Marie-Therese Wolfram (University of Warwick)

Title: From Inverse Optimal Transport to Global Trade
Abstract: Optimal transport theory provides a powerful mathematical and computational framework for problems in data science and economics. This talk explores two key contributions in this field: inferring unknown cost functions in noisy optimal transport plans and leveraging deep learning to infer trading barriers in international commodity trade.
We start by discussing the classic optimal transportation problems studied by Gaspard Monge and Leonid Kantorovich, before focusing on the respective inverse problem, so-called inverse optimal transport. Hereby we wish to infer the underlying transportation cost from solutions that are corrupted by noise. Then we generalize this approach to identify transport costs in global food and agricultural trade.  Our analysis reveals that the global South suffered disproportionately from the war in Ukraine's impact on wheat markets. Additionally, it examines the effects of free-trade agreements, trade disputes with China, and Brexit's impact on British-European trade, uncovering hidden patterns not evident from trade volumes alone.
(Host: Markus Bachmayr)