Inference and decision models for regulatory and business challenges in low-Income countries

Abstract

This thesis develops inference and decision models to address challenges of particular relevance in low-income countries (LICs). The areas studied include intelligent tutoring systems (ITS), network infrastructure pricing, and anti-counterfeiting. The ITS chapter identifies previously unknown and serious limitations to Bayesian Knowledge Tracing and Deep Knowledge Tracing, which are two highly-cited methods designed to aid adaptive educational software. The work on Deep Knowledge Tracing led to new data augmentation methods for training recurrent neural networks to be robust in the face of unseen input sequences. We propose a statistically consistent, efficient, and unbiased alternative inference method for questions engaging one skill at a time. The network infrastructure pricing chapters examine how to allocate the cost of a future infrastructure network whose structure depends on the price-taking decisions of potential users. In a multi-period setting, strategic joining delay by users typically leads to lower utility. We develop a cost-allocation rule that uses rebates to prevent strategic delay. In the single-period setting, we derive closed-form solutions to the expected value of offering to build a simple 1D network and use the 1D solution to establish a lower-bound estimate for more complex 2D networks. The anti-counterfeiting chapter investigates the strategic procurement of counterfeits by retailers and the effects of shared retailer reputation on equilibrium procurement decisions using models that are more flexible and tractable than those previously appearing in the literature.