cognitive modeling
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В статье рассматриваются возможности применения семантического моделирования, включающего, в частности, онтологическое и когнитивное моделирование для поддержки совместных исследований энергетических и социо-экологических систем. Работа посвящена использованию онтологического инжиниринга для структурирования знаний предметных областей и когнитивного моделирования в исследованиях влияния функционирования энергетических объектов на природную среду и человека. Онтологическое моделирование используется для выявления, описания и согласования базовых понятий предметных областей исследований и позволяет систематизировать и наглядно представить взаимосвязи между элементами природной среды, объектами энергетики и их характеристиками, факторами воздействия и методами их расчета. Когнитивное моделирование используется для выявления структуры причинно-следственных связей между факторами, влияющими на устойчивость системы. The article discusses the possibilities of applying semantic modeling, including, in particular, ontological and cognitive modeling to support joint research of energy and socio-ecological systems. The work is devoted to the use of ontological engineering for structuring knowledge of subject areas and cognitive modeling in studies of the impact of the functioning of energy facilities on the natural environment and humans. Ontological modeling is used to identify, describe and coordinate the basic concepts of subject areas of research and allows you to systematize and visualize the relationship between elements of the natural environment, energy facilities and their characteristics, impact factors and methods of their calculation. Cognitive modeling is used to identify the structure of causal relationships between factors affecting the stability of the system.

The role of precedent phenomena in the media space, in the processes of text generation and meaning formation of the media text is studied. Today, the media are modelling the reality. It may differ from the reality as it is, which is not reflected, but presented through diverse interpretations. In interpreting a statement/message, a significant role belongs to precedent phenomena and the underlying knowledge structures. The problems of the media text, a socially significant communicative environment of our time, determined the relevance of the research. The purpose of the study is to characterize the role of precedent units in interpreting the media text (at the level of meaning and significance). The materials of the research are Russian newspapers, the Newspaper Corpus of the Russian Language, the National Corpus of the Russian Language, and internet sources in Russian. The authors used methods of the discursive level, discourse analysis of precedent phenomena, taking into account their extra-linguistic dimension; the cognitive projection of the study assumed linguo-cognitive analysis and linguo-cognitive modeling as modeling the structure of meaning, text formation and sense formation based on precedent units. The study provides a discourse analysis of precedent phenomena taking into account their extralinguistic dimension and a liguocognitive analysis in order to model additional meanings and the structure of the meaning of precedent units. The cognitive approach identified the role of extralinguistic knowledge in forming the meaning of the text and the precedent unit. Thematized (invariant) knowledge shared by communicants is the key to the successful interpretation of the precedent unit and the functional effectiveness of the media text. Prospects: in the future, it seems promising to study the corpus of precedent units relevant to professional and citizen journalism, their structure, differences, spheres of functioning, as well as modeling the meaning of precedent units in social networks, memes, and the blogosphere.

Using Bayesian methods to apply computational models of cognitive processes, or Bayesian cognitive modeling, is an important new trend in psychological research. The rise of Bayesian cognitive modeling has been accelerated by the introduction of software such as Stan and PyMC3 that efficiently automates the Markov chain Monte Carlo (MCMC) sampling used for Bayesian model fitting. Unfortunately, Bayesian cognitive models can struggle to pass the computational checks required of all Bayesian models. If any failures are left undetected, inferences about cognition based on model output may be biased or incorrect. As such, Bayesian cognitive models almost always require troubleshooting before being used for inference. Here, we present a deep treatment of the diagnostic checks and procedures that are critical for effective troubleshooting, but are often left underspecified by tutorial papers. After a conceptual introduction to Bayesian cognitive modeling and MCMC sampling, we outline the diagnostic metrics, procedures, and plots necessary to identify problems in model output with an emphasis on how these requirements have recently been improved. Throughout, we explain how the most commonly encountered problems may be remedied with specific, practical solutions. We also introduce matstanlib, our MATLAB modeling support library, and demonstrate how it facilitates troubleshooting of an example hierarchical Bayesian model of reinforcement learning implemented in Stan. With this comprehensive guide to techniques for detecting, identifying, and overcoming problems in fitting Bayesian cognitive models, psychologists across subfields can more confidently build and use Bayesian cognitive models.All code is freely available from github.com/baribault/matstanlib.

