Teaching and Learning in Uncertainty

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 t...

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Bibliographic Details
Published inIEEE transactions on information theory Vol. 67; no. 1; pp. 598 - 615
Main Authors Jog, Varun, Loh, Po-Ling
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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Summary: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.
ISSN:0018-9448
1557-9654
DOI:10.1109/TIT.2020.3030842