Effect Evaluation of WTO Dispute Settlement Mechanism Based on Artificial Neural Network. (5th July 2022)

• Record Type: Journal Article
• Authors: Xu, Mengfan
• Other Names: Lakshmanna Kuruva Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2022(2022)
• Publication Date: 2022-07-05
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2022/5196698
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : In today's rapid economic development, trade exchanges between countries are increasingly close, and there are inevitable trade frictions that follow as a consequence. The World Trade Organization (WTO) dispute settlement mechanism (DSM) has become a predominant choice to deal with trade frictions for majority of the WTO member states. As part of the process of applying the Sino-US trade friction to the WTO DSM, the rise of US trade protectionism has caused serious damage to the WTO DSM. Also, the DSM similarly exposes its own inherent defects. Hence, the research on improvement of the WTO DSM under the background of trade friction has profound practical value. Based on this background, this paper proposes a framework for the effective evaluation of WTO DSM based on the artificial neural network. The contributions of the paper include the following: (1) Introduction of domestic and foreign scholars' research on different issues in the WTO DSM, wherein the retaliation mechanism is introduced in detail. Also, it is conducive for further deepening of the purpose and significance of the retaliation system in the WTO DSM. (2) The characteristics and structure of the CNN are introduced, and a suitable evaluation system for analyzing the impact of WTO DSM is constructed. Further, an evaluation model based on the CNN is proposed. (3) As per the dimension principle of CNN interlayer calculation, the network structure parameters are selected considering an optimal comprehensive evaluation index of the network. The results verify the effectiveness of the proposed method. Finally, when compared with the other state-of-the-art network models, it is found that CNN generates the highest evaluation accuracy.