RESEARCH OF METHODS FOR CONSTRUCTING CAUSAL GRAPH MODELS FOR COMPLEX SOCIO-HUMANITARIAN SYSTEMS

Abstract

Complex socio-humanitarian systems are a type of systems that are studied in sociology, anthropology,
economics, political science, and other humanities. These systems are characterized by the complexity of
interactions between their constituent elements, which can be both human (individuals, groups) and cultural,
social, economic and political aspects. For example, society as a socio-humanitarian system consists of various
elements, such as people, culture, institutions, values, and so on. They interact with each other, forming a
complex network of connections and influences that determines the behavior and development of society.
To better understand such systems, various approaches are used, including systems analysis, social network
theory, complexity theory, and other methods. These approaches help to identify the main patterns in the
functioning of complex socio-humanitarian systems and predict their development in the future. This article
discusses approaches to identifying cause-and-effect relationships and highlights the basic requirements for
building these connections in the context of complex socio-humanitarian systems that deal mainly with semistructured
information, often in the form of natural language and texts. The strengths and weaknesses of the
identified approaches were identified, and examples of the use of modern methods of constructing graphs on
various tasks were considered: identifying risks in business, analyzing social phenomena, identifying the
presence of causality in texts. The study showed that the most productive methods are machine learning, for
example, language models for extracting knowledge from text in combination with neural network technologies
and graph representations of knowledge. They require solid knowledge of mathematics, statistics and
programming, at least in Python, with the most impressive tool support for solving machine learning problems.
Also, identifying causality is based not only on correlation but also on other methods such as the
Granger test used for time series analysis.