Machine learning algorithms can now outperform classic economic models in predicting quantities ranging from bargaining outcomes, to choice under uncertainty, to an individual's future jobs and wages. Yet this predictive accuracy comes at a cost: most machine learning algorithms function as black boxes, offering little insight into \emph{why} outcomes occur. This article asks whether machine learning can guide the development of new economic theories. Economic models serve an important purpose beyond prediction -- they uncover the general mechanisms behind observed behaviors. A model that identifies the causal pathways of economic development is more valuable than one that merely predicts which countries will escape poverty, because it enables policymakers to encourage that development in countries where it might not have happened otherwise. Similarly, a model that predicts imperfectly across many domains can be more valuable than one that is highly accurate in a specific domain, since the former allows insights and data obtained from one setting to inform decisions and policy in another. Applying machine learning algorithms off-the-shelf is unlikely to yield such models. But recent work shows that, when reconceived with the aims of an economic modeler in mind, machine learning methods can improve both prediction and understanding. These approaches range from adversarially training algorithms to expose the limits of existing models, to imposing economic theory as a constraint on algorithmic search. Advances in large language models complement these strategies and open new research directions.
Artificial intelligence (AI) has become a powerful tool for economic research, enabling large-scale simulation and policy optimization. However, applying AI effectively requires simulation platforms for scalable training and evaluation-yet existing environments remain limited to simplified, narrowly scoped tasks, falling short of capturing complex economic challenges such as demographic shifts, multi-government coordination, and large-scale agent interactions. To address this gap, we introduce EconGym, a scalable and modular testbed that connects diverse economic tasks with AI algorithms. Grounded in rigorous economic modeling, EconGym implements 11 heterogeneous role types (e.g., households, firms, banks, governments), their interaction mechanisms, and agent models with well-defined observations, actions, and rewards. Users can flexibly compose economic roles with diverse agent algorithms to simulate rich multi-agent trajectories across 25+ economic tasks for AI-driven policy learning and analysis. Experiments show that EconGym supports diverse and cross-domain tasks-such as coordinating fiscal, pension, and monetary policies-and enables benchmarking across AI, economic methods, and hybrids. Results indicate that richer task composition and algorithm diversity expand the policy space, while AI agents guided by classical economic methods perform best in complex settings. EconGym also scales to 10k agents with high realism and efficiency.
The article examines the impact of 16 key parameters of the Georgian economy on economic inequality, using the Perelman model and Ricci flow mathematical methods. The study aims to conduct a deep analysis of the impact of socio-economic challenges and technological progress on the dynamics of the Gini coefficient. The article examines the following parameters: income distribution, productivity (GDP per hour), unemployment rate, investment rate, inflation rate, migration (net negative), education level, social mobility, trade infrastructure, capital flows, innovative activities, access to healthcare, fiscal policy (budget deficit), international trade (turnover relative to GDP), social protection programs, and technological access. The results of the study confirm that technological innovations and social protection programs have a positive impact on reducing inequality. Productivity growth, improving the quality of education, and strengthening R&D investments increase the possibility of inclusive development. Sensitivity analysis shows that social mobility and infrastructure are important factors that affect economic stability. The accuracy of the model is confirmed by high R^2 values (80-90%) and the statistical reliability of the Z-statistic (<0.05). The study uses Ricci flow methods, which allow for a geometric analysis of the transformation of economic parameters in time and space. Recommendations include the strategic introduction of technological progress, the expansion of social protection programs, improving the quality of education, and encouraging international trade, which will contribute to economic sustainability and reduce inequality. The article highlights multifaceted approaches that combine technological innovation and responses to socio-economic challenges to ensure sustainable and inclusive economic development.
This paper examines whether regional connectivity causes return and volatility spillovers and the co-movement of stock exchanges to shift from international to regional markets. Using the China-Pakistan free trade agreement (FTA) of 2006 and the China-Pakista Economic Corridor (CPEC) agreement to represent events of regional connectivity, we test this proposition based on data for two regional stock exchanges (the Pakistan Stock Exchange and Shenzhen StockExchange) and two global markets (the FTSE 100 and Nasdaq). We divide the convergence and co-integration of the stock markets into three phases: overall sample (2001–17), pre-FTA and post-FTA, and pre-CPEC and post-CPEC. Applying a GARCH (1, 1) model, co-integration, Granger causality andseasonality, we find that regional connectivity causes return and volatility spillovers and co-movements in the Pakistan Stock Exchange to shift from international markets to regional markets.
