Distorted optimal transport

TL;DR

This paper introduces distorted optimal transport, a new framework that minimizes a non-linear expected cost, with applications in decision theory and risk management, and characterizes optimal plans under various distortion functions.

Contribution

It formulates a novel distorted optimal transport problem, analyzes optimal couplings under different distortions, and establishes duality and uniqueness results.

Findings

Comonotonic coupling is optimal for convex distortions with submodular costs.

For inverse-S-shaped distortions, the optimal coupling has a 'first comonotonic, then counter-monotonic' structure.

The framework links probability, decision theory, and risk management through new mathematical insights.

Abstract

Classic optimal transport theory is formulated through minimizing the expected transport cost between two given distributions. We propose the framework of distorted optimal transport by minimizing a distorted expected cost, which is the cost under a non-linear expectation. This new formulation is motivated by concrete problems in decision theory, robust optimization, and risk management, and it has many distinct features compared to the classic theory. We choose simple cost functions and study different distortion functions and their implications on the optimal transport plan. We show that on the real line, the comonotonic coupling is optimal for the distorted optimal transport problem when the distortion function is convex and the cost function is submodular and monotone. Some forms of duality and uniqueness results are provided. For inverse-S-shaped distortion functions and linear cost, we obtain the unique form of optimal coupling for all marginal distributions, which turns out to have an interesting ``first comonotonic, then counter-monotonic" dependence structure; for S-shaped distortion functions a similar structure is obtained. Our results highlight several challenges and features in distorted optimal transport, offering a new mathematical bridge between the fields of probability, decision theory, and risk management.