2 papers
cs.GT2026
Gradient Dynamics in First-Price Auctions: Iterative Strategy Elimination via Cubic Potentials
Mete Åeref Ahunbay, Weiqiang Zheng, Tao Lin
We show that in discretised first-price auctions with complete information, if the buyers learn to bid with online gradient ascent, in time-average the outcome is (almost) the effi…
cs.GT2025
Nash Convergence of Mean-Based Learning Algorithms in First-Price Auctions
Xiaotie Deng, Xinyan Hu, Tao Lin +1
The convergence properties of learning dynamics in repeated auctions is a timely and important question, with numerous applications in, e.g., online advertising markets. This work…