Work in Progress: Building a “Project-Based Learning for Rural Alabama STEM Middle School Teachers in Machine Learning and Robotics” RET Site

Gong, Xiaowen; Marghitu, Daniela; Roppel, Thaddeus; Wang, Chih-hsuan; Russell, Melody

doi:10.18260/1-2--42795

We introduce a sequential Bayesian binary hypothesis testing problem under social learning, termed selfish learning, where agents work to maximize their individual rewards. In particular, each agent receives a private signal and is aware of decisions made by earlier-acting agents. Beside inferring the underlying hypothesis, agents also decide whether to stop and declare, or pass the inference to the next agent. The employer rewards only correct responses and the reward per worker decreases with the number of employees used for decision making. We characterize decision regions of agents in the infinite and finite horizon. In particular, we show that the decision boundaries in the infinite horizon are the solutions to a Markov Decision Process with discounted costs, and can be solved using value iteration. In the finite horizon, we show that team performance is enhanced upon appropriate incentivization when compared to sequential social learning.

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