Search results

This bibliometric mapping study aimed to provide comprehensive insights into the global research landscape of cybernetics. Utilizing the biblioshiny function in R Studio, we conducted an analysis spanning 1958 to 2023, sourcing data from Scopus. This research focuses on key terms such as cybernetics, cybernetics systems, complex adaptive systems, viable system models (VSM), agent-based modeling, feedback loops and complexity systems.

The analysis leveraged R Studio’s biblioshiny function to perform bibliometric mapping. Keyword searches were conducted within titles, abstracts and keywords, targeting terms central to cybernetics. The timespan, 1958–2023, provides a comprehensive overview of the evolution of cybernetics-related literature. The data were extracted from Scopus to ensure a robust and widely recognized source.

The results revealed a rich and interconnected global research network in cybernetics. The word cloud analysis highlights prominent terms such as “agent-based modeling,” “complex adaptive systems,” “feedback loop,” “viable system model” and “cybernetics.” Notably, the journal Kybernetes has emerged as a focal point, with significant citations, solidifying its position as a key source within the cybernetics research domain. The bibliometric map provides visual clarity regarding the relationships between various concepts and their evolution over time.

This study contributes original insights by employing advanced bibliometric techniques in R Studio to map the cybernetics research landscape. The comprehensive analysis sheds light on the evolution of key concepts and the global collaborative networks shaping cybernetics research. The identification of influential sources, such as Kybernetes, adds value to researchers seeking to navigate and contribute to the dynamic field of cybernetics. Furthermore, this study highlights that cybernetics not only provides a useful framework for understanding and managing major economic shocks but also offers perspectives for understanding phenomena in various fields such as economics, medicine, environmental sciences and climate change.

Economic cybernetics – a very important branch of cybernetics – has elaborated and founded concepts, models, techniques and methods for the investigation both qualitatively and quantitatively of the laws of optimal balanced and proportional growth of the components of the national economy system. At the same time, economic cybernetics performs the role of a “Dialog” between Microsystems and Macrosystems, thus allowing a better understanding and management of the economic mechanism as a whole. The economic approach of economic phenomena and processes of organising and managing economic systems, of achieving the unity between micro‐ and macro‐economics is an obvious reality, confirmed by the results obtained in economic practice. The application of the methods proper to economic cybernetics, in order to find solutions to problems concerning organisation and management of the economy, the design of economic growth trajectories, the achievement of a permanent dialogue between decision and its component of the management of society, is discussed.

The purpose of this paper is to propose an economic cybernetics model based on the grey differential equation GM(1,N) for China's high-tech industries and provide the necessary support to assist high-tech industries management departments with their policy making.

Based on the principle of grey differential equation GM(1,N), the grey differential equations of five high-tech industries in China are established using the net fixed assets, labor quantity and patent application quantity as cybernetics variables. After the discretization and first-order subtraction reduction to the simultaneous equation of the five grey models, a linear cybernetics model is resulted in. The structure parameters in the cybernetics system show explicit economic significance and can be identified through least square principle. At last, the actual data in 2004-2010 are introduced to empirically analyze the high-tech industrial system in China.

The cybernetics system for China's high-tech industries are stable, observable, and controllable. On the whole, China's high-tech industries show higher output coefficients of the patent application quantity than those of net fixed assets and labor quantity. This suggests that China's industry development mainly depends on technological innovation rather than capital or labor inputs. It is expected that the total output value of China's high-tech industries will grow at an average annual rate of 15 percent in 2011-2015, with contributions of pharmaceuticals, aircraft and spacecraft, electronic and telecommunication equipments, computers and office equipments, medical equipments and meters by 21, 16, 13, 10, and 28 percent, respectively. In addition, pharmaceuticals, as well as medical equipments and meters, present upward proportions in the gross of Chinese high-tech industries significantly. Electronic and telecommunication equipments, plus computers and office equipments exhibit an obvious decreasing proportion. The proportion of the output value of aircraft and spacecraft is basically stable.

Empirical analysis results are helpful for related management departments to formulate reasonable industrial policies to keep the sustained and stable development of the high-tech industries in China.

Based on the grey differential equation GM(1,N), this research puts forward an economic cybernetics model for the high-tech industries in China. This model is applicable to the economic system with small sample data set.

The purpose of this paper is to focus on the adjustment of the GM(1, 2) errors for financial data series that measures changes in the public sector financial indicators, taking into account that the errors in grey models remain a key problem in reconstructing the original data series.

Adjusting the errors in grey models must follow some rules that most often cannot be determined based on the chaotic trends they register in reconstructing data series. In order to ensure the adjustment of these errors, for improving the robustness of GM(1, 2), was constructed an adaptive fuzzy controller which is based on two input variables and one output variable. The input variables in the adaptive fuzzy controller are: the absolute error ε i 0 ( k ) [ % ] of GM(1, 2), and the distance between two values x i 0 ( k ) [ % ] , while the output variable is the error adjustment A ε i 0 ( k ) [ % ] determined with the help of the above-mentioned input variables.

The adaptive fuzzy controller has the advantage that sets the values for error adjustments by the intensity (size) of the errors, in this way being possible to determine the value adjustments for each element of the reconstructed financial data series.

