# Noise Stability and Threshold Circuits

The purpose of this post is to describe an old conjecture (or guesses, see this post) by Itai Benjamini, Oded Schramm and myself (taken from this paper) on noise stability of threshold functions. I will start by formulating the conjectures and a little later I will explain further the notions I am using.

## The conjectures

Conjecture 1:  Let $f$ be a monotone Boolean function described by  monotone threshold circuits of size M and depth D. Then $f$ is  stable to (1/t)-noise where $t=(\log M)^{100D}$.

Conjecture 2:   Let $f$ be a monotone Boolean function described by  a threshold circuits of size M and depth D. Then $f$ is  stable to (1/t)-noise where $t=(\log M)^{100D}$.

The constant 100 in the exponent is, of course, negotiable. In fact, replacing $100D$ with any  function of $D$ will be sufficient for the applications we have in mind. The best we can hope for is that the conjectures are true if  $t$ behaves like  $t=(\log M)^{D-1}$.

Conjecture 1 is plausible but it looks difficult. The stronger Conjecture 2 while tempting is quite reckless. Note that the conjectures differ “only” in the location of the word “monotone”. Continue reading

# Noise Sensitivity Lecture and Tales

### A lecture about Noise sensitivity

Several of my recent research projects are related to noise, and noise was also a topic of a recent somewhat philosophical post.   My oldest and perhaps most respectable noise-related project was the work with Itai Benjamini and Oded Schramm on noise sensitivity of Boolean functions. Recently I gave lectures at several places about noise sensitivity and noise stability of Boolean functions, starting with the definition of these terms and their Fourier description,  then the result of Itai, Oded and myself that the crossing event of planar critical percolation is noise-sensitive, and ending with two recent breakthrough results. One is the work by Garban, Pete, and Schramm   that described the scaling limit of the spectral distribution for the crossing event in critical percolation. The second is  the “majority is stablest” theorem of Mossel, O’Donnell, and Oleszkiewicz and the connections to hardness of approximation.

A fun way to explain noise sensitivity (especially after the 2000 US election) is in terms of the probability that small mistakes in counting the votes in an election will change the outcome. Here we assume that there are two candidates, that every voter votes with probability 1/2 for each candidate (independently) and that when we count every ballot there is a small probability $t$ that the voter intention is reversed.

Noise sensitivity is one of the reasons to reject the HEX voting rule; but perhaps not the main one :) . (Noise stability/sensitivity and the related notion of influence play some role in the recent polymath1 project.)

Here is the power point presentation. I hope to blog at a later time about analysis of Boolean functions and about noise sensitivity and noise stability.

Meanwhile let me just give a few general comments and tales:

### Some noise sensitivity tales:

1. We started working on noise sensitivity in 1995 and at that time Itai was expecting a son, so a friend lent him a pager. When we wrote the paper Alon was already 4 years old.

2. The paper by Boris Tsirelson and Anatoly Vershik (that also describes work spanning many years) contains a similar notion. Their motivation was entirely different.  One difficulty in translating the two formulations is that “Boolean function” (or rather “a sequence of Boolean functions + some uniformity condition” ) in our work  is translated to “noise” in Tsirelson’s terminology. And “noise sensitive” is translated to “black” or “non-Fock” or “non-classical” in their work.

3. After the 2000 elections Itai Benjamini and Elchanan Mossel wrote a popular piece about the relevance of this work for elections. Mathematician and writer Barry Cipra wrote a lovely  small article about it. (Looking at it now I find it very impressive how Barry put so much information in a vivid way in such a short article.)

Here is one paragraph from Cipra’s article:

“Three researchers—Itai Benjamini and Oded Schramm, both of Microsoft Research, and Gil Kalai of the Hebrew University in Jerusalem—have turned the question around: How close does a race have to be in order for errors in counting to have a non-negligible chance of reversing the outcome? Their analysis indicates that a simple, nationwide popular vote would be more stable against mistakes than the beleaguered Electoral College system. Indeed, they find, straightforward majority vote is more stable than any other voting method.” Continue reading