Jorge Luis Sánchez Arévalo, Alisson Maxwell Ferreira de Andrade, Elisabeth de Oliveira Vendramin
The systemic risk caused by COVID-19 affected all sectors of the economy, thus showing the vulnerability of some sectors in comparison to others. In this context, the supply shock experienced by the iron ore sector has drawn attention and resulted in a price increase. Linked to this, and in a negative way, oil prices fell due, among other factors, to the price war between producing countries.
In this sense, this study analyses the volatility of the Brazilian stock market indicator in relation to the prices of the aforementioned products and the price of the dollar. The results show the importance of the price formation in these markets for the variation of the indicator. The appreciation of Brent oil and iron ore prices on the Dalian Commodity Exchange (DCE), in China, caused the Ibovespa indicator to move in the same direction. In addition, in statistical terms, the study highlights the great importance of the exchange rate as a determinant in the variation of the indicator and, consequently, affecting the intention to invest.
Laurentiu-Nicolae PRICOPE, Valentin-Marian ANTOHI, Romeo-Victor IONESCU
et al.
Amid the increasingly acute need for systematization and urban social management, Romanian cities are facing transformation attempts, their desideratum being to reach a new level of comfort and safety offered to citizens. All these aspects are in line with the sustainable development goals through the need to create the least polluted cities that offer a healthy standard of living to citizens. Starting from the sustainable development desideratum obtained by orienting urban areas to the needs of the citizen and the community, we intend to analyze through the dispersion method the level of smart cities development in Romania. The mainly resuls consist in the realization of a ranking of the Romanian smart cities.
The labor market in Russia and its regions operates in an unstable socio-economic situation, which can contribute to the emergence of gender disproportion. The study of gender asymmetry is considered the most popular direction during periods of “economic shocks”, since this kind of instability increases the inequality of men and women in various spheres of life. The state of turbulence in which the labor market of Russia as a whole and each of its regions is located contributes to the adaptation of each of its segments to new forms of work. This study examines the results of an analysis of the structure and trends in the dynamics of indexes of employment and unemployment of the male and female population in the Orenburg region for 2016-2021. The choice of the time frame is justified by the uneven development of both the world and the Russian economy. Modern economic realities, in which the labor market exists and develops as a country as a whole and its regions, experiencing a number of shocks, determine the scientific novelty of the issues under consideration.Purpose of the study. The main idea of the work done is the possibility of using the methods of economic and statistical analysis to study dynamic differences in gender structures according to various characteristics, development directions that determine gender asymmetry in the labor market of the Orenburg region, and predict its main indexes for the medium term.Materials and methods. The information base of the study was the statistical information of Orenburgstat, which characterizes gender asymmetry in the labor market of the Orenburg region. To achieve this goal, a set of methods of economic and statistical analysis was applied, including the calculation of indexes of the structure and structural differences, dynamics, identifying trends, forecasting, as well as presenting the results of the study using tables and figures.Results. According to the results of the study, an average portrait of a busy and carefree person by gender was compiled. The characteristics of the male and female population in the labor market of the Orenburg region in 2021 compared to 2016 are highlighted. The study of structural differences was carried out according to the criterion of V. Ryabtsev, during which it was noted that there were no significant changes in the structures of employed men and women. A significant level of differences characterizes the age structure of unemployed men and women in 2021 compared to 2016, as well as the structure of unemployed men by level of education.The study made it possible to assert that in the dynamics of indexes characterizing gender inequality in the labor market of the Orenburg region, unstable changes are observed. The study tested the hypothesis of the presence/absence of trends in the time series of indexes characterizing gender asymmetry in the labor market of the region. For this purpose, one of the modifications of the series criterion was used. The assumption that there is no trend in the dynamics series under consideration has not been confirmed, therefore, the trend exists. Taking into account this circumstance, trend models were constructed, among which, according to the best statistical characteristics, secondorder polynomial models were selected. With the help of the selected polynomials of the second degree, it was determined that in the dynamics of employment and unemployment indexes of the male and female population, downward trends of change prevail. Further, their prospective indexes were calculated.Conclusion. The study made it possible to analyze the main vectors that are emerging in the field of employment and unemployment in the regional labor market by gender, and to predict their main indexes for the medium term. In the dynamics of the number of both male and female labor force in the Orenburg region, unstable trends will be observed in the future. The number of women employed in the region’s economy will grow; the number of unemployed women will decrease. In the forecast period, the number of employed men in the economy of the Orenburg region will change under the influence of a downtrend. The number of unemployed men in the period from 2023 to 2025 will tend to decrease.
