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.
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