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Discrete Rule Learning in First Price Auctions

Author

Listed:
  • Jason Shachat

    (Durham University Business School)

  • Lijia Wei

    (School of Economics and Management, Wuhan University)

Abstract

We present a hidden Markov model of discrete strategic heterogeneity and learning in first price independent private values auctions. The model includes three latent bidding rules: constant absolute mark-up, constant percentage mark-up, and strategic best response. Rule switching probabilities depend upon a bidder's past auction outcomes and a myopic reinforcement learning dynamic. We apply this model to a new experiment that varies the number of bidders and the auction frame between forward and reverse. We find the proportion of bidders following constant absolute mark-up increases with experience, and is higher when the number of bidders is large. The primary driver here is subjects' increased propensity to switch strategies when they experience a loss (win) reinforcement when following a strategic (heuristic) rule.

Suggested Citation

  • Jason Shachat & Lijia Wei, 2023. "Discrete Rule Learning in First Price Auctions," Working Papers 23-07, Chapman University, Economic Science Institute.
    • Handle: RePEc:chu:wpaper:23-07
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    File URL: https://digitalcommons.chapman.edu/esi_working_papers/387/
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    More about this item

    Keywords

    private value auction; discrete heterogeneity; learning; hidden Markov model; laboratory experiment;
    All these keywords.

    JEL classification:

    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D87 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Neuroeconomics
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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