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Hidden monotonicity and canonical transformations for mean field games and master equations

Mohit Bansil, Alpár R. Mészáros

TL;DR

The paper introduces a finite‑dimensional canonical transformation approach for mean field game master equations, establishing that a one‑parameter family of transformed data $(H_\alpha,G_\alpha)$ preserves solvability and yields a controllable shift in solutions. A central equivalence shows that global well‑posedness for $(H,G)$ is equivalent to well‑posedness for $(H_\alpha,G_\alpha)$, enabling a displacement‑monotonicity based route to existence and uniqueness. The authors derive explicit conditions on $H$ that guarantee $H_\alpha$ is displacement monotone, and show that for large $\alpha$ one can regularize the problem via $\tilde H=H+\alpha p\cdot x$, achieving global well‑posedness independent of the time horizon and even in degenerate noise scenarios. The framework unifies and extends recent results on displacement semi‑monotone and anti‑monotone data, showing these cases as corollaries and special instances of the transformation theory. Overall, the work provides a geometric, transformation‑driven perspective that broadens the admissible data classes for the master equation in mean field games and clarifies how monotonicity structures contribute to global solvability.

Abstract

In this paper we unveil novel monotonicity conditions applicable for Mean Field Games through the exploration of finite dimensional $canonical\ transformations$. Our findings contribute to establishing new global well-posedness results for the associated master equations, also in the case of potentially degenerate idiosyncratic noise. Additionally, we show that recent advancements in global well-posedness results, specifically those related to displacement semi-monotone and anti-monotone data, can be easily obtained as a consequence of our main results.

Hidden monotonicity and canonical transformations for mean field games and master equations

TL;DR

The paper introduces a finite‑dimensional canonical transformation approach for mean field game master equations, establishing that a one‑parameter family of transformed data preserves solvability and yields a controllable shift in solutions. A central equivalence shows that global well‑posedness for is equivalent to well‑posedness for , enabling a displacement‑monotonicity based route to existence and uniqueness. The authors derive explicit conditions on that guarantee is displacement monotone, and show that for large one can regularize the problem via , achieving global well‑posedness independent of the time horizon and even in degenerate noise scenarios. The framework unifies and extends recent results on displacement semi‑monotone and anti‑monotone data, showing these cases as corollaries and special instances of the transformation theory. Overall, the work provides a geometric, transformation‑driven perspective that broadens the admissible data classes for the master equation in mean field games and clarifies how monotonicity structures contribute to global solvability.

Abstract

In this paper we unveil novel monotonicity conditions applicable for Mean Field Games through the exploration of finite dimensional . Our findings contribute to establishing new global well-posedness results for the associated master equations, also in the case of potentially degenerate idiosyncratic noise. Additionally, we show that recent advancements in global well-posedness results, specifically those related to displacement semi-monotone and anti-monotone data, can be easily obtained as a consequence of our main results.
Paper Structure (4 sections, 9 theorems, 48 equations)

This paper contains 4 sections, 9 theorems, 48 equations.

Key Result

Theorem 1.1

Suppose that $H:\mathbb{R}^d\times{\mathscr P}_2(\mathbb{R}^d)\times\mathbb{R}^d\to\mathbb{R}$ is twice continuously differentiable with uniformly bounded second order derivatives. Suppose moreover that $H$ is strongly convex in the $p$-variable. Suppose that the symmetric part of $\partial_{xp}H$ i

Theorems & Definitions (30)

  • Theorem 1.1
  • Corollary 1.2
  • Remark 1.3
  • Remark 1.4
  • Remark 1.5
  • Definition 2.1
  • Definition 2.2
  • Remark 2.3
  • Definition 2.4
  • Remark 3.1
  • ...and 20 more