We study a setting where objects and privately-informed buyers arrive stochastically to a market. A seller in this setting faces a sequential allocation problem with a dynamic population. We derive both efficient and revenue-maximizing incentive compatible direct mechanisms. Our main result shows that the sequential ascending auction is a simple indirect mechanism that achieves these desirable objectives. We construct equilibria in memoryless strategies where, in every period, bidders reveal all private information. These equilibria are outcome equivalent to the direct mechanisms. In contrast to static settings, sequential second-price auctions cannot yield these outcomes, as they do not reveal sufficient information.
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number
11456.
Find related papers by JEL classification: D44 - Microeconomics - - Market Structure and Pricing - - - Auctions D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
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Mallesh Pai & Rakesh Vohra, 2008.
"Optimal Dynamic Auctions,"
Discussion Papers
1461, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
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