Optimal Transport in Economics

2026-09-05

AI Summary

该文综述了最优传输理论在经济学中的应用，通过Kantorovich对偶等方法解决资源配置、均衡及计算问题，并探讨其作为匹配模型和工具的角色。

Abstract

Optimal transport provides a common language for allocation, equilibrium, computation, and inference. Its primal problem assigns mass or agents, while its dual variables admit economic interpretations as utilities, prices, and scarcity rents. This review explains why this combination has proved unusually effective in economics. We first present the core results, including Kantorovich duality, integrality, cyclical monotonicity, the canonical distance and quadratic costs, and entropic regularization. We then trace the field's development from planning and operations research to modern analysis, statistics, and computation. The economic literature is organized around two roles for transport: as a model of matching, trade, hedonic equilibrium, and aggregate assignment; and as a tool for coupling distributions, measuring discrepancies, constructing multivariate ranks, solving inverse problems, and certifying economic conclusions. We conclude by examining extensions beyond transferable utility, one-to-one matching, static allocation, and unconstrained transport, together with open questions involving learning and identification across multiple markets.

Problem

• Optimal Transport
• Economics
• Matching
• Trade
• Hedonic Equilibrium

Innovation

• Optimal Transport
• Economic Applications
• Kantorovich Duality
• Entropic Regularization
• Market Equilibrium