Vol 30, No 3 (2022)

The purpose of this work is to consider the possibility of nonlinear influence of social media algorithms to the users opinions. A social media inherent algorithm of information ranging interacts with the user inherent bias and that increases the positive feedback loop. The result of this interaction is receiving by the user the only one side of an opinion and the user looses the very possibility to receive the opposite information. The conditions for the society polarization by means of a social media are investigated. Methods. In this paper, a model of users opinions dynamics was studied. There are two types of user’s strategy was considered: strategy 1, when a user puts "like" on information with proximity to his own view, but differed in any direction; strategy 2, when a user puts "like" on information along his own view, but more strict. Results. It was shown that for strategy 1 the society comes to a consensus, but for strategy 2 the society polarizes to the two opposite views. Considering the mixed society, where both strategies are used, it was found that the bifurcation to the society polarization appears when there are more than 40% of people using strategy 2. Conclusion. The inherent algorithms of social media, which are created to adapt in coming information to the user’s interests, creates or amplifies the bias of the user’s opinion and locks the user in an information chamber of only one type. That effect is substantially created by the social media algorithm itself. Thus interaction of users within a social media may increase the polarization of a society more than if they would communicate offline.

The purpose of this work is to construct a fitness function that depends on the set of coexisting competing hereditary elements based on population dynamics in the “predator– prey” model with the logistic growth of prey. Materials and methods. The work uses the generalized Volterra model. The planktivorous fish plays the role of a predator. Many different species of zooplankton are considered as prey, which differ from each other in the hereditary strategies of daily vertical migrations. The model takes into account the intraspecific competition of prey. The peculiarity of the model consists of the presence of pairs of hereditary strategies in which the carriers of the first can displace the carriers of the second and vice versa — the carriers of the second can displace the carriers of the first, depending on the set of competing strategies in which they coexist. To restore the fitness function, the ranking method is used, which is reduced to the classification of ordered pairs of hereditary strategies into two classes “the first strategy displaces the second” and “the second displaces the first”. Results. The article presents a new methodology for constructing the fitness function. The technique involves two stages. First, the fitness function is reconstructed for a certain finite subset of elements on the basis of processing data on the long-term dynamics and comparing their competitive advantages. At the second stage, the form of the fitness function is derived for an arbitrary set of elements. It uses the features of interspecies interaction reflected in the model. With the help of the constructed fitness function, an evolutionarily stable regime of daily vertical migrations of zooplankton is modeled by numerically solving the minimax problem. Conclusion. The proposed method for constructing a fitness function that depends on a set of competing strategies is quite general and can be applied to a wide range of models of population dynamics. The strategy of diel vertical migrations of zooplankton constructed as a result of modeling is in good agreement with empirical data.

Purpose of work. The article is devoted to the topic of measuring the cognitive potential of a person on the basis of the obtained experimental data in order to identify its potential capabilities, as well as to monitor their dynamics, for example, to diagnose recovery after an illness. This goal is divided in the study into two tasks, namely, to assess the cognitive potential, it is necessary to develop two algorithms: 1. Assessment of the level of cognitive complexity of tasks. 2. Systems of levels of cognitive potential for an individual. Methods. The basis of the methods is a set of experimental, including specially developed author’s, techniques, as well as mathematical methods for processing data and calculating the entered specific parameters to formalize the cognitive potential. Results. On the basis of these methods, methods (and specific formulas) are proposed for calculating the cognitive potential of an individual using experimental data and tasks of various levels of complexity. Conclusion. Within the framework of this study, a methodology for determining the value of cognitive potential was created on the basis of the theory of information images / representations, as well as a specially developed web-toolkit for objectifying cognitive skills (including the so-called softskills). This value can be useful, both in studies related to changes in cognitive abilities as a result of the influence of various internal and external factors (for example, learning, diseases, injuries, etc.), diagnostic goals (for example, with the aim of determining the speed recovery after a disease that affects cognitive activity, such as a stroke or SARS-CoV-2), and in the formation of requirements for certain work positions that significantly depend on the cognitive abilities of the individual.

The purpose of this work is study of the neurodynamic foundations of the creative activity of the brain. Modern AI systems using deep neural network training require large amounts of input data, high computational costs and long training times. On the contrary, the brain can learn from small datasets in no time and, crucially, it is fundamentally creative. Methods. The study was carried out through computational experiments with neural networks containing 5 and 7 oscillatory layers (circuits) trained to represent abstract concepts of a certain class of animals. The scheme of neural networks with even cyclic inhibition (ECI networks) contains only bilateral inhibitory connections and consists of two subnets: a reference noncoding network, which is an analogue of the default brain mode neural network, and the main information network that receives time sequences of environmental signals and contextual inputs. After training, the reading of the population phase codes was performed with a simple linear decoder. Results. Conceptual learning of the network leads to the generation of a number of spatial abstract images that are distinguished by the most pronounced features of the relevant line of animals. In computational experiments, a wide set of isomorphic representations of concepts was obtained through: a) transformations of image spaces in a wide range of time scales of the training input signal flow, b) internal regulation of the time scales of mental representations of concepts, c) confirmation on the model of the dependence of psychological proximity of concepts on semantic distance; d) calling from memory (decoding) distributed groups of neurons of animal concepts, which the network has not been trained in. Conclusion. This paper shows for the first time how, using a small set of event input data (a sequence of 4 CCW and 2 CW signals) and very limited computational resources, ECI networks exhibit creative cognitions based on relational relationships, conceptual learning and generalization of knowledge.

This year 2022, Vladimir Grigorievich celebrated his seventy-fifth anniversary. We, his students and colleagues, sincerely congratulate him on his birthday and wish him health, fulfillment of personal and creative plans!