  Complex dynamical systems driven by the unravelling of information can be
modelled effectively by treating the underlying flow of information as the
model input. Complicated dynamical behaviour of the system is then derived as
an output. Such an information-based approach is in sharp contrast to the
conventional mathematical modelling of information-driven systems whereby one
attempts to come up with essentially {\it ad hoc} models for the outputs. Here,
dynamics of electoral competition is modelled by the specification of the flow
of information relevant to election. The seemingly random evolution of the
election poll statistics are then derived as model outputs, which in turn are
used to study election prediction, impact of disinformation, and the optimal
strategy for information management in an election campaign.
