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A recent body of experimental literature has studied empirical game-theoretical analysis, in which we have partial knowledge of a game, consisting of observations of a subset of the pure-strategy profiles and their associated payoffs to…

Computer Science and Game Theory · Computer Science 2014-02-13 John Fearnley , Martin Gairing , Paul Goldberg , Rahul Savani

This paper studies the distributed generalized Nash equilibrium seeking problem for aggregative games with coupling constraints, where each player optimizes its strategy depending on its local cost function and the estimated strategy…

Optimization and Control · Mathematics 2025-03-12 Wenqing Zhao , Antai Xie , Yuchi Wu , Xinlei Yi , Xiaoqiang Ren

We study a discrete-time finite-horizon two-players nonzero-sum stopping game where the filtration of Player 1 is richer than the filtration of Player 2. A major difficulty which is caused by the information asymmetry is that Player 2 may…

Optimization and Control · Mathematics 2022-09-29 Royi Jacobovic

We present a simple primal-dual algorithm for computing approximate Nash-equilibria in two-person zero-sum sequential games with incomplete information and perfect recall (like Texas Hold'em Poker). Our algorithm is numerically stable,…

Computer Science and Game Theory · Computer Science 2015-12-24 Elvis Dohmatob

We present a new model of incomplete information games without private information in which the players use a distributionally robust optimization approach to cope with the payoff uncertainty. With some specific restrictions, we show that…

Computer Science and Game Theory · Computer Science 2016-10-04 Nicolas Loizou

In this work, we study the system of interacting non-cooperative two Q-learning agents, where one agent has the privilege of observing the other's actions. We show that this information asymmetry can lead to a stable outcome of population…

Machine Learning · Computer Science 2021-01-26 Ezra Tampubolon , Haris Ceribasic , Holger Boche

We study Nash equilibrium learning in partially observable Markov games (POMGs), a multi-agent reinforcement learning framework in which agents cannot fully observe the underlying state. Prior work in this setting relies on centralization…

Computer Science and Game Theory · Computer Science 2026-05-08 Philip Jordan , Maryam Kamgarpour

Many real-world domains contain multiple agents behaving strategically with probabilistic transitions and uncertain (potentially infinite) duration. Such settings can be modeled as stochastic games. While algorithms have been developed for…

Computer Science and Game Theory · Computer Science 2020-06-25 Sam Ganzfried , Conner Laughlin , Charles Morefield

For any two-by-two game $\G$, we define a new two-player game $\G^Q$. The definition is motivated by a vision of players in game $\G$ communicating via quantum technology according to a certain standard protocol originally introduced by…

Optimization and Control · Mathematics 2011-10-07 Steven E. Landsburg

Considering infinite-horizon, discrete-time, linear quadratic, N-player dynamic games with scalar dynamics, a graphical representation of feedback Nash equilibrium solutions is provided. This representation is utilised to derive conditions…

Optimization and Control · Mathematics 2025-01-27 Benita Nortmann , Mario Sassano , Thulasi Mylvaganam

The distributed computation of a Nash equilibrium in aggregative games is gaining increased traction in recent years. Of particular interest is the mediator-free scenario where individual players only access or observe the decisions of…

Computer Science and Game Theory · Computer Science 2023-06-26 Yongqiang Wang , Angelia Nedich

We present multilinear and mixed-integer multilinear programs to find a Nash equilibrium in multi-player noncooperative games. We compare the formulations to common algorithms in Gambit, and conclude that a multilinear feasibility program…

Optimization and Control · Mathematics 2024-03-27 Miriam Fischer , Akshay Gupte

Claude Shannon's zero-error communication paradigm reshaped our understanding of fault-tolerant information transfer. Here, we adapt this notion into game theory with incomplete information. We ask: can players with private information…

We investigate mean-field games (MFG) in which agents can actively control their speed of access to information. Specifically, the agents can dynamically decide to obtain observations with reduced delay by accepting higher observation…

Optimization and Control · Mathematics 2025-06-03 Dirk Becherer , Christoph Reisinger , Jonathan Tam

Inspired by the path coordination problem arising from robo-taxis, warehouse management, and mixed-vehicle routing problems, we model a group of heterogeneous players responding to stochastic demands as a congestion game under Markov…

Multiagent Systems · Computer Science 2022-07-06 Sarah H. Q. Li , Dan Calderone , Behcet Acikmese

This paper is concerned with a linear-quadratic non-zero sum differential game with asymmetric delayed information. To be specific, two players exist time delays simultaneously which are different, leading the dynamical system being an…

Optimization and Control · Mathematics 2025-10-27 Yuxin Ye , Jingtao Shi

This paper explores distributed Nash equilibrium seeking problems for games in which the players have limited knowledge on other players' actions. In particular, the involved players are considered to be high-order integrators with their…

Optimization and Control · Mathematics 2021-08-17 Maojiao Ye , Lei Ding , Shengyuan Xu

In two player bi-matrix games with partial monitoring, actions played are not observed, only some messages are received. Those games satisfy a crucial property of usual bi-matrix games: there are only a finite number of required (mixed)…

Computer Science and Game Theory · Computer Science 2013-01-15 Vianney Perchet

Non-cooperative dynamic game theory provides a principled approach to modeling sequential decision-making among multiple noncommunicative agents. A key focus has been on finding Nash equilibria in two-agent zero-sum dynamic games under…

Computer Science and Game Theory · Computer Science 2025-03-20 Kushagra Gupta , Ross Allen , David Fridovich-Keil , Ufuk Topcu

One key in real-life Nash equilibrium applications is to calibrate players' cost functions. To leverage the approximation ability of neural networks, we proposed a general framework for optimizing and learning Nash equilibrium using neural…

Computer Science and Game Theory · Computer Science 2024-09-04 Di Zhang , Wei Gu , Qing Jin