Game Theory

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21 papers in the last four weeks, up 200% on the four weeks before. 0.2% of all new papers.

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Learning in Structured Stackelberg Games
Apr 11, 2025Maria-Florina Balcan, Kiriaki Fragkia, Keegan HarrisGame Theory Stackelberg Games
Carnegie Mellon University, Pittsburgh, USA · University of California, Berkeley, USA
Nonlinear Equilibrium Transitions in a Potential Game Model for Federated Learning
Nov 18, 2024Kang Liu, Ziqi Wang, Enrique ZuazuaNash Equilibrium Game Theory
Institut de Mathématiques de Bourgogne, Université Bourgogne Europe, CNRS, Dijon, 21000, France · Chair for Dynamics, Control, Machine Learning and Numerics – Alexander von Humboldt Professorship, Department of Mathematics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstrasse 11, Erlangen, 91058, Germany · Chair of Computational Mathematics, Fundación Deusto, Avenida de las Universidades, 24, Bilbao, 48007, Basque Country, Spain +1
Decentralized Best-Response-Based Learning in Two-Player Zero-Sum Stochastic Games: A Finite-Sample Analysis
Sep 2, 2024Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar +2Nash Equilibrium Game Theory
IE, Purdue University. · ECE, University of Maryland, College Park. · CMS, Caltech. +1
Neural Dynamic Data Valuation via Stochastic State-Adjoint Trajectories
Apr 30, 2024Zhangyong Liang, Ji Zhang, Huanhuan GaoCooperative Game Theory Game Theory
National Center for Applied Mathematics Tianjin University Tianjin 300072, China · School of Mathematics, Physics and Computing University of Southern Queensland Toowoomba, Queensland 4350, Australia · School of Mechanical and Aerospace Engineering Jilin University Changchun 130025, China
Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents
Jun 5, 2023Nika Haghtalab, Chara Podimata, Kunhe YangImperfect-Information Games Game Theory
University of California, Berkeley · Massachusetts Institute of Technology
Observable Perfect Equilibrium
Oct 29, 2022Sam GanzfriedNash Equilibrium Imperfect-Information Games
Ganzfried Research
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics
Date pendingPhilip Jordan, Maryam KamgarpourNash Equilibrium Reinforcement Learning
SYCAMORE, EPFL
Strategic Type Spaces
Date pendingOlivier Gossner, Rafael VeielImperfect-Information Games Game Theory
CNRS–École Polytechnique and London School of Economics · University of Texas at Austin
Stability and Convergence of Optimistic Exponential Weights with Asymmetric Step Sizes in Bimatrix Games
Date pendingHédi Hadiji, Sarah SachsNash Equilibrium Last-Iterate Convergence
Laboratoire des Signaux et Systèmes, CentraleSupélec, Paris, France · School of Mathematics, University of Bristol, Bristol, United Kingdom

Apr 11, 2025, cs.GTJ/K move · Enter open · S save

Learning in Structured Stackelberg Games

Maria-Florina Balcan, Kiriaki Fragkia, Keegan Harris

Carnegie Mellon University, Pittsburgh, USA · University of California, Berkeley, USA

Game Theory Stackelberg Games Statistical Learning Theory Online Learning

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We initiate the study of structured Stackelberg games, a novel form of strategic interaction between a leader and a follower where contextual information can be predictive of the follower's (unknown) type. Motivated by applications such as security games and AI safety, we show how this additional structure can help the leader learn a utility-maximizing policy in both the online and distributional settings. In the online setting, we first prove that standard learning-theoretic measures of complexity do not characterize the difficulty of the leader's learning task. Notably, we find that there exists a learning-theoretic measure of complexity, analogous to the Littlestone dimension in online classification, that tightly characterizes the leader's instance-optimal regret. We term this the Stackelberg-Littlestone dimension, and leverage it to provide a provably optimal online learning algorithm. In the distributional setting, we provide analogous results by showing that two new dimensions control the sample complexity upper- and lower-bound.

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