scholarly journals Multidimensional mechanism design: Revenue maximization and the multiple-good monopoly

2007 ◽  
Vol 137 (1) ◽  
pp. 153-185 ◽  
Author(s):  
Alejandro M. Manelli ◽  
Daniel R. Vincent
Keyword(s):  
Mechanism Design ◽  
Revenue Maximization

Mechanism Design for Correlated Valuations: Efficient Methods for Revenue Maximization

2021 ◽  
Author(s):  
Michael Albert ◽  
Vincent Conitzer ◽  
Giuseppe Lopomo ◽  
Peter Stone
Keyword(s):  
Mechanism Design ◽  
Good Reason ◽  
Exact Distribution ◽  
Revenue Maximization ◽  
Computationally Efficient ◽  
Auction Design ◽  
Efficient Procedure ◽  
Underlying Distribution ◽  
Theoretical Results

Traditionally, much of the focus of the mechanism/auction design community has been on revenue optimal mechanisms for settings where bidders’ private valuations over outcomes can be reasonably thought of as independent of each other. This has been the case even though there is good reason to believe that valuations are often correlated and there are theoretical results suggesting that mechanisms designed with this correlation in mind can generate much higher revenue. In “Mechanism Design for Correlated Valuations: Efficient Methods for Revenue Maximization,” we look at the setting where there is correlation, but the exact distribution is unknown and must be estimated from samples. We show that in this setting, the previous extremely strong theoretical results around the usefulness of correlation are now very sensitive to the degree of correlation in the underlying distribution and the number of samples that the mechanism designer has access to. However, we also show that if correlation is sufficient, we can construct mechanisms, using a computationally efficient procedure, that significantly outperform traditional mechanism design paradigms.

A Mechanism Design and Learning Approach for Revenue Maximization on Cloud Dynamic Spot Markets

2021 ◽  
Author(s):  
Asterios Tsiourvas ◽  
Constantinos Bitsakos ◽  
Ioannis Konstantinou ◽  
Dimitris Fotakis ◽  
Nectarios Koziris
Keyword(s):  
Mechanism Design ◽  
Revenue Maximization ◽  
Spot Markets ◽  
Learning Approach

scholarly journals Dynamic Revenue Maximization with Heterogeneous Objects: A Mechanism Design Approach

2009 ◽  
Vol 1 (2) ◽  
pp. 168-198 ◽  
Author(s):  
Alex Gershkov ◽  
Benny Moldovanu
Keyword(s):  
Dynamic Programming ◽  
Mechanism Design ◽  
Design Approach ◽  
Programming Approach ◽  
Revenue Maximization ◽  
Allocation Policy ◽  
Dynamic Programming Approach ◽  
Heterogeneous Objects ◽  
Empirical Regularities ◽  
Expected Revenue

We 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)

Bayesian Mechanism Design

2013 ◽  
Author(s):  
Aranyak Mehta
Keyword(s):  
Mechanism Design
Sign in / Sign up

Export Citation Format

Share Document