Dynamic Revenue Maximization with Heterogeneous Objects: A Mechanism Design Approach
AbstractWe study the revenue-maximizing allocation of several heterogeneous, commonly ranked objects to impatient agents with privately known characteristics who arrive sequentially. There is a deadline after which no more objects can be allocated. We first characterize implementable allocation schemes, and compute the expected revenue for any implementable, deterministic and Markovian allocation policy. The revenue-maximizing policy is obtained by a variational argument which sheds more light on its properties than the usual dynamic programming approach. Finally, we use our main result in order to derive the optimal inventory choice, and explain empirical regularities about pricing in clearance sales. (JEL C61, D21, D82)
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Bibliographic InfoArticle provided by American Economic Association in its journal American Economic Journal: Microeconomics.
Volume (Year): 1 (2009)
Issue (Month): 2 (August)
Find related papers by JEL classification:
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
- D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Gabriel Bitran & René Caldentey, 2003. "An Overview of Pricing Models for Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 5(3), pages 203-229, August.
- Pashigian, B Peter, 1988. "Demand Uncertainty and Sales: A Study of Fashion and Markdown Pricin g," American Economic Review, American Economic Association, vol. 78(5), pages 936-53, December.
- Das Varma, Gopal & Vettas, Nikolaos, 2001. "Optimal dynamic pricing with inventories," Economics Letters, Elsevier, vol. 72(3), pages 335-340, September.
- Dirk Bergemann & Maher Said, 2010.
"Dynamic Auctions: A Survey,"
Cowles Foundation Discussion Papers
1757, Cowles Foundation for Research in Economics, Yale University.
- Dirk Bergemann & Maher Said, 2010. "Dynamic Auctions: A Survey," Cowles Foundation Discussion Papers 1757R, Cowles Foundation for Research in Economics, Yale University, revised May 2010.
- Dirk Bergemann & Maher Said, 2010. "Dynamic Auctions: A Survey," Levine's Working Paper Archive 661465000000000111, David K. Levine.
- Dirk Bergemann & Maher Said, 2010. "Dynamic Auctions: A Survey," Levine's Working Paper Archive 661465000000000035, David K. Levine.
- Deniz Dizdar & Alex Gershkov & Benny Moldovanu, 2010.
"Revenue Maximization in the Dynamic Knapsack Problem,"
Discussion Paper Series
dp544, The Center for the Study of Rationality, Hebrew University, Jerusalem.
- Moldovanu, Benny & Dizdar, Deniz & Gershkov, Alex, 2011. "Revenue maximization in the dynamic knapsack problem," Theoretical Economics, Econometric Society, vol. 6(2), May.
- Johannes Horner & Larry Samuelson, 2011. "Managing Strategic Buyers," Levine's Working Paper Archive 786969000000000025, David K. Levine.
- Johannes Horner & Larry Samuelson, 2008. "Managing Strategic Buyers," Cowles Foundation Discussion Papers 1684R, Cowles Foundation for Research in Economics, Yale University, revised Sep 2010.
- Said, Maher, 2012.
"Auctions with dynamic populations: Efficiency and revenue maximization,"
Journal of Economic Theory,
Elsevier, vol. 147(6), pages 2419-2438.
- Said, Maher, 2008. "Auctions with Dynamic Populations: Efficiency and Revenue Maximization," MPRA Paper 11456, University Library of Munich, Germany.
- Francis Bloch & David Cantala, 2008.
"Markovian assignment rules,"
- Francis Bloch & David Cantala, 2010. "Markovian assignment rules," Serie documentos de trabajo del Centro de Estudios EconÃ³micos 2010-18, El Colegio de México, Centro de Estudios Económicos.
- Johannes Horner & Larry Samuelson, 2009. "Managing Strategic Buyers," Levine's Working Paper Archive 814577000000000059, David K. Levine.
- Pancs, Romans, 2013. "Sequential negotiations with costly information acquisition," Games and Economic Behavior, Elsevier, vol. 82(C), pages 522-543.
- Gershkov, Alex & Moldovanu, Benny, 2012. "Dynamic allocation and pricing: A mechanism design approach," International Journal of Industrial Organization, Elsevier, vol. 30(3), pages 283-286.
- Johannes Horner & Larry Samuelson, 2010. "Managing Strategic Buyers," Levine's Working Paper Archive 661465000000000279, David K. Levine.
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