A learning approach to auctions
We analyze a repeated first-price auction in which the types of the players are determined before the first round. It is proved that if every player is using either a belief-based learning scheme with bounded recall or a generalized fictitious play learning scheme, then for sufficiently large time, the players' bids are in equilibrium in the one-shot auction in which the types are commonly known.
|Date of creation:||01 Jun 1997|
|Note:||Financial support from the Deutsche Forschungsgemeinschaft, SFB 504, at the University of Mannheim, is gratefully acknowledged.|
|Contact details of provider:|| Postal: D-68131 Mannheim|
Phone: (49) (0) 621-292-2547
Fax: (49) (0) 621-292-5594
Web page: http://www.sfb504.uni-mannheim.de/
More information through EDIRC
Web page: http://www.sfb504.uni-mannheim.de
|Order Information:|| Email: |
When requesting a correction, please mention this item's handle: RePEc:xrs:sfbmaa:97-11. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Carsten Schmidt)
If references are entirely missing, you can add them using this form.