This volume brings together the collected contributions of Dirk Bergemann and Juuso Välimäki, on the theme of learning, experimentation and intertemporal incentives, spanning over two decades of thought (1996 to 2019).The collection starts with a comprehensive introduction to the recent developments of dynamic mechanism design with a primary focus on the quasilinear case. The authors describe socially optimal and revenue optimal dynamic mechanism. They cover models of sequential screening and revenue maximizing auctions with dynamically changing bidder types, and also discuss models of information management where the mechanism designer can control (at least partially) the stochastic process governing the agent's types.Consolidating the research agenda on learning, experimentation, and dynamic mechanism design, which has been prominent in the area of economic theory, the authors present for the first time the main results of the research agenda in this volume.

This volume brings together the collected contributions of Dirk Bergemann and Juuso Välimäki, on the theme of learning, experimentation and intertemporal incentives, spanning over two decades of thought (1996 to 2019).The collection starts with a comprehensive introduction to the recent developments of dynamic mechanism design with a primary focus on the quasilinear case. The authors describe socially optimal and revenue optimal dynamic mechanism. They cover models of sequential screening and revenue maximizing auctions with dynamically changing bidder types, and also discuss models of information management where the mechanism designer can control (at least partially) the stochastic process governing the agent's types.Consolidating the research agenda on learning, experimentation, and dynamic mechanism design, which has been prominent in the area of economic theory, the authors present for the first time the main results of the research agenda in this volume.

This volume brings together the collected contributions of Dirk Bergemann and Juuso Välimäki, on the theme of learning, experimentation and intertemporal incentives, spanning over two decades of thought (1996 to 2019).The collection starts with a comprehensive introduction to the recent developments of dynamic mechanism design with a primary focus on the quasilinear case. The authors describe socially optimal and revenue optimal dynamic mechanism. They cover models of sequential screening and revenue maximizing auctions with dynamically changing bidder types, and also discuss models of information management where the mechanism designer can control (at least partially) the stochastic process governing the agent's types.Consolidating the research agenda on learning, experimentation, and dynamic mechanism design, which has been prominent in the area of economic theory, the authors present for the first time the main results of the research agenda in this volume.