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.

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Abstract:The topology of the interbank market plays a crucial role during a crisis, a ecting 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 di erent 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.

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