We characterize the incentive compatible, constrained efficient policy ("second-best") in a dynamic matching environment, where impatient, privately informed agents arrive over time, and where the designer gradually learns about the distribution of agents' values. We also derive conditions on...

We consider a standard social choice environment with linear utilities and independent, one-dimensional, private types. We prove that for any Bayesian incentive compatible mechanism there exists an equivalent dominant strategy incentive compatible mechanism that delivers the same interim...

We study a novel dynamic principal—agent setting with moral hazard and adverse selection (persistent as well as repeated). In the model an agent whose skills are his private information faces a finite sequence of tasks, one after the other. Upon arrival of each task the agent learns its level...

We study the welfare maximizing assignment of several heterogeneous, commonly ranked objects to impatient agents with privately known characteristics who arrive sequentially according to a Poisson or renewal process. There is a deadline after which no more objects can be allocated. We first show...

We study an allocation problem where a set of objects needs to be allocated to agents arriving over time. The basic model is of the private, independent values type. The dynamically efficient allocation is implementable if the distribution of agents' values is known. Whereas lack of knowledge...

We consider a standard social choice environment with linear utility and one-dimensional types. We show by counterexample that, when there are at least three physical alternatives, Bayes-Nash Incentive Compatibility (BIC) and Dominant Strategy Incentive Compatibility (DIC) need no longer be...

We study dominant strategy incentive compatible (DIC) and deterministic mechanisms in a social choice setting with several alternatives. The agents are privately informed about their preferences, and have single-crossing utility functions. Monetary transfers are not feasible. We use an...

We show that a non-Bayesian learning procedure leads to very permissive implementation results concerning the efficient allocation of resources in a dynamic environment where impatient, privately informed agents arrive over time, and where the designer gradually learns about the distribution of...

We study a generalization of the classical monopoly insurance problem under adverse selection (see Stiglitz [1977]) where we allow for a random distribution of losses, possibly correlated with the agent's risk parameter that is private information. Our model explains patterns of observed...