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At the middle of the 2000-th, scientists studying the functioning of insect communities identified four basic patterns of the organizational structure of such communities. (i) Cooperation is more developed in groups with strong kinship. (ii) Cooperation in species with large colony sizes is often more developed than in species with small colony sizes. And small-sized colonies often exhibit greater internal reproductive conflict and less morphological and behavioral specialization. (iii) Within a single species, brood size (i. e., in a sense, efficiency) per capita usually decreases as colony size increases. (iv) Advanced cooperation tends to occur when resources are limited and intergroup competition is fierce. Thinking of the functioning of a group of organisms as a two-level competitive market in which individuals face the problem of allocating their energy between investment in intergroup competition and investment in intragroup competition, i. e., an internal struggle for the share of resources obtained through intergroup competition, we can compare such a biological situation with the economic phenomenon of “coopetition” — the cooperation of competing agents with the goal of later competitively dividing the resources won in consequence In the framework of economic researches the effects similar to (ii) — in the framework of large and small group competition the optimal strategy of large group would be complete squeezing out of the second group and monopolization of the market (i. e. large groups tend to act cooperatively) and (iii) — there are conditions, in which the size of the group has a negative impact on productivity of each of its individuals (this effect is called the paradox of group size or Ringelman effect). The general idea of modeling such effects is the idea of proportionality — each individual (an individual/rational agent) decides what share of his forces to invest in intergroup competition and what share to invest in intragroup competition. The group’s gain must be proportional to its total investment in competition, while the individual’s gain is proportional to its contribution to intra-group competition. Despite the prevalence of empirical observations, no gametheoretic model has yet been introduced in which the empirically observed effects can be confirmed. This paper proposes a model that eliminates the problems of previously existing ones and the simulation of Nash equilibrium states within the proposed model allows the above effects to be observed in numerical experiments.

In this article we propose a new approach to the analysis of econometric industry parameters for the industry consolidation level. The research is based on the simple industry automatic control model. The state of the industry is measured by quarterly obtained econometric parameters from each industry’s company provided by the tax control regulator. An approach to analysis of the industry, which does not provide for tracking the economy of each company, but explores the parameters of the set of all companies as a whole, is proposed. Quarterly obtained econometric parameters from each industry’s company are Income, Quantity of employers, Taxes, and Income from Software Licenses. The ABC analysis method was modified by ABCD analysis (D — companies with zero-level impact to industry metrics) and used to make the results obtained for different indicators comparable. Pareto charts were formed for the set of econometric indicators.

To estimate the industry monopolization, the Herfindahl – Hirschman index was calculated for the most sensitive companies metrics. Using the HHI approach, it was proved that COVID-19 does not lead to changes in the monopolization of the Russian IT industry.

As the most visually obvious approach to the industry visualization, scattering diagrams in combination with the Pareto graph colors were proposed. The affect of the accreditation procedure is clearly observed by scattering diagram in combination with red/black dots for accredited and nonaccredited companies respectively.

The last reported result is the proposal to use the Licenses End-to-End Product Identification as the market structure control instrument. It is the basis to avoid the multiple accounting of the licenses reselling within the chain of software distribution.

The results of research could be the basis for future IT industry analysis and simulation on the agent based approach.

The paper considers the basic model of competitive interactions and its use for the analysis and description of social processes. The peculiarity of the model is that it describes the interaction of several competing actors, while actors can vary the strategy of their actions, in particular, form coalitions to jointly counter a common enemy. As a result of modeling, various modes of competitive interaction were identified, their classification was conducted, and their features were described. In the course of the study, the attention is paid to the so-called “rough” (according to A.A. Andronov) cases of the implementation of competitive interaction, which until now have rarely been considered in the scientific literature, but are quite common in real life. Using a basic mathematical model, the conditions for the implementation of various modes of competitive interactions are considered, the conditions for the transition from one mode to another are determined, examples of the implementation of these modes in the economy, social and political life are given. It is shown that with a relatively low level of competition, which is non-antagonistic in nature, competition can lead to an increase in the activity of interacting actors and to overall economic growth. Moreover, in the presence of expanding resource opportunities (as long as such opportunities remain), this growth may have a hyperbolic character. With a decrease in resource capabilities and increased competition, there is a transition to an oscillatory mode, when weaker actors unite to jointly counteract stronger ones. With a further decrease in resource opportunities and increased competition, there is a transition to the formation of stable hierarchical structures. At the same time, the model shows that at a certain moment there is a loss of stability, the system becomes “rough” according to A.A. Andronov and sensitive to fluctuations in parameter changes. As a result, the existing hierarchies may collapse and be replaced by new ones. With a further increase in the intensity of competition, the actor-leader completely suppresses his opponents and establishes monopolism. Examples from economic, social, and political life are given, illustrating the patterns identified on the basis of modeling using the basic model of competition. The obtained results can be used in the analysis, modeling and forecasting of socioeconomic and political processes.