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In many settings where multiple agents interact, the optimal choices for each agent depend heavily on the choices of the others. These coupled interactions are well-described by a general-sum differential game, in which players have…

Robotics · Computer Science 2020-05-07 Lasse Peters , David Fridovich-Keil , Claire J. Tomlin , Zachary N. Sunberg

In many game-theoretic settings, agents are challenged with taking decisions against the uncertain behavior exhibited by others. Often, this uncertainty arises from multiple sources, e.g., incomplete information, limited computation,…

Computer Science and Game Theory · Computer Science 2025-07-22 Nicolas Lanzetti , Sylvain Fricker , Saverio Bolognani , Florian Dörfler , Dario Paccagnan

We evaluate the best-response (BR) algorithm for lattice convex-quadratic games, where the players have nonlinear objectives and unbounded feasible sets. We provide a sufficient condition that if certain interaction matrices (the product of…

Computer Science and Game Theory · Computer Science 2025-03-31 Sriram Sankaranarayanan

In multi-agent reinforcement learning (MARL) and game theory, agents repeatedly interact and revise their strategies as new data arrives, producing a sequence of strategy profiles. This paper studies sequences of strategies satisfying a…

Computer Science and Game Theory · Computer Science 2024-10-02 Bora Yongacoglu , Gürdal Arslan , Lacra Pavel , Serdar Yüksel

The Team-maxmin equilibrium prescribes the optimal strategies for a team of rational players sharing the same goal and without the capability of correlating their strategies in strategic games against an adversary. This solution concept can…

Artificial Intelligence · Computer Science 2016-11-21 Nicola Basilico , Andrea Celli , Giuseppe De Nittis , Nicola Gatti

An analysis of several important aspects of competition or conflict in games, social choice and decision theory is presented. Inherent difficulties and complexities in cooperation are highlighted. These have over the years led to a certain…

Optimization and Control · Mathematics 2007-05-23 Elemér E Rosinger

We study the quality of outcomes in repeated games when the population of players is dynamically changing and participants use learning algorithms to adapt to the changing environment. Game theory classically considers Nash equilibria of…

Computer Science and Game Theory · Computer Science 2020-05-25 Thodoris Lykouris , Vasilis Syrgkanis , Eva Tardos

We study the complexity of computing equilibria in binary public goods games on undirected graphs. In such a game, players correspond to vertices in a graph and face a binary choice of performing an action, or not. Each player's decision…

Computer Science and Game Theory · Computer Science 2023-05-22 Max Klimm , Maximilian J. Stahlberg

We derive the rate of convergence to the strongly variationally stable Nash equilibrium in a convex game, for a zeroth-order learning algorithm. Though we do not assume strong monotonicity of the game, our rates for the one-point feedback…

Optimization and Control · Mathematics 2024-03-12 Tatiana Tatarenko , Maryam Kamgarpour

Best response (BR) dynamics is a natural method by which players proceed toward a pure Nash equilibrium via a local search method. The quality of the equilibrium reached may depend heavily on the order by which players are chosen to perform…

Computer Science and Game Theory · Computer Science 2020-02-27 Michal Feldman , Yuval Snappir , Tami Tamir

Game theory provides a well-established framework for the analysis of concurrent and multi-agent systems. The basic idea is that concurrent processes (agents) can be understood as corresponding to players in a game; plays represent the…

Logic in Computer Science · Computer Science 2023-06-22 Julian Gutierrez , Paul Harrenstein , Giuseppe Perelli , Michael Wooldridge

While fictitious play is guaranteed to converge to Nash equilibrium in certain game classes, such as two-player zero-sum games, it is not guaranteed to converge in non-zero-sum and multiplayer games. We show that fictitious play in fact…

Computer Science and Game Theory · Computer Science 2024-07-30 Sam Ganzfried

Imitating successful behavior is a natural and frequently applied approach to trust in when facing scenarios for which we have little or no experience upon which we can base our decision. In this paper, we consider such behavior in atomic…

Computer Science and Game Theory · Computer Science 2008-10-04 Heiner Ackermann , Petra Berenbrink , Simon Fischer , Martin Hoefer

We study the query complexity of approximate notions of Nash equilibrium in games with a large number of players $n$. Our main result states that for $n$-player binary-action games and for constant $\varepsilon$, the query complexity of an…

Computer Science and Game Theory · Computer Science 2014-07-21 Yakov Babichenko

Adversarial training, a special case of multi-objective optimization, is an increasingly prevalent machine learning technique: some of its most notable applications include GAN-based generative modeling and self-play techniques in…

Machine Learning · Statistics 2021-03-17 Gauthier Gidel , David Balduzzi , Wojciech Marian Czarnecki , Marta Garnelo , Yoram Bachrach

Dynamic games are powerful tools to model multi-agent decision-making, yet computing Nash (generalized Nash) equilibria remains a central challenge in such settings. Complexity arises from tightly coupled optimality conditions, nested…

Computer Science and Game Theory · Computer Science 2026-02-06 Mahdis Rabbani , Navid Mojahed , Shima Nazari

We construct a finite deterministic graphical (DG) game without Nash equilibria in pure stationary strategies. This game has 3 players $I=\{1,2,3\}$ and 5 outcomes: 2 terminal $a_1$ and $a_2$ and 3 cyclic. Furthermore, for 2 players a…

Combinatorics · Mathematics 2024-12-24 Bogdan Butyrin , Vladimir Gurvich , Anton Lutsenko , Mariya Naumova , Maxim Peskin

This paper addresses the problem of fair equilibrium selection in graphical games. Our approach is based on the data structure called the {\em best response policy}, which was proposed by Kearns et al. \cite{kls} as a way to represent all…

Computer Science and Game Theory · Computer Science 2007-05-23 Edith Elkind , Leslie Ann Goldberg , Paul W. Goldberg

A celebrated result in the interface of online learning and game theory guarantees that the repeated interaction of no-regret players leads to a coarse correlated equilibrium (CCE) -- a natural game-theoretic solution concept. Despite the…

Computer Science and Game Theory · Computer Science 2024-11-05 Ioannis Anagnostides , Alkis Kalavasis , Tuomas Sandholm

The theory of mean field games is a tool to understand noncooperative dynamic stochastic games with a large number of players. Much of the theory has evolved under conditions ensuring uniqueness of the mean field game Nash equilibrium.…

Optimization and Control · Mathematics 2019-03-19 Bruce Hajek , Michael Livesay