Internet Auctions with Artificial Adaptive Agents: A Study on Market Design
John Duffy () and
Utku Unver ()
Computational Economics from University Library of Munich, Germany
Many internet auction sites implement ascending-bid, second-price auctions. Empirically, lastminute or “late” bidding is frequently observed in “hard-close” but not in “soft-close” versions of these auctions. In this paper, we introduce an independent private-value repeated internet auction model to explain this observed difference in bidding behavior. We use finite automata to model the repeated auction strategies. We report results from simulations involving populations of artificial bidders who update their strategies via a genetic algorithm. We show that our model can deliver late or early bidding behavior, depending on the auction closing rule in accordance with the empirical evidence. As an interesting result, we observe that hard-close auctions raise less revenue than soft-close auctions. We also investigate interesting properties of the evolving strategies and arrive at some conclusions regarding both auction designs from a market design point of view.
JEL-codes: C8 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-exp and nep-gth
Date: 2005-10-01, Revised 2005-10-07
Note: Type of Document - pdf
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Journal Article: Internet auctions with artificial adaptive agents: A study on market design (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpco:0510001
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