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 -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}
}