English

A Unified Theory of $\theta$-Expectations

Probability 2025-12-02 v2 Artificial Intelligence Machine Learning Machine Learning

Abstract

We derive a new class of non-linear expectations from first-principles deterministic chaotic dynamics. The homogenization of the system's skew-adjoint microscopic generator is achieved using the spectral theory of transfer operators for uniformly hyperbolic flows. We prove convergence in the viscosity sense to a macroscopic evolution governed by a fully non-linear Hamilton-Jacobi-Bellman (HJB) equation. Our central result establishes that the HJB Hamiltonian possesses a rigid structure: affine in the Hessian but demonstrably non-convex in the gradient. This defines a new θ\theta-expectation and constructively establishes a class of non-convex stochastic control problems fundamentally outside the sub-additive framework of G-expectations.

Keywords

Cite

@article{arxiv.2507.20353,
  title  = {A Unified Theory of $\theta$-Expectations},
  author = {Qian Qi},
  journal= {arXiv preprint arXiv:2507.20353},
  year   = {2025}
}