mlsanityData to 14 Oct 2026

A geometric decomposition of finite games: Convergence vs. recurrence under exponential weights

arXiv 2405.07224 ↗First posted 12 May 2024cs.GT cs.LG math.OCcited by 4

Not on the map. Why?

Abstract (arXiv)

In view of the complexity of the dynamics of learning in games, we seek to decompose a game into simpler components where the dynamics' long-run behavior is well understood. A natural starting point for this is Helmholtz's theorem, which decomposes a vector field into a potential and an incompressible component. However, the geometry of game dynamics - and, in particular, the dynamics of exponential / multiplicative weights (EW) schemes - is not compatible with the Euclidean underpinnings of Helmholtz's theorem. This leads us to consider a specific Riemannian framework based on the so-called Shahshahani metric, and introduce the class of incompressible games, for which we establish the following results: First, in addition to being volume-preserving, the continuous-time EW dynamics in incompressible games admit a constant of motion and are Poincar'e recurrent - i.e., almost every trajectory of play comes arbitrarily close to its starting point infinitely often. Second, we establish a deep connection with a well-known decomposition of games into a potential and harmonic component (where the players' objectives are aligned and anti-aligned respectively): a game is incompressible if and only if it is harmonic, implying in turn that the EW dynamics lead to Poincar'e recurrence in harmonic games.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 17 of its 85 references that are in the map. First posted on arXiv 12 May 2024.

Builds on

17 of 85 references.

References · 17

  1. 2013Optimization, Learning, and Games with Predictable Sequencescited by 188
  2. 2018Optimistic mirror descent in saddle-point problems: Going the extra (gradient) milecited by 136
  3. 2017Cycles in adversarial regularized learningcited by 124
  4. 2016Learning in games with continuous action sets and unknown payoff functionscited by 81
  5. 2021Adaptive Learning in Continuous Games: Optimal Regret Bounds and Convergence to Nash Equilibriumcited by 51
  6. 2020No-regret learning and mixed Nash equilibria: They do not mixcited by 50
  7. 2020Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scalingcited by 49
  8. 2016Riemannian game dynamicscited by 19
  9. 2022No-Regret Learning in Games with Noisy Feedback: Faster Rates and Adaptivity via Learning Rate Separationcited by 17
  10. 2014A continuous-time approach to online optimizationcited by 13
  11. 2020Chaos of Learning Beyond Zero-sum and Coordination via Game Decompositionscited by 9
  12. 2014On the robustness of learning in games with stochastically perturbed payoff observationscited by 7
5 more
  1. 2016Strategic Decompositions of Normal Form Games: Zero-sum Games and Potential Gamescited by 5
  2. 2019Decomposition of games: some strategic considerationscited by 5
  3. 2019From Darwin to Poincar'e and von Neumann: Recurrence and Cycles in Evolutionary and Algorithmic Game Theorycited by 5
  4. 2013Inertial game dynamics and applications to constrained optimizationcited by 2
  5. 2019A Note On Orthogonal Decomposition of Finite Gamescited by 2

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Citing papers per year: 2024: 3; 2025: 1.

4 papers.