Certifying Concavity and Monotonicity in Games via Sum-of-Squares Hierarchies

Vincent Leon, Iosif Sakos, Ryann Sim, Antonios Varvitsiotis
12/11/2025
cs.GTcs.MAmath.OC

No official code found. Best implementation: ryanndelion/sos-concave-monotone-games (0 stars, MIT, last commit 2025-09-23).

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 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 imperfect recall extensive-form games.

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Cite this paper

@article{leon2025certifying,
  title  = {Certifying Concavity and Monotonicity in Games via Sum-of-Squares Hierarchies},
  author = {Vincent Leon and Iosif Sakos and Ryann Sim and Antonios Varvitsiotis},
  year   = {2025},
  eprint = {2512.10292},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2512.10292v1},
  journal = {NEURIPS 2025 2025}
}

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