Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions. (arXiv:2110.03906v2 [cs.GT] UPDATED)

Understanding the convergence properties of learning dynamics in repeated
auctions is a timely and important question in the area of learning in
auctions, with numerous applications in, e.g., online advertising markets. This
work focuses on repeated first price auctions where bidders with fixed values
for the item learn to bid using mean-based algorithms -- a large class of
online learning algorithms that include popular no-regret algorithms such as
Multiplicative Weights Update and Follow the Perturbed Leader. We completely
characterize the …

algorithms arxiv learning price