Strategic management in Russia: automated construction of a cause-and-effect diagram and identification of key themes based on media data

IF 0.4 Q4 MATHEMATICS, APPLIED
A. Zagranovskaia
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引用次数: 0

Abstract

Modern economic conditions are characterized by a high degree of uncertainty and complexity, which is difficult to formalize. Fuzzy cognitive maps make it possible to solve this problem – to cope with complexity, but when cognitive maps are built on the basis of expert opinions, this sometimes causes distrust due to the subjectivity of the judgments of individual specialists and doubts about compliance with the examination procedure. Therefore, the task of developing analytical tools to increase the awareness of decision makers about the real state of affairs in the organization and in the external environment is relevant, because contributes to the growth of their efficiency. The article proposes and tests a procedure for automated construction of a cause-and-effect diagram using statistical methods, as well as methods and models of machine learning. With the help of modern methods of topic modeling, key topics (concepts) are identified in the area under consideration for the considered period of time. The Doc2Vec model is then used to derive a fixed length numeric vector from the identified topics. The Granger test is then used to establish the possibility of a causal relationship between the topics found. The constructed cause-and-effect diagram allows you to describe the current situation and understand the key concepts of the area under consideration. According to the Russian media for 20 years (from 2002 to 2021), a cause-and-effect diagram was built that reflects the problems of strategic management in Russia. The analysis of the diagram made it possible to conclude that the topic of Russian projects is the most significant in the area under consideration.
俄罗斯的战略管理:基于媒体数据自动构建因果关系图和识别关键主题
现代经济状况具有高度的不确定性和复杂性,难以形式化。模糊认知地图使解决这一问题成为可能——应对复杂性,但当认知地图建立在专家意见的基础上时,有时会由于个别专家判断的主观性和对审查程序的遵从性的怀疑而引起不信任。因此,开发分析工具以提高决策者对组织和外部环境中事务的真实状态的认识的任务是相关的,因为有助于提高他们的效率。本文提出并测试了一个使用统计方法自动构建因果关系图的程序,以及机器学习的方法和模型。在现代主题建模方法的帮助下,在考虑的时间段内,在考虑的领域中确定关键主题(概念)。然后使用Doc2Vec模型从已标识的主题派生出固定长度的数字向量。然后使用格兰杰检验来建立所发现主题之间因果关系的可能性。构建的因果关系图允许您描述当前情况并理解所考虑的区域的关键概念。根据俄罗斯媒体20年(2002年至2021年)的报道,建立了一个反映俄罗斯战略管理问题的因果关系图。通过对图表的分析,可以得出结论,在审议的领域中,俄罗斯项目的主题是最重要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.70
自引率
0.00%
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