Abstract The necessary measures to ensure the safety of technical structures, freight/passenger stations and other transport infrastructure facilities include continuous video monitoring with a comprehensive analysis of the scene. In conditions of high density of numerous objects continuously moving through the observation area, one of the main available signs of detecting anomalies in their behavior is their trajectory of the object. In this paper, we propose an approach to building a system for analyzing the behavior of dynamic video surveillance objects based on their tracking, implemented by means of cognitive modeling. The proposed procedures for intelligent analysis of the nature of movement of video surveillance objects are based on a combination of neural network technologies and the logical inference mechanism of the expert system, which expands the basic algorithms for technical equipment of video surveillance systems. The practical significance of the considered solutions is to increase the efficiency of detecting suspicious situations in conditions of high traffic density by conducting a parallel analysis of the movement of numerous objects of the scene, which entails the prevention of possible illegal actions in places of mass presence of people, including transport infrastructure facilities.

The article discusses topical issues of the influence of informatization on the development of the country’s regions in the conditions of the modern unstable world. The nature of the development of a region can be reflected and understood on the basis of qualitative and quantitative information about its socio-economic indicators, about their relationship and trends in their changes under the influence of internal and external factors. At the same time, information can most often be incomplete, difficult to access, untimely, contradictory, etc. Therefore, in this paper, it is proposed to use a cognitive approach and cognitive modeling of complex systems to overcome the problems of information deficiency by imitating cognitive modeling of the structure and behavior of a complex regional system. The simulation was carried out using the author’s CMCS (Cognitive Modeling Complex System) software system. The results of multi-stage cognitive modeling, consisting in the development of cognitive maps “Influence of ICT on the state of the region” and “Digitalization of the republic” (according to the socio-economic state of the Republic of Dagestan), analysis of structural properties and modeling scenarios for the development of situations on the model are presented. Scenarios make it possible to foresee the ways of possible development of the system under the influence of various factors, including the factor of informatization.

Abstract. The article develops and substantiates the need to determine the causal links between concepts that contribute to the quality of higher education. The lack of motivation for radical reform of higher education is still hampered by attempts to use the successful international experience of the process of building a full-fledged system of quality assurance in the provision of educational services. The aim of the article is to develop a model for identifying causal links between the concepts of financial and economic support, which contribute to improving the quality of higher education. A system of concepts of the internal state and macro-environment of financial security has been formed, which has a positive or negative impact on the intensification of higher education. A fuzzy cognitive map of the impact of financial and economic support on improving the quality of higher education has been built. Scales and criteria for providing a qualitative assessment of the impact of the concepts of financial and economic support for the intensification of higher education in accordance with the introduced linguistic sets are calculated on the basis of the trapezoidal number method. The concepts of internal state and macroenvironment for activation of higher education are defined. The negative impact on the level of public spending on education and opportunities for access to ICT, which constrains the prospects for development and realization of the potential of participants in the higher education process, has been proved. The negative impact on the quality of higher education on the migration of students to study abroad has been identified, as access to higher education is almost unlimited due to significant government procurement and relatively low cost of contract education, and the return on higher education is relatively low. According to the simulation results, in order to intensify higher education, it is necessary to focus on the quality of teachers, provide opportunities for development, competence development, obtaining a higher level of qualification, which includes postgraduate and doctoral studies and academic degrees. Currently, an important priority of the European innovation system is the formation of the European Research Area. That is why there is a need to find effective mechanisms to influence the quality of research and innovation, which is represented by the number of publications / patents / CAT, investment and innovation projects, grants. Support for these concepts will provide an opportunity to unleash scientific and innovative potential, have a high social status in society and will improve the quality of educational services provided. Keywords: higher education, cognitive modeling, financial and economic support, consonance and dissonance, influence on the system, concepts of internal state, concepts of macroenvironment. JEL Classіfіcatіon І22, G17 Formulas: 9; fig.: 1; tabl.: 5; bibl.: 11.

Рассматривается понятие устойчивости в смысле «Resilience» и связанные с ним понятия энергетической и экологической безопасности. Предлагается рассматривать качество жизни как фактор интеграции исследований устойчивости энергетических, социо-экологических и социо-экономических систем. Вводятся критерии устойчивости энергетических, экологических и социальных систем. Когнитивное моделирование рассматривается как один из основных инструментов исследований устойчивости. Приводятся примеры когнитивного моделирования. The concept of resilience and related concepts of energy and environmental safety are considered. It is proposed to use the quality of life as a integration factor of resilience research of energy, socio-ecological and socio-economic systems. Criteria for the resilience of energy, ecological and social systems are introduced. Cognitive modeling is seen as one of the main tools in resilience research. Examples of cognitive modeling are given.