Technical Program

Paper Detail

Paper Title Teaching and learning in uncertainty
Paper IdentifierFR1.R2.4
Authors Varun Jog, University of Wisconsin - Madison, United States
Session Emerging Applications of IT II
Location Saint Germain, Level 3
Session Time Friday, 12 July, 09:50 - 11:10
Presentation Time Friday, 12 July, 10:50 - 11:10
Manuscript  Click here to download the manuscript
Abstract 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 $\Theta$, however, the teacher herself is not certain about $\Theta$ and needs to simultaneously learn it and teach it to the student. We model the teacher's and the student's uncertainty via binary symmetric channels and employ a simple heuristic decoder at the student's end. 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 $\Theta$ at each time instant. 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. Our primary technical contribution is a detailed analysis of the large deviation properties of the sign of a transient Markov random walk on the integers.