Evolution game simulation study on Asia–Europe grain trade cooperation network

IF 1.5 4区 物理与天体物理 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS International Journal of Modern Physics C Pub Date : 2024-06-05 DOI:10.1142/s0129183124501559
Zhidong Xie, Jinlong Ma, Hongbin Zhang, Huawei Pang
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Abstract

There has unavoidably been a problem for international grain cooperation in the Asia–Europe region as a result of the Russian–Ukrainian conflict. Analysis of the impact of Russian–Ukrainian conflict on the evolution of grain trade cooperation, identifying key influencing factors and formulating corresponding measures are of practical value for safeguarding grain security in the Asia–Europe region. In this paper, a grain trade network is constructed according to grain trade data from the Asian–European grain trade cooperation countries. A grain trade network game (GTNG) model of grain trade cooperation is established in order to investigate the elements that influence trade cooperation. The reciprocal preference is introduced. Through simulation experiments, the effects of cooperation costs, neighboring countries’ incentives, and punitive pressures, the degree of goodwill perception, the types of game, and the proportion of initial cooperators on trade cooperation are examined. This study may give useful policy recommendations for promoting Asia–Europe grain trade cooperation under the influence of the Russian–Ukrainian conflict.

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亚欧粮食贸易合作网络的演化博弈模拟研究
俄乌冲突给亚欧地区的国际粮食合作带来了不可避免的问题。分析俄乌冲突对粮食贸易合作演变的影响,找出主要影响因素并制定相应措施,对保障亚欧地区粮食安全具有现实价值。本文根据亚欧粮食贸易合作国的粮食贸易数据,构建了一个粮食贸易网络。建立了粮食贸易合作网络博弈(GTNG)模型,以研究影响贸易合作的要素。引入了互惠偏好。通过模拟实验,考察了合作成本、邻国激励和惩罚压力、善意认知程度、博弈类型和初始合作者比例对贸易合作的影响。本研究可为在俄乌冲突影响下促进亚欧粮食贸易合作提供有益的政策建议。
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来源期刊
International Journal of Modern Physics C
International Journal of Modern Physics C 物理-计算机:跨学科应用
CiteScore
3.00
自引率
15.80%
发文量
158
审稿时长
4 months
期刊介绍: International Journal of Modern Physics C (IJMPC) is a journal dedicated to Computational Physics and aims at publishing both review and research articles on the use of computers to advance knowledge in physical sciences and the use of physical analogies in computation. Topics covered include: algorithms; computational biophysics; computational fluid dynamics; statistical physics; complex systems; computer and information science; condensed matter physics, materials science; socio- and econophysics; data analysis and computation in experimental physics; environmental physics; traffic modelling; physical computation including neural nets, cellular automata and genetic algorithms.
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