Abstract
We present a unified duality approach to Bayesian persuasion. The optimal dual variable, interpreted as a price function, is shown to be a super-gradient of the concave closure of the objective function at the prior belief. Under regularity conditions, our general duality result implies known results for the case when the objective function depends only on the expected state. We apply our approach to characterize the optimal signal in the case when the state is two-dimensional.
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