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.
Shachat, J., & Wei, L. (2023). Discrete rule learning in first price auctions. ESI Working Paper 23-07. https://digitalcommons.chapman.edu/esi_working_papers/387/