一种用于国际石油贸易网络的可解释机器学习模型
An interpretable machine-learned model for international oil trade network
AI总结：
本研究融合机器学习、博弈论与效用理论构建可解释的iOTN决策模型，验证有效性后开展政策模拟，发现出口导向型经济体受外部冲击更脆弱，贸易摩擦成本影响不对称且国际组织间差异显著。
摘要：
能源安全与能源贸易是全球经济社会发展的基石。国际石油贸易网络（iOTN）的结构稳健性对全球经济具有重要作用。我们融合机器学习优化算法、博弈论与效用理论，学习一种包含各经济体在国际石油贸易中的收益禀赋与成本禀赋的石油贸易决策模型。我们较好地重构了iOTN的网络度、聚类系数与接近中心性，以验证模型的有效性。最后，在更贴近现实的环境中开展了基于博弈论和基于主体的模型（agent-based model）的政策模拟。我们发现，在受到外部冲击后，出口导向型经济体比进口导向型经济体更易受影响。此外，贸易摩擦成本的上升与下降对国际石油贸易的影响具有非对称性，且不同国际组织间存在显著差异。
英文摘要：
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 which 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 the export-oriented economies are more vulnerable to be 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.
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