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

36 Pages Posted: 30 Aug 2006

See all articles by John Duffy

John Duffy

University of California, Irvine

M. Utku Ünver

Boston College, Department of Economics

Date Written: February 10, 2007

Abstract

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.

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

JEL Classification: D44, D83, C63, C99

Suggested Citation

Duffy, John and Unver, 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

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 Avenue
Chestnut Hill, MA 02467
United States
6175640771 (Phone)
+1 (617) 552 2318 (Fax)

HOME PAGE: http://www2.bc.edu/~unver

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