Modeling of Russian–Ukrainian war based on fuzzy cognitive map with genetic tuning

IF 1 Q3 ENGINEERING, MULTIDISCIPLINARY
A. Rotshtein, Brian A. Polin, D. Katielnikov, Neskorodieva Tetiana
{"title":"Modeling of Russian–Ukrainian war based on fuzzy cognitive map with genetic tuning","authors":"A. Rotshtein, Brian A. Polin, D. Katielnikov, Neskorodieva Tetiana","doi":"10.1177/15485129231184900","DOIUrl":null,"url":null,"abstract":"The Russian–Ukrainian conflict is considered as a dynamic system, whose variables are factors affecting the losses of the Russian army and the threat of the use of nuclear weapons. A fuzzy cognitive map (FCM) is used for modeling, that is, a directed graph whose vertices are model variables, and the weights of arcs are the degrees of positive and negative influences of variables on each other. The following factors influencing the losses of the Russian army and the threat of a nuclear strike were selected: resistance of the Ukrainian army, support of Ukraine with weapons, economic sanctions against Russia, opposition to the Russian government and its self-preservation instinct. The degrees of the influence of factors on each other and on the possibility of using nuclear weapons are evaluated by experts using fuzzy terms, which correspond to numeric values. To adjust the FCM, a genetic algorithm is used to select the degrees of influence of factors that minimize the discrepancy between the simulation results and expert estimations. The obtained FCM is used for scenario modeling of the conflict according to the “what if” scheme and ranking of factors according to their degree of influence on the level of nuclear threat.","PeriodicalId":44661,"journal":{"name":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Defense Modeling and Simulation-Applications Methodology Technology-JDMS","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/15485129231184900","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

The Russian–Ukrainian conflict is considered as a dynamic system, whose variables are factors affecting the losses of the Russian army and the threat of the use of nuclear weapons. A fuzzy cognitive map (FCM) is used for modeling, that is, a directed graph whose vertices are model variables, and the weights of arcs are the degrees of positive and negative influences of variables on each other. The following factors influencing the losses of the Russian army and the threat of a nuclear strike were selected: resistance of the Ukrainian army, support of Ukraine with weapons, economic sanctions against Russia, opposition to the Russian government and its self-preservation instinct. The degrees of the influence of factors on each other and on the possibility of using nuclear weapons are evaluated by experts using fuzzy terms, which correspond to numeric values. To adjust the FCM, a genetic algorithm is used to select the degrees of influence of factors that minimize the discrepancy between the simulation results and expert estimations. The obtained FCM is used for scenario modeling of the conflict according to the “what if” scheme and ranking of factors according to their degree of influence on the level of nuclear threat.
基于遗传调谐模糊认知图的俄乌战争建模
俄乌冲突被认为是一个动态系统,其变量是影响俄军损失和使用核武器威胁的因素。使用模糊认知图(FCM)进行建模,即一个有向图,其顶点是模型变量,弧的权重是变量相互之间的正负影响程度。影响俄军损失和核打击威胁的因素有:乌克兰军队的抵抗、乌克兰的武器支持、对俄罗斯的经济制裁、对俄罗斯政府的反对以及俄罗斯政府的自我保护本能。各因素之间的相互影响程度以及对使用核武器可能性的影响程度由专家使用模糊术语进行评估,这些模糊术语对应于数值。为了调整FCM,采用遗传算法选择影响因素的程度,使仿真结果与专家估计的差异最小。得到的FCM根据“假设”方案对冲突进行情景建模,并根据其对核威胁水平的影响程度对因素进行排序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
2.80
自引率
12.50%
发文量
40
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信