  We investigate a simple model for social learning with two agents: a teacher
and a student. The teacher's goal is to teach the student the state of the
world; however, the teacher himself is not certain about the state of the world
and needs to simultaneously learn this parameter and teach it to the student.
We model the teacher's and student's uncertainties via noisy transmission
channels, and employ two simple decoding strategies for the student. We focus
on two teaching strategies: a "low-effort" strategy of simply forwarding
information, and a "high-effort" strategy of communicating the teacher's
current best estimate of the world at each time instant, based on his own
cumulative learning. Using tools from large deviation theory, we calculate the
exact learning rates for these strategies and demonstrate regimes where the
low-effort strategy outperforms the high-effort strategy. Finally, we present a
conjecture concerning the optimal learning rate for the student over all joint
strategies between the student and the teacher.
