Speaker
Description
In this talk I will focus on two challenging problems in applied optimal transport: 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 he 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.
