AND testing and robust judgement aggregation

A function fg¶{0,1}ⁿ→ {0,1} is called an approximate AND-homomorphism if choosing x,ygn uniformly at random, we have that f(xg§ y) = f(x)g§ f(y) with probability at least 1-ϵ, where xg§ y = (x₁g§ y₁,...,x_ng§ y_n). We prove that if fg¶ {0,1}ⁿ → {0,1} is an approximate AND-homomorphism, then f is -close to either a constant function or an AND function, where (ϵ) → 0 as ϵ→ 0. This improves on a result of Nehama, who proved a similar statement in which δdepends on n. Our theorem implies a strong result on judgement aggregation in computational social choice. In the language of social choice, our result shows that if f is ϵ-close to satisfying judgement aggregation, then it is (ϵ)-close to an oligarchy (the name for the AND function in social choice theory). This improves on Nehama's result, in which δdecays polynomially with n. Our result follows from a more general one, in which we characterize approximate solutions to the eigenvalue equation f = λ g, where is the downwards noise operator f(x) = _y[f(x g§ y)], f is [0,1]-valued, and g is {0,1}-valued. We identify all exact solutions to this equation, and show that any approximate solution in which f and λ g are close is close to an exact solution.