We provide an introduction to the recent developments of dynamic mechanism design, with a primary focus on the quasilinear case. First, we describe socially optimal (or efficient) dynamic mechanisms. These mechanisms extend the well-known Vickrey– Clark–Groves and D’Aspremont–Gérard–Varet mechanisms to a dynamic environment. Second, we discuss revenue optimal mechanisms. We cover models of sequential screening and revenue-maximizing auctions with dynamically changing bidder types. We also discuss models of information management where the mechanism designer can control (at least partially) the stochastic process governing the agents’ types. Third, we consider models with changing populations of agents over time. After discussing related models with risk-averse agents and limited liability, we conclude with a number of open questions and challenges that remain for the theory of dynamic mechanism design.

Many auctions and procurement contests entail non-trivial bidding costs, which makes the bidders’ participation decisions endogenous to the auction design. We analyse the effect of different auction rules on potential bidders’ incentives to participate. We focus on first-price auctions with affiliated common values and a large pool of potential bidders. Our main interest is on auctions where the realized number of bidders is unknown at the bidding stage. In contrast to the standard case, both participation and bidding decisions are often non-monotonic in the symmetric equilibrium of our model. The expected revenue to the seller is often higher in the auction where the realized number of participating bidders is not disclosed.

We analyse information aggregation in a stopping game with uncertain pay-offs that are correlated across players. Players learn from their own private experiences as well as by observing the actions of other players. We give a full characterization of the symmetric mixed strategy equilibrium, and show that information aggregates in randomly occurring exit waves. Observational learning induces the players to stay in the game longer. The equilibria display aggregate randomness even for large numbers of players.