Agent-based models (ABMs) that have been actively developing recently are the most appropriate tool to simulate the behavior of complex socio-economic systems, as well as to evaluate multi-level quality management systems [1]. The basic idea behind the models of this class is to build a computing tool which is a combination of agents with a particular set of properties and which allows for the simulation of real phenomena. ABM differs from object-based models by the "activity" of its elements each having not only a given set of personal characteristics ("resources"), but also a target function ("interest") based on which its reaction to changes in the environment affecting areas of its interest (the "behavior") is simulated. The emergence of ABMs can be seen as the result of evolution in modeling methodology: the transition from mono-models (one model -one algorithm) to multi-models (one model -a set of independent algorithms). Thus, an ABM is an artificial society of autonomous interacting agents that can simulate a system as close to reality as possible. Agent-based approach to modeling is universal and easy to use for applied scientists due to its visual impact, but at the same time it imposes certain requirements on computing resources. It is obvious that direct modeling of lengthy social processes at national (or global) level generally requires considerable computational power.In their turn, supercomputers allow for severalfold increase in the number of agents and other quantitative parameters (network nodes, territory size) in models originally developed for use on conventional desktop computers. Therefore, supercomputer simulation is a logical and desirable step for the simple models which have already been successfully tested on conventional computers. However, the specific architecture of modern computers does not guarantee that the computer model software will immediately work on a supercomputer. The computing core parallelisation and often its 1
F orming the regional space of innovation is accompanied by the simultaneous development of various structures. The contemporary model of innovative development assumes interactions between government, industry, and universities. In this paper, the set of potential links between research organizations and the innovation activity of enterprises is characterized as the innovative space and is seen as a resource for innovation.Obtaining quantitative characteristics of such links and interactions is one of the most difficult tasks in analysing innovation processes. Our hypothesis is that regional innovation depends on the size of the innovation space and on how effectively it is used. The econometric modeling results do not contradict our hypothesis.Keywords: regional economy; innovation; econometric modeling; check of hypotheses; stochastic border; efficiency assessment Our estimates of the size of the innovation space used by regions of Russia when creating new production technologies confirm the high potential value of this resource. Using a Computable General Equilibrium (CGE) model that we developed, we analysed the innovative elements of regional economies (based on the example of the Republic of Bashkortostan) and quantitatively assessed the effects of different scenarios that aim to improve the socioeconomic system. We included an indicator of the effective use of the innovation space for a given region as one of the agents of the CGE model production function.Our results indicate the important role of regional authorities in promoting cooperation between the state, industry, and the research and education communities as well as in developing regional innovation systems.
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