This volume brings together the collected contributions of Dirk
Bergemann and Juuso Valimaki, 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.
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