Much of the classical work on algorithms for multi-armed bandits focuses on rewards that are stationary over time. By contrast, we study multi-armed bandit (MAB) games, where the rewards obtained by an agent also depend on how many other agents choose the same arm (as might be the case in many...

Recent Medicare legislation seeks to improve patient care quality by financially penalizing providers for hospital-acquired infections (HAIs). However, Medicare cannot directly monitor HAI rates, and instead relies on providers accurately self-reporting HAIs in claims to correctly assess...

Big data has enabled decision-makers to tailor decisions at the individual-level in a variety of domains such as personalized medicine and online advertising. This involves learning a model of decision rewards conditional on individual-specific covariates. In many practical settings, these...

How should agents bid in repeated sequential auctions when they are budget constrained? A motivating example is that of sponsored search auctions, where advertisers bid in a sequence of generalized second price (GSP) auctions. These auctions, specifically in the context of sponsored search, have...

We study the problem of learning shared structure across a sequence of dynamic pricing experiments for related products. We consider a practical formulation where the unknown demand parameters for each product come from an unknown distribution (prior) that is shared across products. We then...

To mitigate environmental and social harm, policy-makers often provide incentives or impose sanctions to discourage harmful behavior. Such policies are usually implemented with limited monitoring capabilities, which may cause strategic behavior that leads to unintended consequences. Three...