The source of emergence of sustainability problems related to social and economic systems lies in factors of complexity and uncertainty. The presence of sufficient sustainability in the performance of social and economic objects characterizes effectiveness of adaptation mechanisms and their abilities in risks overcoming. In our opinion, sustainability assessment is possible on the basis of the theory of elasticity connected to adaptation potential formation. The article proposes and tests authors' approach to social and economic systems sustainability assessment. The used criteria were as follows: functionals of difference or relation between real and ideal characteristics of system performance quality. To estimate connection between resource perturbations and deviations from program reference points of system development the elasticity functions were elaborated. They are derived by correspondent modification of production functions. Recommendations for ways of perfecting the mechanisms running programs of social and economic systems development on different levels are proposed.
The purpose of this work is to assess the effectiveness of achieving planned investment indicators within the framework of the national program «Digital Economy of the Russian Federation» and the adequacy of investments in the digitalization of the economy to form positive dynamics of Russia's GDP. The hypothesis is tested about the presence of influence, along with the indicators of the functioning of traditional sectors of the economy, of indicators of digital sectors of the economy and indicators of investments in digital transformation in Russia. The methodology for such calculation is based on the theory of elasticity, which can be used to analyze the efficiency of resources (investments) in the case of alleged underfunding of a certain economic entity, that took place in the case of the specified program. This technique involves the construction of a Cobb-Douglas production function. The data of Russian statistical compilations in the regional context for the period from 2015 to 2018 were used as an information base for calculating and constructing a production function. Within the adopted specification of the cross-sectional regression model, the parameters of the production function were determined for each year within the specified period. Also, the predicted values of the indicators of the used information base for 2019 and 2020 were determined using the linear regression method, and, proceeding from them, parameters of the production function were determined. Due to the incompatibility of data on the indicator of internal costs for the development of the digital economy in the program «Digital Economy of the Russian Federation» and the statistical indicator of the cost of information and communication technologies, it was necessary to calculate the ratio between these indicators. The solution to this problem showed that these indicators are in good agreement with each other with a difference of only a few percent. The final result of the study is an assessment of GDP losses while maintaining the dynamics of digitalization costs observed in 2015-2018, suggesting continuation of the trend towards the Digital Economy of the Russian Federation program being underfunded in 2019 and 2020.
The offered method allows to define the requirements for testing measurements accuracy and optimize the cost of testing of complex electronic devices and with it to provide the required reliability of the testing results.Keywords: complex (multivariable) testing of products, simulation, testing errors of the first and second kind. IntroductionThe recommendations for a solution of the urgent problem of testing complex technical products are given in [I]. The article offers a method of optimization of complete testing of mass produced wares based on the economic criterion. 'The method implies testing several parameters which can be independent or statistically dependent (with correlation factor determined).Let us assume that the criterion of optimum product testing is the costs involved in testing C. 'These costs include the costs of testing itself and losses from testing errors. It is common practice to characterize the quality of testing (the factor of merit) by probability of a testing error of the first kind PI (risk of the manufacturer) and by probability of a testing error of the second kind P2 (risk of the consumer) [ 11.PI characterizes an average share of falsely rejected (as faulty) products in the total amount of products tested. A testing error of the first kind results from inaccuracy of testing measurements. P2 characterizes an average share of actually faulty products in the total amount of products delivered to the consumer as functioning. Errors of the second kind can result from inaccuracy of measurements and an irrational choice of number of tested parameters (see the example).The losses on account of testing errors of the second kind are losses from using malfunctioning products. Obviously, they are proportional to probability P2. These losses can be evaluated only by the consumer.We will name the testing procedure optimum if the costs of carrying out the testing are minimum and the inequality is valid:Here Pzlnax is the maximum permissible value of probability of a testing error of the second kind. The value Pzniax should be determined by both the consumer and the manufacturer of products.Further optimization is understood as choice of the best values of parameters for the testing procedure which would minimize the costs C and ensure the fulfillment of the inequality (1).The optimized parameters of the testing procedure are the permissible inaccuracy of measurements while testing and the values of checking tolerance (1). Similarly, a problem of choice of an optimum number of tested parameters can be solved. The solution of the problem.To determine the costs C let us observe the final stage of manufacturing, which includes adjustment and testing of all products. After adjustment N products comes to testing during a time interval T, of them N f are actually functioning and N ni are malfunctioning. It is supposed that malfunctioning products have latent defects, which can be revealed or not revealed while testing. 0-7803-736 1 -8/01/$17.00 02002 IEEE 118
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