Hostname: page-component-76d6cb85b7-2r2wp Total loading time: 0 Render date: 2026-07-21T10:52:26.399Z Has data issue: false hasContentIssue false

Optimal thresholds for monotone non-Boolean functions

Published online by Cambridge University Press:  21 July 2026

Saba Lepsveridze
Affiliation:
Massachusetts Institute of Technology, USA
Allen Lin*
Affiliation:
Massachusetts Institute of Technology, USA
*
Corresponding author: Allen Lin; Email: allenees@mit.edu

Abstract

Let $[q] = \{0,1,\ldots ,q-1\}$, let $\Delta [q]$ denote the simplex of probability measures on $[q]$, and let $\gamma$ denote the Lebesgue measure normalized on $\Delta [q]$. We prove that for any symmetric monotone function ${\kern1pt}f \colon{\kern-1pt} [q]^n \to [q]$ and any $a \in [q]$, we have

\begin{equation*} \gamma (\{\mu \in \Delta [q]\;\vert \;\mathbb{P}_{x\sim \mu ^{\otimes n}}[\,f(x)=a] \in (\varepsilon ,1-\varepsilon )\}) = O(1/\log n)\text{.} \end{equation*}

We also show that this bound is tight. This improves Kalai and Mossel's previous bound of $O(\!\log \log n/\log n)$ and answers their question completely.

Information

Type
Paper
Copyright
© The Author(s), 2026. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable