Discrete Rule Learning in First Price Auctions

Jason Shachat and Lijia Wei

Working Papers from Chapman University, Economic Science Institute

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.

Keywords: private value auction; discrete heterogeneity; learning; hidden Markov model; laboratory experiment (search for similar items in EconPapers)
JEL-codes: C15 C72 C92 D44 D87 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-des, nep-exp and nep-gth
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Persistent link: https://EconPapers.repec.org/RePEc:chu:wpaper:23-07

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