The field of artificial intelligence (AI) strives to build rational agents capable of perceiving the
world around them and taking actions to advance specified goals. Put another way, AI …

Modern recommender systems lie at the heart of complex ecosystems that couple the
behavior of users, content providers, advertisers, and other actors. Despite this, the focus of …

The literature on algorithmic mechanism design is mostly concerned with game-theoretic
versions of optimization problems to which standard economic money-based mechanisms …

Algorithms are often used to produce decision-making rules that classify or evaluate
individuals. When these individuals have incentives to be classified a certain way, they may …

We address the question of repeatedly learning linear classifiers against agents who
are\emph {strategically} trying to\emph {game} the deployed classifiers, and we use …

We consider a platform's problem of collecting data from privacy sensitive users to estimate
an underlying parameter of interest. We formulate this question as a Bayesian-optimal …

In many predictive decision-making scenarios, such as credit scoring and academic testing,
a decision-maker must construct a model that accounts for agents' propensity to “game” the …

Inspired by real-time ad exchanges for online display advertising, we consider the problem
of inferring a buyer's value distribution for a good when the buyer is repeatedly interacting …

We propose an optimum mechanism for providing monetary incentives to the data sources
of a statistical estimator such as linear regression, so that high quality data is provided at low …

Motivated by applications such as college admission and insurance rate determination, we
propose an evaluation problem where the inputs are controlled by strategic individuals who …