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Internet Auctions With Artificial Adaptive Agents: A Study on Market Design

John Duffy

University of California, Irvine

M. Utku Ünver

Boston College - Department of Economics

February 10, 2007

Many internet auction sites implement ascending-bid, second-price auctions. Empirically, last minute 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. Among other findings, 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.

Number of Pages in PDF File: 36

Keywords: auctions, artificial agent simulations, genetic algorithm, finite automata

JEL Classification: D44, D83, C63, C99

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Date posted: August 30, 2006  

Suggested Citation

Duffy, John and Ünver, M. Utku, Internet Auctions With Artificial Adaptive Agents: A Study on Market Design (February 10, 2007). Available at SSRN: https://ssrn.com/abstract=926913 or http://dx.doi.org/10.2139/ssrn.926913

Contact Information

John Duffy
University of California, Irvine ( email )
Department of Economics
3151 Social Science Plaza
Irvine, CA 92697
United States
949-824-8341 (Phone)
Utku Unver (Contact Author)
Boston College - Department of Economics ( email )
140 Commonwealth Ave.
Chestnut Hill, MA 02467
United States
+1 (617) 552 2217 (Phone)
+1 (617) 552 2318 (Fax)
HOME PAGE: http://www2.bc.edu/~unver
Feedback to SSRN

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