DocumentCode :
3430049
Title :
Learning user preferences in mechanism design
Author :
Chorppath, Anil Kumar ; Alpcan, Tansu
Author_Institution :
Technical University of Berlin, Deutsche Telekom Laboratories, 10587, Germany
fYear :
2011
fDate :
12-15 Dec. 2011
Firstpage :
5349
Lastpage :
5355
Abstract :
In designing a mechanism for allocation of a divisible resource, the designer needs to know the player utility functions, which are often infinitely dimensional, in order to choose the appropriate pricing and allocation rules. This paper utilizes Gaussian process regression learning techniques to infer general player preferences by a designer in a mechanism design setting. In pricing mechanisms, the price taking players are charged with the appropriate value of Lagrange multiplier, in order to achieve efficiency. This value is obtained iteratively through learning. Likewise, the reserve price in auction mechanisms with price anticipating players, a parameter in allocation and pricing rules, is modified iteratively using online learning to move the system solution to near efficiency. A numerical example illustrates the approach and demonstrates the online learning algorithm.
Keywords :
Algorithm design and analysis; Games; Optimization; Pricing; Resource management; Silicon; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control and European Control Conference (CDC-ECC), 2011 50th IEEE Conference on
Conference_Location :
Orlando, FL, USA
ISSN :
0743-1546
Print_ISBN :
978-1-61284-800-6
Electronic_ISBN :
0743-1546
Type :
conf
DOI :
10.1109/CDC.2011.6160642
Filename :
6160642
Link To Document :
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