To ensure a robust process of planning the financial resources, the available financial data are used for long periods of time, in order to notice the trend of the financial indicators that need to be planned. In this context, the financial data series could be reconstituted using grey models that are based on sequences of financial data that best describe the status of the analyzed indicators and the status of the relevant factors of influence. In this context, the present study proposes the construction of a fuzzy adaptive controller that with the help of the output variable will ensure the error’s adjustment in the reconstituted data series with GM(1, 2).

The present paper tries to give a new vision on the firm's future evolution forecasting. By taking into account some of the current values of its symptoms and applying one of the most used models in the grey systems theory, namely the GM(1,1), the predictions related to its future symptoms' values can be determined. Having these projected values and the grey economic-financial matrix, K, the future diseases that can hit a company can be depicted along with their causes. The paper aims to discuss these issues.

Forecasting the future development of a firm is always an important issue in firm's survival in nowadays economy. Most of all, it is extremely important to be aware all the time about the inner and outer factors than can make a difference between a successful and a bankrupt firm. For this, here the authors have used three GM(1,1) models for forecasting the future symptoms (expressed through financial indicators) and performance indicator of a firm. Each time, based on the determined accuracy rate, a specific GM model has been chosen for every indicator's forecasting.

Considering some previous researches and findings in bankruptcy modelling and diagnosis, this paper enlarges their applicability by adding the possibility to make future predictions on the indicators' evolution and to observe and to better manage their causes. As it was expected, the GM(1,1) models used for the forecasting of the various time series variables taken into account were different from one case to another, choosing the best-specific model for each variable case conducted to more accurate data-fit, with direct results in the causes hierarchy.

By knowing the main causes that determine a certain state in firms' development and understanding them, the manager can action upon them in a manner that can make the difference between a bankrupt and a real successful firm.

The paper succeeds in enlarging the view regarding bankruptcy forecasting by adding a dynamic view over the considered variables. If, in most of the cases when facing with financial forecasting, a single model is used for predictions, here the best GM model has been chosen for each variable based on the obtained accuracy rate. The results are concluding.

Lange's rejection of bureaucratic socialism can be traced throughout his works, from the very beginning to the end. Planners and economic agents have to act under mandatory rules provided by economic theory to avoid the risk of an autocratic regime detached from the people's needs. Drawing upon this premise, Walrasian theory played a pivotal role to introduce rules to tie the hands of socialist bureaucrats based on scientific, economic theory. In this sense, Lange's anti-bureaucratism and Walrasianism descended from a similar political premise. However, the coexistence of conflicting views into the same theory (such as Marxian and Walrasian theories) also implied potential risks, mainly when socialist democracy is conceived in technocratic terms and Marxian theory assimilates to natural sciences.

This paper aims to investigate and formulate several business models (BM) for various energy communities (EC) members: prosumers, storage facilities, electric vehicle (EV) charging stations, aggregators and local markets.

One of the flexibility drivers is triggered by avoiding the cost and maximizing value that consists of delivering a service such as increasing generation or reducing consumption when it is valued most. The transition to greener economies led to the emergence of aggregators that aggregate bits of flexibility and handle the interest of their providers, e.g. small entities such as consumers, prosumers and other small service providers. On one hand, the research method consists of formulating six BM and implementing a BM that includes several consumers and an aggregator, namely, scheduling the household electricity consumption (downstream) and using flexibility to obtain revenue or avoid the cost. This is usually performed by reducing or shifting the consumption from peak to off-peak hours when the energy is cheaper. Thus, the role of aggregators in EC is significant as they intermediate small-scale energy threads and large entities' requirements, such as grid operators or retailers. On the other hand, in the proposed BM, the aggregators' strategy (upstream) will be to minimize the cost of electricity procurement using consumers’ flexibility. They set up markets to buy flexibility that is valued as long as their costs are reduced.

Interesting insights are revealed, such as when the flexibility price doubles, the deficit coverage increases from 62% to 91% and both parties, consumers and retailers obtain financial benefits from the local market.

One of the limitations of using the potential of flexibility is related to the high costs that are necessary to implement direct load control. Another issue is related to the data privacy aspects related to the breakdown of electricity consumption. Furthermore, data availability for scientific research is limited. However, this study expects that new BM for various EC members will emerge in the future largely depending on Information Communications and Technology developments.

An implementation of a local flexibility market (LFM) using 114 apartments with flexible loads is proposed, demonstrating the gains obtained from trading flexibility. For LFM simulation, this study considers exemplifying a BM using 114 apartments located in a multi-apartment building representing a small urban EC situated in the New England region in North America. Open data recorded in 2016 is provided by UMassTraceRepository.

As a novelty, six BM are proposed considering a bottom-up approach and including various EC members.

Carbon dioxide emissions affect the environment, presenting major implications for sustainable development and consequently model climate change policies. The main aim of the paper is to highlight the factors leading to CO2 emissions in Latin America.

The analysis was performed using data for 1990–2020 and panel regression and STATA software.

The results highlighted that the variables have significantly influence CO2 emissions in case of the countries in the sample.

The novelty of the paper consists in using all financial inflows of together (foreign direct investment, official development assistance and remittences), Latin America heavily in-flowed with remittances from the USA. Since Latin America is enriched with forest areas, the authors also covered this variable in the estimations. Urbanization and transportation are induced by remittance inflows, thus wellbeing was incorporated in the model. The conclusion of the study demonstrates the need for complex measures involving public-private partnerships, public awareness of the need for energy transition and the involvement of foreign-owned companies that must not only pursue their own interests but also generate positive economic, environmental, and social externalities in host countries.