Cohen’s tables and econometric modelling of socio-economic systems
Annotation. This thesis explores the application of Cohen’s Tables and econometric modelling in their respective fields of neuropsychology and economics. The paper also discusses the role of econometric modelling in forecasting and analyzing socio-economic processes. The work emphasizes the commonality between these two approaches—both aim to provide data-driven insights for making informed decisions in their respective domains.Given these perspectives, a promising direction for future research is the integration of these two modeling methods in enterprise development management.
The relevance of studying the impact of cognitive features in the perception and processing of information by economic agents lies in its potential to improve decision-making processes and enhance the functioning of socio-economic systems. Understanding these cognitive aspects can provide valuable insights into how economic agents interact with data, make choices, and influence overall economic outcomes. [1, 2]
Cohen’s Tables, introduced in the 1960s, are tools for assessing cognitive changes typically used in neuropsychology and psychology. They serve to measure changes in cognitive functions such as memory, attention, perception, and problem-solving. Cohen’s Tables help standardize and objectively measure these indicators, allowing researchers and practitioners to analyze the dynamics of cognitive changes in different population groups, for example, when treating neuropsychological disorders or assessing the consequences of brain injuries. The Cohen’s Tables method is also used in scientific research related to neuroplasticity and rehabilitation. However, recent advancements have led to the development of more sophisticated tools. For example, the Tablet-Based Cognitive Assessment Tool (TabCAT), developed at the University of California, San Francisco, offers tablet-based neuropsychological tests that assess executive function, memory, visuospatial skills, and socioemotional functions. The platform is available in 10 languages, enhancing its accessibility for diverse populations. This technological advancement allows for broader and more efficient cognitive assessments, facilitating quicker and more comprehensive analysisof socio-economic systems [3].
On the other hand, econometric modelling is widely used to construct models that describe economic processes through statistical methods. It helps identify relationships between economic variables and predict the future development of socio-economic processes. This approach examines important factors such as GDP growth, unemployment rate, inflation, consumer spending, investments, and other key indicators. Econometric models not only allow for the analysis of current economic conditions but also provide accurate forecasts about the future state of the economy based on historical data. This plays a crucial role for governments and businesses in developing strategies and making management decisions.
For instance, a notable study by Ilyash et al. (2025) employs causal econometric modelling to examine the relationships between gross value added (GVA) and production factors in Ukraine’s manufacturing sector. The research utilizes a multiplicative power production function, incorporating labor costs, fixed assets, and capital investments as key variables. The findings highlight that labor costs significantly influence GVA, underscoring the importance of investment in technological development and human capital to bolster economic growth [4].
Additionally, McHenry (2023) focus on assessing the innovative potential of regional socio-economic systems through econometric modelling. The study emphasizes the necessity of evaluating innovative capacity, considering the impact of digital technologies and regional competitive advantages. The authors advocate for a targeted innovative system to drive long-term development, offering valuable insights for policymakers aiming to enhance regional economic performance [5].
These studies exemplify the dynamic application of econometric models in understanding and forecasting socio-economic processes, highlighting their relevance in both policy formulation and economic strategy development ofsocio-economicsystems. In this context, econometric models allow for the identification of critical economic indicators and provide a means for accurate forecasting, helping organizations and governments make informed decisions to optimize resource allocation and respond to economic changes.
Thus, despite the difference in areas of application (neuropsychology and economics), both Cohen’s Tables and econometric modelling play key roles in research and practical activities, helping to make informed decisions based on accurate data and forecasts. While Cohen’s Tables are instrumental in cognitive assessments and understanding human behavior, econometric modelling is essential in analyzing and forecasting complex socio-economic systems. Both approaches contribute to their respective fields by providing reliable methods for analyzing trends and making data-driven decisions.
Given these perspectives, a promising direction for future research is the integration of these two modelling methods in enterprise development management. This would enable consideration of cognitive components in decision-making processes and allow for tracking their impact on the performance of socio-economic systems through econometric methods.
Література
- Turlakova S., Lohvinenko B. Artificial intelligence tools for managing the behavior of economic agents at micro level. Neuro-Fuzzy Modelling Techniques in Economics. 2023. № 12. P. 3–39. URL: http://doi.org/10.33111/nfmte.2023.003.
- Reznikov R., Turlakova S. Importance of machine learning and data science in modern business. 2024. SSRN. https://doi.org/10.2139/ssrn.4851005.
- National Institute on Aging. (n.d.). Tablet-based cognitive assessments for Alzheimer’s and dementia research. U.S. Department of Health & Human Services. URL: https://www.nia.nih.gov/research/alzheimers-dementia-outreach-recruitment-engagement-resources/tablet-based-cognitive.
- Ilyash O., Chernousova Z., Fartushnyi I., Sachenko A. Causal-econometric modelling of the relationships between gross value added and production factors in the development of Ukraine’s manufacturing. Economic Analysis. 2025. №35(1). URL: https://www.econa.org.ua/index.php/econa/article/view/6236.
- McHenry M. S., Mukherjee D., Bhavnani S., Kirolos A., Piper J. D., Crespo-Llado M. M., Gladstone M. J. The current landscape and future of tablet-based cognitive assessments for children in low-resourced settings. PLOS Digital Health, 2(2), e0000196. 2023. URL: https://doi.org/10.1371/journal.pdig.0000196.