Predictions about people, such as their expected educational achievement or their credit
risk, can be performative and shape the outcome that they are designed to predict …

We study the hidden-action principal-agent problem in an online setting. In each round, the
principal posts a contract that specifies the payment to the agent based on each outcome …

Strategic classification studies learning in settings where self-interested users can
strategically modify their features to obtain favorable predictive outcomes. A key working …

Principal component regression (PCR) is a popular technique for fixed-design error-in-
variables regression, a generalization of the linear regression setting in which the observed …

Algorithmic decision-making in high-stakes domains often involves assigning decisions to
agents with incentives to strategically modify their input to the algorithm. In addition to …

The creator economy has revolutionized the way individuals can profit through online
platforms. In this paper, we initiate the study of online learning in the creator economy by …

When users can benefit from certain predictive outcomes, they may be prone to act to
achieve those outcome, eg, by strategically modifying their features. The goal in strategic …

We study offline reinforcement learning under a novel model called strategic MDP, which
characterizes the strategic interactions between a principal and a sequence of myopic …

The primary goal in recommendation is to suggest relevant content to users, but optimizing
for accuracy often results in recommendations that lack diversity. To remedy this …

Motivated by equilibrium models of labor markets, we develop a formulation of causal
strategic classification in which strategic agents can directly manipulate their outcomes. As …