name
Tselykh Alexey Alexandrovich
Scholastic degree
•
Academic rank
associated professor
Honorary rank
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Organization, job position
• South Federal University
Институт компьютерных технологий и информационной безопасности
доцент кафедры информационно-аналитических систем безопасности
Research interests
моделирование социотехнических систем, интеллектуальный анализ данных, теория нечетких графов и гиперграфов
Web site url
—
Current rating (overall rating of articles)
0
TOP5 co-authors
Articles count: 2
Сформировать список работ, опубликованных в Научном журнале КубГАУ
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METHODOLOGY OF MODELING SOCIAL SYSTEMS FOR CONFLICT ANALYSIS AND CONTROL
Description
This article provides results of studying the world achievements in modeling social systems in the aspects of personality, group, and social institute (e.g. the state and army). Through prism of conflict, we review research methodology for modeling social identity, social navigation, geopolitical processes, and command and control systems of the enemy. We discuss four agent-based models of social identity: SCIPR, MetaContrast, PS-I, and SILAS. We examine Spence model for modeling social navigation. Basic principles for modeling relations between the states are considered using ontology approach. The presented model allows systemic analysis of various micro- (intrastate) and macrolevel (external) variables and relations between them. Modeling command and control system of the enemy is implemented as a part of an automated decision support system that tackles the problems of enemy structure identification as well as classification of objects and relations within the structures. The object of study are approaches, methods and models for representation and analysis of group interaction. The subject of study are processes of agent self-identification and interaction, formal and informal organizations, the states and public institutes as well as processes and principles for group formation and mechanisms for behavior control. Research methodology: social network analysis, ontology approach, theory of graphs and hypergraphs, multiagent systems
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Description
Our research aims at providing new effective control methods for scientific and technical expertise to organizations and foundations that fund research projects on a competitive basis. These methods should provide analytical decision support for group decision making in a distributed environment. In this work, we consider an integral model for knowledge representation – a socio-semantic knowledge network that combines social links and a semantic description of knowledge into a mathematically formalized graph theoretic structure. In terms of actor-network theory, we consider a multimodal network with actors on one of the levels and artefacts (i.e. contexts that express network links) on the other level. In order to represent knowledge domain based on a graph and hypergraph paradigm and fuzzy sets theory, we provide a sufficiently complete set of elements and relations (either trustworthy or partially trustworthy) both between multitype elements and their heterogeneous groups. The formal representation allows applying the model to solve numerous practical tasks such as expert finding, formation of expert groups, expertise refinement, reduction of subjectivity, analysis of an expertise process, analysis of processes within expert groups. Research methodology: social network analysis (SNA), theory of graphs and hypergraphs, fuzzy calculus and fuzzy logic
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