The article explores the possibility of applying tensor method of dual networks for analysis of transport and tourism components in sustainable development of territories. The tensor method of dual networks, in contrast to other methods allows to consider the structure of the large-scale intelligence system and the processes occurring in it as one whole. Thus, we have the possible to complex analyze all the components of a large-scale system even when its structure, the number of its elements and the connections between them will be changed. Tensor equations make it possible to accurately calculate the parameters of a system when simulating various ways of connecting its elements. On the example of the analysis of the tourist transport system, the advantages of using the method of double networks to assess the impact of the system on the sustainable development of the territory are shown.

The article deals with research related to the use of artificial intelligence technologies for effective decision-making in corporate governance under conditions of deep uncertainty. To process uncertainty, it is proposed to use the cognitive capabilities of artificial intelligence. Cognitivism can be used to implement intuitive, psychological and other components of the internal mental activity of a person when making decisions. These capabilities allow one to make informed decisions and predict the consequences of these decisions. To study the properties of deep uncertainty, the authors suggest using a tensor model. The tensor model of deep uncertainty makes it possible to study additional properties of uncertainty that are not available in traditional models, such as Bayesian formalism, Dempster-Shafer theory, fuzzy sets, a method based on certain factors (Stanford formalism), and others. The use of the tensor model allows one to study the spatial model of uncertainty, real and imaginary values of uncertainty, as well as uncertainty invariants with respect to various transformations of the coordinate system.