36 Pages Posted: 30 Aug 2006
Date Written: 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.
Keywords: auctions, artificial agent simulations, genetic algorithm, finite automata
JEL Classification: D44, D83, C63, C99
Suggested Citation: 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