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Learning to bid, but not to quit – Experience and Internet auctions

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  • Bramsen, Jens-Martin

Abstract

A classic argument in economics is that experience in the market place will eliminate mistakes and cognitive biases. Internet auctions are a popular market were some bidders gather extensive experience. In a unique data set from a Scandinavian auction site I question if and what bidders learn. At face value experienced bidders do adapt better bidding strategies. However, the so-called pseudo-endowment effect does not disappear. Regardless of their experience, bidders will be inclined to increase their willingness to pay as a response to having had “ownership” (the leading bid) before being outbid. Thus, this data can confirm that feedback, and especially negative feedback, seems to be a critical component in learning.

Suggested Citation

  • Bramsen, Jens-Martin, 2008. "Learning to bid, but not to quit – Experience and Internet auctions," MPRA Paper 14815, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:14815
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    References listed on IDEAS

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    1. Braga, Jacinto & Humphrey, Steven J. & Starmer, Chris, 2009. "Market experience eliminates some anomalies--and creates new ones," European Economic Review, Elsevier, vol. 53(4), pages 401-416, May.
    2. Rodney Garratt & John Wooders, 2010. "Efficiency in Second-Price Auctions: A New Look at Old Data," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 37(1), pages 43-50, August.
    3. Jeffrey A. Livingston, 2010. "The Behavior Of Inexperienced Bidders In Internet Auctions," Economic Inquiry, Western Economic Association International, vol. 48(2), pages 237-253, April.
    4. Patrick Bajari & Ali Hortaçsu, 2004. "Economic Insights from Internet Auctions," Journal of Economic Literature, American Economic Association, vol. 42(2), pages 457-486, June.
    5. Michael S. Haigh & John A. List, 2005. "Do Professional Traders Exhibit Myopic Loss Aversion? An Experimental Analysis," Journal of Finance, American Finance Association, vol. 60(1), pages 523-534, February.
    6. Graham Loomes & Chris Starmer & Robert Sugden, 2003. "Do Anomalies Disappear in Repeated Markets?," Economic Journal, Royal Economic Society, vol. 113(486), pages 153-166, March.
    7. Ockenfels, Axel & Roth, Alvin E., 2006. "Late and multiple bidding in second price Internet auctions: Theory and evidence concerning different rules for ending an auction," Games and Economic Behavior, Elsevier, vol. 55(2), pages 297-320, May.
    8. Kagel, John H & Levin, Dan, 1993. "Independent Private Value Auctions: Bidder Behaviour in First-, Second- and Third-Price Auctions with Varying Numbers of Bidders," Economic Journal, Royal Economic Society, vol. 103(419), pages 868-879, July.
    9. John A. List, 2003. "Does Market Experience Eliminate Market Anomalies?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(1), pages 41-71.
    10. Bramsen, Jens-Martin, 2008. "A pseudo-endowment effect in internet auctions," MPRA Paper 14813, University Library of Munich, Germany.
    11. Axel Ockenfels & Alvin E. Roth, 2001. "The Timing of Bids in Internet Auctions: Market Design, Bidder Behavior, and Artificial Agents," Papers on Strategic Interaction 2002-33, Max Planck Institute of Economics, Strategic Interaction Group.
    12. John A. List, 2004. "Neoclassical Theory Versus Prospect Theory: Evidence from the Marketplace," Econometrica, Econometric Society, vol. 72(2), pages 615-625, March.
    13. Bramsen, Jens-Martin, 2008. "Bid early and get it cheap - Timing effects in Internet auctions," MPRA Paper 14811, University Library of Munich, Germany.
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    Keywords

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    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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