Cyclical Dynamics in Economics and Politics in the Past and in the Future
The innovation capacity of a system can be measured as the synergy in interactions among its parts. Synergy can be considered as a consequence of negative entropies among three parts of the system. We analyze the development of synergy value in the Norwegian innovation system in terms of mutual information among geographical, sectorial, and size distributions of firms. We use three different techniques for the evaluation of the evolution of synergy over time: rescaled range analysis, DFT, and geographical synergy decomposition. The data was provided by Statistics Norway for all Norwegian firms registered in the database between 2002 and 2014. The results suggest that the synergy at the level of both the country and its seven regions show non-chaotic oscillatory behavior which resonates in a set of natural frequencies. The finding of a set of frequencies implies a complex Triple-Helix structure, composed of many elementary triple helices, which can be theorized in terms of a fractal TH manifold.
В данной книге авторы предлагают новую методологию долгосрочного социально-экономического моделирования и прогнозирования, основанную на кондратьевских больших циклах экономической конъюнктуры. Использование методологии позволяет обнаружить точки кризисов, рецессий и бифуркаций и тем самым повышает точность и надежность прогноза. Методология применяется для системного анализа мировой динамики и построения сценариев развития России.
Autonomous higher order differential equations with scalar nonlinearities, periodic with respect to the main phase variable under appropriate generic conditions, have an infinite sequence of isolated cycles with amplitudes growing to infinity and periods converging to some specific value T.
В главе рассматривается зарубежный опыт долгосрочного прогнозирования научно-технологического развития. Обсуждаются основные апробированные практикой методы прогнозирования, их возможности и ограничения.
This issue starts a series of annual almanacs dedicated to the analysis of economic fluctuations of various lengths, but especially – to the study of large-scale wave-like perturbations of the global socioeconomic realm with a characteristic length of about half a century. These fluctuations, or cycles as preferred by some authors, were named ‘Kondratieff waves’ after the famous Russian scientist Nikolay Kondratieff. In the present publication these waves will be frequently referred also as K-waves for short. The analysis of K-waves allows understanding the long-term dynamics of the World System development, as well as proposing future scenarios about the unfolding of the global economy, for it clarifies much for our understanding of the crises of the past and the current global economic crisis. Kondratieff waves constitute a sort of mystery that has been haunting economic and social researchers for almost a century. Why do we observe such regularity in the long-term behavior of economic and non-economic indicators? Why in certain periods do we observe prolonged upswings, whereas in other periods – notwithstanding all the enormous efforts of interested macroeconomic actors – economic development is accompanied by prolonged depressions? What gets out of order in social and economic mechanisms? This first issue offers a wide panorama of views on the Kondratieff waves' phenomenon; here one can also find information on Kondratieff's life and works. This edition will be useful for economists, social scientists, as well as for a wide circle of those interested in the problems of the past, present, and future of world economics and globalization.
Time series of US patents per million inhabitants show cyclic structures which can be attributed to the different knowledge-generating paradigms that drive innovation systems. The changes in the slopes between the waves can be used to indicate efficiencies in the generation of knowledge. When knowledge-generating systems are associated with idem innovation systems, the efficiency of the latter can be modeled in terms of interactions among dimensions (for example, in terms of university–industry–government relations). The resulting model predicts an increase in efficiency with an increasing number of dimensions due to the effects of self-organization among them. The dynamics of the knowledge-generating cycles can be analyzed in terms of Fibonacci numbers; successive cycles are expected to exhibit shorter life cycles than previous ones. This perspective enables us to forecast the expected dates of future paradigm changes.