From Monopoly to Competition: Optimal Contests Prevail
Feb 1, 2023
Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions
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 focuses on repeated first-price auctions where bidders with fixed values 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 learning dynamics of mean-based algorithms, under two notions of convergence: (1) time-average: the fraction of rounds where bidders play a Nash equilibrium converges to 1; (2) last-iterate: the mixed strategy profile of bidders converges to a Nash equilibrium. Specifically, the results depend on the number of bidders with the highest value:
Apr 25, 2022
How Many Representatives Do We Need? The Optimal Size of a Congress Voting on Binary Issues
Feb 1, 2022
Learning Utilities and Equilibria in Non-Truthful Auctions
Dec 2, 2020
A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling
Dec 1, 2020
Private Data Manipulation in Optimal Sponsored Search Auction
May 1, 2020
An example conference paper
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Jul 1, 2013