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arXiv · 2606.05108

Gradient Dynamics in First-Price Auctions: Iterative Strategy Elimination via Cubic Potentials

Abstract

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 efficient outcome of the second-price auction. Our proof rests on two novel innovations in the analysis of online gradient ascent in normal-form games, which may be useful in a wider range of applications. First, we develop a potential-function-based argument for the analysis of gradient ascent in normal-form games, allowing us to deduce that certain strategies will not be played in time-average. We provide sufficient conditions which ensure this argument can be applied iteratively, resulting in a procedure reminiscent of iterative elimination of dominated strategies. Second, we develop a novel class of cubic "candidate potential functions", classifying a family of quadratic strategy modifications on the probability simplex against which online gradient ascent incurs no regret.

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BibTeXRIS

Mete Şeref Ahunbay, Weiqiang Zheng, Tao Lin. 2026-06-03. Gradient Dynamics in First-Price Auctions: Iterative Strategy Elimination via Cubic Potentials. https://arxiv.org/abs/2606.05108

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