10 citations · 14 across the 10 of their papers we have counts for
6 papers · 1 filter
Gradient Dynamics in First-Price Auctions: Iterative Strategy Elimination via Cubic Potentials
Mete Şeref 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…
Persuading a Behavioral Agent: Approximately Best Responding and Learning
Yiling Chen, Tao Lin
The classic Bayesian persuasion model assumes a Bayesian and best-responding receiver. We study a relaxation of the Bayesian persuasion model where the receiver can approximately b…
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…
From Monopoly to Competition: Optimal Contests Prevail
Xiaotie Deng, Yotam Gafni, Ron Lavi +2
We study competition among contests in a general model that allows for an arbitrary and heterogeneous space of contest design, where the goal of the contest designers is to maximiz…
A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling
Xiaotie Deng, Ron Lavi, Tao Lin +3
The Empirical Revenue Maximization (ERM) is one of the most important price learning algorithms in auction design: as the literature shows it can learn approximately optimal reserv…
Learning Utilities and Equilibria in Non-Truthful Auctions
Hu Fu, Tao Lin
In non-truthful auctions, agents' utility for a strategy depends on the strategies of the opponents and also the prior distribution over their private types; the set of Bayes Nash…