文献类型：期刊文献

英文题名：An interpretable machine-learned model for international oil trade network

作者：Xie, Wen-Jie[1];Wei, Na;Zhou, Wei-Xing[1,2,3,4]

机构：[1]East China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Res Ctr Econophys, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Dept Math, Shanghai 200237, Peoples R China;[4]East China Univ Sci & Technol, Sch Business, 130 Meilong Rd,POB 114, Shanghai 200237, Peoples R China

年份：2023

卷号：82

外文期刊名：RESOURCES POLICY

收录：;EI(收录号：20231413841644);WOS:【SSCI(收录号:WOS:000973650400001)】；

语种：英文

外文关键词：Global oil market; Oil trade network; Machine learning; Policy simulation

摘要：Energy security and energy trade are the cornerstones of global economic and social development. The structural robustness of the international oil trade network (iOTN) plays an important role in the global economy. We integrate the machine learning optimization algorithm, game theory, and utility theory for learning an oil trade decision-making model that contains the benefit endowment and cost endowment of economies in international oil trades. We have reconstructed the network degree, clustering coefficient, and closeness of the iOTN well to verify the effectiveness of the model. In the end, policy simulations based on game theory and agent-based model are carried out in a more realistic environment. We find that export -oriented economies are more vulnerable to being affected than import-oriented economies after receiving external shocks. Moreover, the impact of the increase and decrease of trade friction costs on the international oil trade is asymmetrical, and there are significant differences between international organizations.