  I describe a Bayesian persuasion problem where Receiver has a private type
representing a cutoff for choosing Sender's preferred action, and Sender has
maxmin preferences over all Receiver type distributions with known mean and
bounds. This problem can be represented as a zero-sum game where Sender chooses
a distribution of posterior mean beliefs that is a mean-preserving contraction
of the prior over states, and an adversarial Nature chooses a Receiver type
distribution with the known mean; the player with the higher realization from
their chosen distribution wins. I formalize the connection between maxmin
persuasion and similar games used to model political spending, all-pay
auctions, and competitive persuasion. In both a standard binary-state setting
and a new continuous-state setting, Sender optimally linearizes the prior
distribution over states to create a distribution of posterior means that is
uniform on a known interval with an atom at the lower bound of its support.
