Certifying Concavity and Monotonicity in Games via Sum-of-Squares Hierarchies
Abstract
Concavity and its refinements underpin tractability in multiplayer games, where players independently choose actions to maximize their own payoffs which depend on other players’ actions. In *concave* games, where players' strategy sets are compact and convex, and their payoffs are concave in their own actions, strong guarantees follow: Nash equilibria always exist and decentralized algorithms converge to equilibria. If the game is furthermore *monotone*, an even stronger guarantee holds: Nash equilibria are unique under strictness assumptions. Unfortunately, we show that *certifying* concavity or monotonicity is NP-hard, already for games where utilities are multivariate polynomials and compact, convex basic semialgebraic strategy sets—an expressive class that captures extensive-form games with imperfect recall. On the positive side, we develop two hierarchies of sum-of-squares programs that certify concavity and monotonicity of a given game, and each level of the hierarchies can be solved in polynomial time. We show that almost all concave/monotone games are certified at some finite level of the hierarchies. Subsequently, we introduce the classes of SOS-concave/monotone games, which globally approximate concave/monotone games, and show that for any given game we can compute the closest SOS-concave/monotone game in polynomial time. Finally, we apply our techniques to canonical examples of extensive-form games with imperfect recall.
Cite
Text
Leon et al. "Certifying Concavity and Monotonicity in Games via Sum-of-Squares Hierarchies." Advances in Neural Information Processing Systems, 2025.Markdown
[Leon et al. "Certifying Concavity and Monotonicity in Games via Sum-of-Squares Hierarchies." Advances in Neural Information Processing Systems, 2025.](https://mlanthology.org/neurips/2025/leon2025neurips-certifying/)BibTeX
@inproceedings{leon2025neurips-certifying,
title = {{Certifying Concavity and Monotonicity in Games via Sum-of-Squares Hierarchies}},
author = {Leon, Vincent and Sakos, Iosif and Sim, Ryann and Varvitsiotis, Antonios},
booktitle = {Advances in Neural Information Processing Systems},
year = {2025},
url = {https://mlanthology.org/neurips/2025/leon2025neurips-certifying/}
}