Finite composite games: Equilibria and dynamics
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Abstract (arXiv)
We study games with finitely many participants, each having finitely many choices. We consider the following categories of participants: (I) populations: sets of nonatomic agents, (II) atomic splittable players, (III) atomic non splittable players. We recall and compare the basic properties, expressed through variational inequalities, concerning equilibria, potential games and dissipative games, as well as evolutionary dynamics. Then we consider composite games where the three categories of participants are present, a typical example being congestion games, and extend the previous properties of equilibria and dynamics. Finally we describe an instance of composite potential game.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Mar 2015.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 3; 2024: 1; 2025: 1.
8 papers.
- 2021Doubly Optimal No-Regret Online Learning in Strongly Monotone Games with Bandit Feedbackcited by 14
- 2021Learning in nonatomic games, Part I: Finite action spaces and population gamescited by 6
- 2023Asymptotic Convergence and Performance of Multi-Agent Q-Learning Dynamicscited by 6
- 2023Adaptive, Doubly Optimal No-Regret Learning in Strongly Monotone and Exp-Concave Games with Gradient Feedbackcited by 6
- 2021Second-Order Mirror Descent: Convergence in Games Beyond Averaging and Discountingcited by 1
- 2025Convergence and Connectivity: Dynamics of Multi-Agent Q-Learning in Random Networkscited by 1
- 2023Stability of Multi-Agent Learning: Convergence in Network Games with Many Playerscited by 0
- 2024Nested replicator dynamics, nested logit choice, and similarity-based learningcited by 0
- 2025Convergence and Connectivity: Dynamics of Multi-Agent Q-Learning in Random Networks13 Mar 2025
- 2024Nested replicator dynamics, nested logit choice, and similarity-based learning25 Jul 2024
- 2023Adaptive, Doubly Optimal No-Regret Learning in Strongly Monotone and Exp-Concave Games with Gradient Feedback21 Oct 2023
- 2023Stability of Multi-Agent Learning: Convergence in Network Games with Many Players26 Jul 2023
- 2023Asymptotic Convergence and Performance of Multi-Agent Q-Learning Dynamics23 Jan 2023
- 2021Doubly Optimal No-Regret Online Learning in Strongly Monotone Games with Bandit Feedback6 Dec 2021
- 2021Second-Order Mirror Descent: Convergence in Games Beyond Averaging and Discounting18 Nov 2021
- 2021Learning in nonatomic games, Part I: Finite action spaces and population games4 Jul 2021