A study of distributional complexity measures for Boolean functions

Journal article, 2026

A number of complexity measures for Boolean functions have previously been introduced. These include
(1) sensitivity, (2) block sensitivity, (3) witness complexity, (4) subcube partition complexity and (5) algorithmic
complexity. Each of these is concerned with ``worst-case'' inputs. It has been shown that there is ``asymptotic separation''
between these complexity measures and very recently, due to the work of Huang, it has been established that they are all
``polynomially related''. In this paper, we study the notion of distributional complexity where the input bits are independent
and one considers all of the above notions in expectation. We obtain a number of results concerning distributional complexity
measures, among others addressing the above concepts of ``asymptotic separation'' and being ``polynomially related'' in this
context. We introduce a new distributional complexity measure, local witness complexity, which only makes sense in the
distributional context and we also study a new version of algorithmic complexity which involves partial information.
Many interesting examples are presented including some related to percolation. The latter connects a number of the recent
developments in percolation theory over the last two decades with the study of complexity measures in theoretical computer science.