In this paper, we introduce a generalization of the standard Stackelberg Games (SGs)
framework: Calibrated Stackelberg Games. In CSGs, a principal repeatedly interacts with an …

We study repeated two-player games where one of the players, the learner, employs a no-
regret learning strategy, while the other, the optimizer, is a rational utility maximizer. We …

We consider the problem of evaluating forecasts of binary events whose predictions are
consumed by rational agents who take an action in response to a prediction, but whose …

We give a simple and computationally efficient algorithm that, for any constant ε> 0, obtains ε
T-swap regret within only T=(n) rounds; this is an exponential improvement compared to the …

We consider a number of questions related to tradeoffs between reward and regret in
repeated gameplay between two agents. To facilitate this, we introduce a notion of …

Many real-life contractual relations differ completely from the clean, static model at the heart
of principal-agent theory. Typically, they involve repeated strategic interactions of the …

We analyze a scenario in which software agents implemented as regret-minimizing
algorithms engage in a repeated auction on behalf of their users. We study first-price and …

We consider the problem of repeatedly auctioning a single item to multiple iid buyers who
each use a no-regret learning algorithm to bid over time. In particular, we study the seller's …

We study the quality of outcomes in repeated games when the population of players is
dynamically changing, and where participants use learning algorithms to adapt to the …

We study a repeated Principal Agent problem between a long lived Principal and Agent pair
in a prior free setting. In our setting, the sequence of realized states of nature may be …