Tootfinder

Aggregate Fictitious Play for Learning in Anonymous Polymatrix Games (Extended Version)
Semih Kara, Tamer Ba\c{s}ar
https://arxiv.org/abs/2508.19371 https://

Aggregate Fictitious Play for Learning in Anonymous Polymatrix Games (Extended Version)
Fictitious play (FP) is a well-studied algorithm that enables agents to learn Nash equilibrium in games with certain reward structures. However, when agents have no prior knowledge of the reward functions, FP faces a major challenge: the joint action space grows exponentially with the number of agents, which slows down reward exploration. Anonymous games offer a structure that mitigates this issue. In these games, the rewards depend only on the actions taken; not on who is taking which action. …

Asymmetric Network Games: $\alpha$-Potential Function and Learning
Kiran Rokade, Adit Jain, Francesca Parise, Vikram Krishnamurthy, Eva Tardos
https://arxiv.org/abs/2508.06619 h…

Asymmetric Network Games: $α$-Potential Function and Learning
In a network game, players interact over a network and the utility of each player depends on his own action and on an aggregate of his neighbours' actions. Many real world networks of interest are asymmetric and involve a large number of heterogeneous players. This paper analyzes static network games using the framework of $α$-potential games. Under mild assumptions on the action sets (compact intervals) and the utility functions (twice continuously differentiable) of the players, we derive an…