Distributed intelligent systems (DIS) appear where natural intelligence agents (humans) and artificial intelligence agents (algorithms) interact, exchanging data and decisions and learning how to evolve toward a better quality of solutions. The networked dynamics of distributed natural and artificial intelligence agents leads to emerging complexity different from the ones observed before. In this study, we review and systematize different approaches in the distributed intelligence field, including the quantum domain. A definition and mathematical model of DIS (as a new class of systems) and its components, including a general model of DIS dynamics, are introduced. In particular, the suggested new model of DIS contains both natural (humans) and artificial (computer programs, chatbots, etc.) intelligence agents, which take into account their interactions and communications. We present the case study of domain-oriented DIS based on different agents’ classes and show that DIS dynamics shows complexity effects observed in other well-studied complex systems. We examine our model by means of the platform of personal self-adaptive educational assistants (avatars), especially designed in our University. Avatars interact with each other and with their owners. Our experiment allows finding an answer to the vital question: How quickly will DIS adapt to owners’ preferences so that they are satisfied? We introduce and examine in detail learning time as a function of network topology. We have shown that DIS has an intrinsic source of complexity that needs to be addressed while developing predictable and trustworthy systems of natural and artificial intelligence agents. Remarkably, our research and findings promoted the improvement of the educational process at our university in the presence of COVID-19 pandemic conditions.

The changing environment creates the conditions for changing people’s behavior which together can lead to crisis situations. In the case of making economic decisions, the emerging non-stationarity of the dynamics of different components of the system can represent an economic crisis. The possibility of universal human values consideration in modeling decision-making under conditions of changing environment has been studied. The internal processes of an agent prior to making a decision are reflected in the concept of Beliefs-Desires-Intentions-Actions-Reactions (BDIAR). The article reviews the regularities and existing approaches to modeling economic decisions, and proposes a new, author’s approach. The mechanisms of influence of stress on decision-making, factors of rationality limitation, and risk assessment in the context of behavioral economics are revealed. The well-known theories of the influence of values on the change in the structure of consumption in a crisis situation are considered. Ways of taking into account emotions in the architecture of the agent are shown. In the proposed model, values are considered as a social factor in decision making. Due to their subjectivity, they are presented mathematically as the basis for assessing environmental objects. The subjectivity of the objects of choice assessments is reflected in the functions of attractiveness of the objects, the functions of the agent’s state dynamics, and in the subjectivity of the decision influence on the satisfaction of the agent. A possible modification of the components of the agent model is shown, taking into account the influence of values on the dynamics of consumption. A method is proposed for taking into account values in the BDIAR architecture when modeling an agent’s decision-making where the levels of the architecture correspond to values, preference functions, and functions of the agent’s state dynamics. The pseudonymized transactional data on debit cards of the partner bank customers were analyzed separately for 2017–2019 and 2020. The subjectivity of the environment influence in a crisis situation on the dynamics of changes in values and needs for different consumer groups is demonstrated, taking into account the clustering of their behavior types. Differences in the dynamics of values at the individual level and the level of groups are shown; an increase in the priority of survival values and a different rate of return to the pre-crisis state for different behavioral groups are also shown. The results obtained are useful for developing methods for modeling economic behavior in a non-stationary external environment, in particular, in the case of crises.

The topology of the interbank market plays a crucial role during a crisis, affecting the spreading or absorption of financial shock. The structure of an interbank network changes in the process of its evolution because of the interbank interactions and the interactions between banks and customers. To simulate a temporal interbank network, it is necessary to set an initial state and an evolution law for the topology and system entities. Because of the complex interplay between the network topology and the bank states, the stability of a temporal interbank network is generally unpredictable, even if all parameters and rules of interactions are known. In this paper, we present a simulation tool for temporal interbank networks aimed at exploring the different drivers contributing to evolutionary dynamics of banking networks. We describe a general-simulation scheme for temporal interbank networks and incorporate the creation and rewiring of edges because of the counter-party choices with the deletion of nodes and edges in case of a bank default. An example of the implementation of the general scheme is also presented and include models of banks and customers, strategies of counter-party choice, and clearing algorithms. To perform a qualitative and quantitative study of the evolutionary process, the proposed simulation tool supports the calculation of different topological and stability metrics and visualization of network evolution. The experimental study demonstrates (i) an illustrative example of the application of the simulation tool for synthetic networks while varying the counter-party choice policies and parameters of nodes and edges, and (ii) an investigation of the computational complexity and scalability of the simulation scheme.

Research question stated in current paper concerns measuring significance of interest topic to a person on the base of digital footprints, observed in on-line social media. Interests are represented by on-line social groups in VK social network, which were marked by topics. Topic significance to a person is supposed to be related to the fraction of representative groups in user’s subscription list. We imply that for each topic, depending on its popularity, relation to geographical region, and social acceptability, there is a value of group size which is significant. In addition, we suppose, that professional clusters of groups demonstrate relatively higher inner density and unify common groups. Therefore, following groups from more specific clusters indicate higher personal involvement to a topic – in this way, representative topical groups are marked. We build social group similarity graph, which is based on the number of common followers, extract subgraphs related to a single topic, and analyse bins of groups, build with increase of group sizes. Results show topics of general interests have higher density at larger groups in contrast to specific interests, which is in correspondence with initial hypothesis.