Internet Auctions with Artificial Adaptive Agents: A Study on Market Design
AbstractMany internet auction sites implement ascending-bid, second-price auctions. Empirically, lastminute or âlateâ bidding is frequently observed in âhard-closeâ but not in âsoft-closeâ 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.
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Bibliographic InfoPaper provided by University of Pittsburgh, Department of Economics in its series Working Papers with number 260.
Date of creation: Jan 2005
Date of revision: Jan 2005
Other versions of this item:
- Duffy, John & Ünver, M.Utku, 2008. "Internet auctions with artificial adaptive agents: A study on market design," Journal of Economic Behavior & Organization, Elsevier, vol. 67(2), pages 394-417, August.
- John Duffy & M. Utku Unver, 2005. "Internet Auctions with Artificial Adaptive Agents: A Study on Market Design," Computational Economics 0510001, EconWPA, revised 07 Oct 2005.
- C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-10-14 (All new papers)
- NEP-CMP-2006-10-14 (Computational Economics)
- NEP-FMK-2006-10-14 (Financial Markets)
- NEP-GTH-2006-10-14 (Game Theory)
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