Периодизации исторического процесса и выделение раннего нового времени как самостоятельного периода
The periodization of historical process determines a definite scale and scope to compare societies and identify their development level in a certain period. Such a periodization can be based on the most significant technological changes – productive revolutions (agrarian, industrial, informational-scientific). Industrial revolution can be divided into two stages: the beginning of the first one is dated to the 15–16th centuries (the early industrial revolution), the start of the second stage – to 1730–60s (the industrial breakthrough). Thus, the early Modern period (the late 15th – the late 18th centuries) is the time when both stages of the industrial revolution took place, i.e. this period can be considered to obtain an inner wholeness and structure in terms of the theory of periodization of historical process.
The author argues on expediency and mutual conditionality of evolutionary changes in the nature and in society. In the article three major factors of the evolution are allocated, namely: the accident, the factor of coincidence of circumstances and the factor of acceleration of social evolution.
There is no doubt that periodization is a rather effective method of data ordering and analysis, but it deals with exceptionally complex types of processual and temporal phenomena and thus it simplifies historical reality. Many scholars emphasize the great importance of periodization for the study of history. In fact, any periodization suffers from one-sidedness and certain deviations from reality. However, the number and significance of such deviations can be radically diminished as the effectiveness of periodization is directly connected with its author's understanding of the rules and peculiarities of this methodological procedure. In this paper we would like to suggest a model of periodization of history based on our theory of historical process. We shall also demonstrate some possibilities of mathematical modeling for the problems concerning the macroperiodization of the world historical process. This analysis identifies a number of cycles within this process and suggests its generally hyperexponential shape, which makes it possible to propose a number of forecasts concerning the forthcoming decades.
Phenomenology, that is the description of observed phenomena, is absolutely insufficient for a profound comprehension of global processes, so one needs some explanatory theories. The present article offers such a new explanatory concept useful for analyzing causes and trends of global shifts in historical process – the theory of production revolutions. The author reveals possibilities of using this theory to explain the logic of interaction between technological macroshifts, significant changes in social structure and qualitative growth of processes' scale (globality), connections and phenomena within the World System. Much attention is paid to the interrelation between the effects of the latest (information-scientific) revolution and globalization processes. Some forecasts for the future are made basing on the theory of production revolutions
In the present paper, on the basis of the theory of production principles and production revolutions, we reveal the interrelation between K-waves and major technological breakthroughs in history and make forecasts about features of the sixth Kondratieff wave in the light of the Cybernetic Revolution that, from our point of view, started in the 1950s. We assume that the sixth K-wave in the 2030s and 2040s will merge with the final phase of the Cybernetic Revolution (which we call a phase of self-regulating systems). This period will be characterized by the breakthrough in medical technologies which will be capable to combine many other technologies into a single complex of MBNRIC-technologies (med-bio-nano-robo-info-cognitive technologies). The article offers some forecasts concerning the development of these technologies.
We address the external effects on public sector efficiency measures acquired using Data Envelopment Analysis. We use the health care system in Russian regions in 2011 to evaluate modern approaches to accounting for external effects. We propose a promising method of correcting DEA efficiency measures. Despite the multiple advantages DEA offers, the usage of this approach carries with it a number of methodological difficulties. Accounting for multiple factors of efficiency calls for more complex methods, among which the most promising are DMU clustering and calculating local production possibility frontiers. Using regression models for estimate correction requires further study due to possible systematic errors during estimation. A mixture of data correction and DMU clustering together with multi-stage DEA seems most promising at the moment. Analyzing several stages of transforming society’s resources into social welfare will allow for picking out the weak points in a state agency’s work.