All issues

The paper presents agent-based model for city formation named Technoscape which is both local and nonlocal. Technoscape can, to a certain degree, be also assumed as a model for emergence of global economy. The current version of the model implements very simple way of agents’ behavior and interaction, still the model provides rather interesting spatio-temporal patterns.

Locality and non-locality mean here the spatial features of the way the agents interact with each other and with geographical space upon which the evolution takes place. Technoscape agent is some conventional artisan, family, or а producing and trading firm, while there is no difference between production and trade. Agents are located upon and move through bounded two-dimensional space divided into square cells. The model demonstrates processes of agents’ concentration in a small set of cells, which is interpreted as «city» formation. Agents are immortal, they don’t mutate and evolve, though this is interesting perspective for the evolution of the model itself.

Technoscape provides some distinctively new type of self-organization. Partially, this type of selforganization resembles the behavior of segregation model by Thomas Shelling, still that model has evolution rules substantially different from Technoscape. In Shelling model there exist avalanches still simple equilibria exist if no new agents are added to the game board, while in Technoscape no such equilibria exist. At best, we can observe quasi-equilibrium, slowly changing global states.

One non-trivial phenomenon Technoscape exhibits, which also contrasts to Shelling segregation model, is the ability of agents to concentrate in local cells (interpreted as cities) even explicitly and totally ignoring local interactions, using non-local interactions only.

At the same time, while the agents tend to concentrate in large one-cell cities, large scale of such cities does not guarantee them from decay: there always exists a process of «enticement» of agents and their flow to new cities.

This study presents a methodology for assessing the functioning of complex systems, taking into account the type of distribution of indicators characterizing the functioning of structural elements of such a system operating under conditions of uncertainty. Unlike previous studies, the work integrates the identification of the type of indicator distribution with their Bayesian assessment, taking into account the norms characterizing the required results of the functioning of the structural elements of the system. The type of distribution and its characteristics provide more complete information about the properties of an object and their consideration affects the results of evaluating the functioning of elements of complex systems, especially in cases of the presence of “heavy” tails characteristic of a number of socio-economic processes and systems. It is shown that it is advisable to identify the type of distribution based on modified data due to the exclusion of the best trend in statistical characteristics. The determination of the best type of distribution is carried out according to the Kolmogorov – Smirnov agreement criterion, which showed the best (according to the available sample) statistical estimates compared to other criteria or a mixture of them. The obtained type of distribution is used in the probabilistic assessment of the results of the functioning of subsystem elements based on Bayesian intelligent technologies that successfully operate in conditions of uncertainty, and which make it possible to present such results on numerical and linguistic scales in a general hierarchical information model. The methodology was tested using the example of the object subsystem (G.B.Kleiner’s spatio-temporal classification) of the economic subsystem of the Tula region, considered as a complex system — a socio-ecological-economic system. The volume of gross domestic product by region in six sections of the all-Russian classifier of economic activities (sections A, B, C, D, E) was used as assessment indicators. In some cases, it is possible to reduce the level of uncertainty in the estimates of indicators.; to obtain estimates that are more stringent than the norm, which ultimately leads to the conclusion that the use of the distribution type in the formation of probabilistic estimates of indicators of structural elements of complex systems makes it possible to justify their correct application within the framework of the concept of evidence-based modeling.

The article proposes a space-time approach to modeling the diffusion of information and communication technologies based on the Fisher –Kolmogorov– Petrovsky – Piskunov equation, in which the diffusion kinetics is described by the Bass model, which is widely used to model the diffusion of innovations in the market. For this equation, its equilibrium positions are studied, and based on the singular perturbation theory, was obtained an approximate solution in the form of a traveling wave, i. e. a solution that propagates at a constant speed while maintaining its shape in space. The wave speed shows how much the “spatial” characteristic, which determines the given level of technology dissemination, changes in a single time interval. This speed is significantly higher than the speed at which propagation occurs due to diffusion. By constructing such an autowave solution, it becomes possible to estimate the time required for the subject of research to achieve the current indicator of the leader.

The obtained approximate solution was further applied to assess the factors affecting the rate of dissemination of information and communication technologies in the federal districts of the Russian Federation. Various socio-economic indicators were considered as “spatial” variables for the diffusion of mobile communications among the population. Growth poles in which innovation occurs are usually characterized by the highest values of “spatial” variables. For Russia, Moscow is such a growth pole; therefore, indicators of federal districts related to Moscow’s indicators were considered as factor indicators. The best approximation to the initial data was obtained for the ratio of the share of R&D costs in GRP to the indicator of Moscow, average for the period 2000–2009. It was found that for the Ural Federal District at the initial stage of the spread of mobile communications, the lag behind the capital was less than one year, for the Central Federal District, the Northwestern Federal District — 1.4 years, for the Volga Federal District, the Siberian Federal District, the Southern Federal District and the Far Eastern Federal District — less than two years, in the North Caucasian Federal District — a little more 2 years. In addition, estimates of the delay time for the spread of digital technologies (intranet, extranet, etc.) used by organizations of the federal districts of the Russian Federation from Moscow indicators were obtained.