经济学中的最优传输
Optimal Transport in Economics
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中文总结 AI 辅助
本文综述最优传输在经济学中的核心理论、发展历程及其在匹配、均衡、分布耦合等领域的双重作用，并探讨未来扩展方向。
中文摘要 AI 辅助
最优传输为配置、均衡、计算和推断提供了共同语言。其原始问题分配质量或主体，而其对偶变量则具有作为效用、价格和稀缺租金的经济学解释。本综述解释了为何这种结合在经济学中被证明异常有效。我们首先介绍核心结果，包括Kantorovich对偶性、整数性、循环单调性、规范距离与二次成本，以及熵正则化。然后，我们追溯该领域从规划与运筹学到现代分析、统计学和计算的发展历程。经济学文献围绕传输的两个作用进行组织：一是作为匹配、贸易、特征均衡和总体配置的模型；二是作为耦合分布、衡量差异、构建多元秩、求解逆问题以及验证经济结论的工具。最后，我们考察了超越可转移效用、一对一匹配、静态配置和无约束传输的扩展，以及涉及跨多个市场的学习和识别等开放问题。
英文摘要
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.
发表机构
- New York University（纽约大学）
机构由 AI 辅助整理，请以论文原文为准。
