Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions

We consider repeated first price auctions where each bidder, having a deterministic type, learns to bid using a mean-based learning algorithm. We completely characterize the Nash convergence property of the bidding dynamics in two senses: (1) time-average: the fraction of rounds where bidders play a Nash equilibrium approaches to 1 in the limit; (2) last-iterate: the mixed strategy profile of bidders approaches to a Nash equilibrium in the limit. Specifically, the results depend on the number of bidders with the highest value:-if the number is at least three, the bidding dynamics almost surely converges to a Nash equilibrium of the auction, both in time-average and in last-iterate. -if the number is two, the bidding dynamics almost surely converges to a Nash equilibrium in time-average but not necessarily in last-iterate. -if the number is one, the bidding dynamics may not converge to a Nash equilibrium in time-average nor in last-iterate. Our discovery opens up new possibilities in the study of convergence dynamics of learning algorithms.