Representations of Boolean functions by real polynomials
play an important role in complexity theory. Typically,
one is interested in the least degree of a polynomial
p(x_1,...,x_n) that approximates or sign-represents
a given Boolean function f(x_1,...,x_n). This article
surveys a new and growing body of work in communication
complexity that centers around the dual objects,
i.e., polynomials that certify the difficulty of
approximating or sign-representing a given function.
We provide a unified guide to the following results,
complete with all the key proofs:

(1) Sherstov's Degree/Discrepancy Theorem, which
translates lower bounds on the threshold degree of
a Boolean function into upper bounds on the discrepancy
of a related function;

(2) Two different methods for proving lower bounds
on bounded-error communication based on the approximate
degree: Sherstov's pattern matrix method and Shi and
Zhu's block composition method;

(3) Extension of the pattern matrix method to the
multiparty model, obtained by Lee and Shraibman and
by Chattopadhyay and Ada, and the resulting improved
lower bounds for DISJOINTNESS;

(4) David and Pitassi's separation of NP and BPP in
multiparty communication complexity for k=(1-eps)log n
players.