In this paper a simple dynamic optimization problem is solved with the help of the recursive saddle point method developed by Marcet and Marimon (1999). According to Marcet and Marimon, their technique should yield a full characterization of the set of solutions for this problem. We show though,...

We bring together the theories of duality and dynamic programming. We show that the dual of an additively separable dynamic optimization problem can be recursively decomposed using summaries of past Lagrange multipliers as state variables. Analogous to the Bellman decomposition of the primal...

Many separable dynamic incentive problems have primal recursive formulations in which utility promises serve as state variables. We associate families of dual recursive problems with these by selectively dualizing constraints. We make transparent the connections between recursive primal and dual...

Several recent papers have proposed recursive Lagrangian-basedmethods for solving dynamic contracting problems. Thesemethods give rise to Bellman operators that incorporate either a dual inf-sup or a saddle point operation. We give conditions that ensure the Bellman operator implied by a dual...

We characterize the boundaries of the set of transfers implementing a given allocation rule without imposing any assumptions on the agent's type space or utility function besides quasi-linearity. In particular, we characterize the pointwise largest and the pointwise smallest transfer that...

This paper provides a new explanation why several US states have implemented supermajority requirements for tax increases. We model a dynamic and stochastic OLG economy where individual preferences depend on age and change over time in a systematic way. In this setting, we show that the first...

This paper explores the sale of an object to an ambiguity averse buyer. We show that the seller can increase his profit by using an ambiguous mechanism. That is, the seller can benefit from hiding certain features of the mechanism that he has committed to from the agent. We then characterize the...