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Learning to bid in sequential Dutch auctions

Author

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  • Guerci, E.
  • Kirman, A.
  • Moulet, S.

Abstract

We propose an agent-based computational model to investigate sequential Dutch auctions with particular emphasis on markets for perishable goods and we take as an example wholesale fish markets. Buyers in these markets sell the fish they purchase on a retail market. The paper provides an original model of boundedly rational behavior for wholesale buyers׳ behavior incorporating learning to improve profits, conjectures as to the bids that will be made and fictitious learning. We analyze the dynamics of the aggregate price under different market conditions in order to explain the emergence of market price patterns such as the well-known declining price paradox and the empirically observed fact that the very last transactions in the day may be at a higher price. The proposed behavioral model provides alternative explanations for market price dynamics to those which depend on standard hypotheses such as diminishing marginal profits. Furthermore, agents learn the option value of having the possibility of bidding in later rounds. When confronted with random buyers, such as occasional participants or new entrants, they learn to bid in the optimal way without being conscious of the strategies of the other buyers. When faced with other buyers who are also learning their behavior still displays some of the characteristics learned in the simpler case even though the problem is not analytically tractable.

Suggested Citation

  • Guerci, E. & Kirman, A. & Moulet, S., 2014. "Learning to bid in sequential Dutch auctions," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 374-393.
  • Handle: RePEc:eee:dyncon:v:48:y:2014:i:c:p:374-393
    DOI: 10.1016/j.jedc.2014.09.029
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    References listed on IDEAS

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    Cited by:

    1. Fuqiang Lu & Yanli Hu & Hualing Bi & Min Huang & Meng Zhao, 2018. "An Auction Approach for Cost and Schedule Management of IT Outsourcing Project," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 35(05), pages 1-23, October.
    2. Li, Zhen & Yue, Jinfeng & Kuo, Ching-Chung, 2018. "Design of discrete Dutch auctions with consideration of time," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1159-1171.
    3. Paul Pezanis-Christou & Hang Wu, 2018. "A non-game-theoretic approach to bidding in first-price and all-pay auctions," School of Economics Working Papers 2018-12, University of Adelaide, School of Economics.

    More about this item

    Keywords

    Multi-agent learning; Auction markets; Agent-based computational economics;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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