This paper studies learning in monotone Bayesian games with one-dimensional types and finitely many actions. Players switch between actions at a set of thresholds.  A learning algorithm under which players adjust their strategies in the direction of better ones using payoffs received at...

We study strategic information transmission in a Sender-Receiver game where players' optimal actions depend on the realization of multiple signals but the players disagree on the relative importance of each piece of news. We characterize a statistical environment - featuring symmetric loss...

We study strategic information transmission in a Sender-Receiver game where players' optimal actions depend on the realization of multiple signals but the players disagree on the relative importance of each piece of news. We characterize a statistical environment - featuring symmetric loss...

We study strategic information transmission in a Sender-Receiver game where players' optimal actions depend on the realization of multiple signals but the players disagree on the relative importance of each piece of news. We characterize a statistical environment - featuring symmetric loss...

We study strategic communication between a Sender and Receiver who are both uncertain about their preferred actions. The Sender observes noisy signals about both players' ideal policies and then communicates with the Receiver. Even though Sender and Receiver disagree about ideal policies as a...