We examine the design of incentive-compatible screening mechanisms for dynamic environments in which the agents' types follow a (possibly non-Markov) stochastic process, decisions may be made over time and may affect the type process, and payoffs need not be time-separable. We derive a formula...

We examine the design of incentive-compatible screening mechanisms for dynamic environments in which the agents' types follow a (possibly non-Markov) stochastic process, decisions may be made over time and may affect the type process, and payoffs need not be time-separable. We derive a formula...

We study a combinatorial variant of the classical principal-agent model. In our setting a principal wishes to incentivize a team of strategic agents to exert costly effort on his behalf. Agentsʼ actions are hidden and the principal observes only the outcome of the team, which depends...