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About this paper
Janik Bürgermeister, Julius Durmann, Martin Bichler, Mete Şeref Ahunbay, Bary Pradelski, Marco Scarsini
The paper characterizes outcomes of learning in discretized symmetric two-bidder first-price auctions through BSCCE, a set shaped by probability-preserving shifts associated with projected gradient updates. For the Beta(2,1) prior, its maximum squared Wasserstein-2 distance from the continuous Bayesian Nash equilibrium decreases as the grid is refined, while the broader BCCE bound remains high; contraction is numerical and not universal.
Summary by alphaXiv.
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