to noise sensitivity and stability concepts.

Findings

Majority function exhibits volatility under certain conditions.

Percolation on trees shows different stability behaviors at various parameters.

Iterated 3-majority and AND/OR functions display distinct dynamic properties.

Abstract

We study the volatility of the output of a Boolean function when the input bits undergo a natural dynamics. For $n = 1,2,\ldots$, let $f_n:\{0,1\}^{m_n} \ra \{0,1\}$ be a Boolean function and $X^{(n)}(t)=(X_1(t),\ldots,X_{m_n}(t))_{t \in [0,\infty)}$ be a vector of i.i.d.\ stationary continuous time Markov chains on $\{0,1\}$ that jump from $0$ to $1$ with rate $p_n \in [0,1]$ and from $1$ to $0$ with rate $q_n=1-p_n$. Our object of study will be $C_n$ which is the number of state changes of $f_n(X^{(n)}(t))$ as a function of $t$ during $[0,1]$. We say that the family $\{f_n\}_{n\ge 1}$ is volatile if $C_n \ra \iy$ in distribution as $n\to\infty$ and say that $\{f_n\}_{n\ge 1}$ is tame if $\{C_n\}_{n\ge 1}$ is tight. We study these concepts in and of themselves as well as investigate their relationship with the recent notions of noise sensitivity and noise stability. In addition, we study the question of lameness which means that $\Pro(C_n =0)\ra 1$ as $n\to\infty$. Finally, we investigate these properties for a number of standard Boolean functions such as the majority function, iterated 3-majority, the AND/OR function on the binary tree and percolation on certain trees at various levels of the parameter $p_n$.