Системное моделирование социально-экономический процессов: труды 42-ой международной научной школы-семинара имени академика С.С.Шаталина
This article examines the industrial wastes and environmental effects of Soviet technological development through the history of the Karelian Isthmus, a border territory that had previously been Finnish. Focusing primarily on the history of two large enterprises – the Svetogorskii (former Enso) and Sovetskii (former Johannes) pulp and paper making plants, the authors illustrate the polluting nature of the Soviet economy in the 1940s-1980s. We contend that from the very beginning, important as they were for the USSR, the enterprises of the Isthmus were built into a system of shortages of techniques and materials that contributed to the hectic fulfillment of the plan. Producing pulp and pulp-based products remained a priority during the whole Soviet period. On the level of industrial enterprises, the Soviet system revealed itself as incapable of solving the problem of pollution and wasting. After waste treatment facilities developed by Soviet engineers in the 1960s turned out to be inadequate for dealing with increasing pollution, the Soviet authorities called on Finnish companies to carry out substantial modernization of a few enterprises on the Isthmus. This helped the modernized plants remain functioning in the age of economic crisis at the end of the Soviet epoch. Old problems, however, such as shortages and lack of expertise, remained pivotal, while new sources of pollution, such as carbon emissions, appeared. As a result, the level of contamination was still high and led to negative environmental impacts.
Workshop concentrates on an interdisciplinary approach to modelling human behavior incorporating data mining and expert knowledge from behavioral sciences. Data analysis results extracted from clean data of laboratory experiments will be compared with noisy industrial datasets from the web e.g. Insights from behavioral sciences will help data scientists. Behavior scientists will see new inspirations to research from industrial data science. Market leaders in Big Data, as Microsoft, Facebook, and Google, have already realized the importance of experimental economics know-how for their business.
In Experimental Economics, although financial rewards restrict subjects preferences in experiments, exclusive application of analytical game theory is not enough to explain the collected data. It calls for the development and evaluation of more sophisticated models. The more data is used for evaluation, the more statistical significance can be achieved. Since large amounts of behavioral data are required to scan for regularities, along with automated agents needed to simulate and intervene in human interactions, Machine Learning is the tool of choice for research in Experimental Economics. This workshop is aimed at bringing together researchers from both Data Analysis and Economics in order to achieve mutually beneficial results.
The paper argues that when developing an explanatory model of the early-stage entrepreneurial activity level (measured by total index of early entrepreneurial activity - TEA) one should consider the ‘path dependency’ of the ‘institutional matrix’ of different societies. Otherwise one could wonder why some theoretical models of TEA determining factors, as provided by a lot of studies, are not statistically significant for younger market systems and entrepreneurship in transitional economies. However, comparing Global Entrepreneurship Monitor (GEM) data with the scope of official statistics provides a deeper insight into adults’ intrinsic incentives to become entrepreneurial. A statistical analysis of national TEA levels does not support the thesis that TEA levels, and structure, change under economic slowdown. Therefore, it seems logical to suggest that to interpret the TEA level it is important to examine some fundamental specific of different types of national markets rather than just the actual economic situation itself. When testing this hypothesis, the authors compared the characteristics of GEM countries with stable, high or low TEA levels. A Fisher’s linear discriminant analysis (FLDA) is used to examine whether different groups of countries can be distinguished by linear combinations of predictor variables and to determine which variables are responsible for this separation. The FLDA model explains the parabolic form of the relation between the level of economic development and TEA. A database of independent variables includes some different quantitative, ordinal and nominal variables determining the context of the national capital accumulation history. Using FLDA, we argue, one might foresee future tendencies of TEA - not only for GEM participating countries.
The paper describes the results of years action of the Constitution of the Russian Federation, determines the need to continue the development of the constitutional provisions, on the historical example shows the need for constitutional development in the constitutional dialogue.