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...

I extend Myerson's (1981) ironing technique to more general objective functions. The approach can be used to solve quasilinear principal-agent models with a one-dimensional type in the general case where the monotonicity constraint implied by incentive compatibility may be binding at the...