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Internet Auctions with Artificial Adaptive Agents

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  • M. Utku Unver

Abstract

The growing number of auction sites on the internet enable game theorists to ask strategic questions on rationality of the observed bidding behavior. The most popular of them (eBay, Amazon.com, AuctionWatch.com and Yahoo!, etc.) operate under similar sets of rules with seemingly small differences. They implement types of ascending-bid auctions and can strategically differ from a single-round sealed-bid second-price auction.\t The major difference between auction formats is about their ending procedures (as described in the paper). Most of the bidding activity is observed in the final hour of the auctions. Hence, the seemingly small difference in auction ending can cause later bids in the eBay auctions than Amazon.com. In this paper, I try to investigate the evolution of bidding patterns in internet auctions. I investigate evolutionary stability of late and multiple bidding in the private-value and common-value frameworks. I use adaptive artificial agent markets in the analysis. I seek similarities between the simulation data and actual human bidding behavior. I implement discrete finite time, sequential and repeated auctions in our simulations. In this paper, I show that common- and private-value auctions can evolutionarily lead to multiple and late bidding. I observe that artificial agent late bidding is in much more frequency in eBay auctions than Amazon.com in the private-value format (i.e. computer auctions). With common values (i.e. antique auctions), expert artificial bidders bid later more frequently than naive ones. The eBay auctions stage more frequent late bidding in the eBay auctions than in Amazon.com when there is a single naive bidder. Otherwise, auctions in Amazon.com cause more late bidding. I observe that eBay auctions generate in general less average revenue for sellers. They also cause more average profit for bidders in the private-value model. For the common-value model, bidders are indifferent betIen the two formats: each can dominate the other for different number of bidders. I also test the robustness of the results under different sets of parameters.\t

Suggested Citation

  • M. Utku Unver, 2001. "Internet Auctions with Artificial Adaptive Agents," Computing in Economics and Finance 2001 38, Society for Computational Economics.
  • Handle: RePEc:sce:scecf1:38
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    References listed on IDEAS

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    1. Haruvy, Ernan & Roth, Alvin E. & Unver, M. Utku, 2006. "The dynamics of law clerk matching: An experimental and computational investigation of proposals for reform of the market," Journal of Economic Dynamics and Control, Elsevier, vol. 30(3), pages 457-486, March.
    2. Bullard, James & Duffy, John, 1999. "Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs," Computational Economics, Springer;Society for Computational Economics, vol. 13(1), pages 41-60, February.
    3. Andreoni James & Miller John H., 1995. "Auctions with Artificial Adaptive Agents," Games and Economic Behavior, Elsevier, vol. 10(1), pages 39-64, July.
    4. Jim Engle-Warnick & Robert Slonim, 2006. "Inferring repeated-game strategies from actions: evidence from trust game experiments," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 28(3), pages 603-632, August.
    5. M. Utku Ünver, 2005. "On the survival of some unstable two-sided matching mechanisms," International Journal of Game Theory, Springer;Game Theory Society, vol. 33(2), pages 239-254, June.
    6. Miller, John H., 1996. "The coevolution of automata in the repeated Prisoner's Dilemma," Journal of Economic Behavior & Organization, Elsevier, vol. 29(1), pages 87-112, January.
    7. McAfee, R Preston & McMillan, John, 1987. "Auctions and Bidding," Journal of Economic Literature, American Economic Association, vol. 25(2), pages 699-738, June.
    8. Ariel Rubinstein, 1997. "Modeling Bounded Rationality," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262681005, May.
    9. Alvin E. Roth & Axel Ockenfels, 2002. "Last-Minute Bidding and the Rules for Ending Second-Price Auctions: Evidence from eBay and Amazon Auctions on the Internet," American Economic Review, American Economic Association, vol. 92(4), pages 1093-1103, September.
    10. Bullard, James & Duffy, John, 1998. "A model of learning and emulation with artificial adaptive agents," Journal of Economic Dynamics and Control, Elsevier, vol. 22(2), pages 179-207, February.
    11. Patrick Bajari & Ali Hortacsu, 2000. "Winner's Curse, Reserve Prices and Endogenous Entry: Empirical Insights from eBay Auctions," Econometric Society World Congress 2000 Contributed Papers 1927, Econometric Society.
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    Cited by:

    1. Axel Ockenfels, 2002. "New Institutional Structures on the Internet: The Economic Design of Online Auctions," Papers on Strategic Interaction 2002-08, Max Planck Institute of Economics, Strategic Interaction Group.

    More about this item

    Keywords

    Second-Price Auctions; Internet Auctions; Artificial Adaptive Agents;

    JEL classification:

    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C99 - Mathematical and Quantitative Methods - - Design of Experiments - - - Other

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