We discuss the economic reasons why the predictions of price and return statistical moments in the coming decades, in the best case, will be limited by their averages and volatilities. That limits the accuracy of the forecasts of price and return probabilities by Gaussian distributions. The economic origin of these restrictions lies in the fact that the predictions of the market-based n-th statistical moments of price and return for n=1,2,.., require the description of the economic variables of the n-th order that are determined by sums of the n-th degrees of values or volumes of market trades. The lack of existing models that describe the evolution of the economic variables determined by the sums of the 2nd degrees of market trades results in the fact that even predictions of the volatilities of price and return are very uncertain. One can ignore existing economic barriers that we highlight but cannot overcome or resolve them. The accuracy of predictions of price and return probabilities substantially determines the reliability of asset pricing models and portfolio theories. The restrictions on the accuracy of predictions of price and return statistical moments reduce the reliability and veracity of modern asset pricing and portfolio theories.
Ummya Salma, Md. Fazlul Huq Khan, Md. Masum Billah
This study aims to examine the relationship between Foreign Direct Investment (FDI), personal remittances received, and official development assistance (ODA) in the economic growth of Bangladesh. The study utilizes time series data on Bangladesh from 1976 to 2021. Additionally, this research contributes to the existing literature by introducing the Foreign Capital Depthless Index (FCDI) and exploring its impact on Bangladesh's economic growth. The results of the Vector Error Correction Model (VECM) suggest that the economic growth of Bangladesh depends on FDI, remittances, and aid in the long run. However, these variables do not exhibit a causal relationship with GDP in the short run. The relationship between FCDI and economic growth is positive in the long run. Nevertheless, the presence of these three variables has a more significant impact on the economic growth of Bangladesh
Several tiers of social organization with varying economic and social disparities have been observed. However, a quantitative characterization of the types and the causal mechanisms for the transitions have hardly been explained. While anthropologists have emphasized that gift exchange, rather than market exchange, prevails in traditional societies and shapes social relations, few mathematical studies have explored its consequences for social organizations. In this study, we present a simple model of competitive gift-giving that describes how gifts bring goods to the recipient and honor to the donor, and simulate social change. Numerical simulations and an analysis of the corresponding mean-field theory demonstrate the transitions between the following four phases with different distribution shapes of wealth and social reputation: the band, without economic or social disparities; the tribe, with economic but without social disparities; the chiefdom, with both; and the kingdom, with economic disparity and weak social disparity except for an outlier, namely, the ``monarch''. The emergence of strong disparities is characterized by power law distributions and is attributed to the ``rich get richer'' process. In contrast, the absence of such a process leads to exponential distributions due to random fluctuations. The phases depend on the parameters characterizing the frequency and scale of gift interactions. Our findings provide quantitative criteria for classifying social organizations based on economic and social disparities, consistent with anthropological theory and empirical observations. Thus, we propose empirically measurable explanatory variables and characteristics for the evolution of social organizations. The constructive model, guided by social scientific theory, can provide the basic mechanistic explanation of social evolution and integrate theories of the social sciences.
Piotr Teodorowski, Saiqa Ahmed, Naheed Tahir
et al.
Objectives
Public involvement and engagement have been growing within big data research. However, seldom heard voices such as migrant and ethnic minorities communities are often underrepresented. This study explored how Polish and South Asian communities in the United Kingdom could be better included in public involvement and engagement activities.
Approach
We conducted semi-structured interviews with Polish (n=20) and South Asians (n=20) to elicit their views on big data research, public involvement and engagement. We focused on Polish and South Asian communities as they represent some of the United Kingdom’s largest migrant and ethnic minority groups. Data were analysed using inductive thematic analysis. Public advisors were involved in the analysis. They and one of the researchers come from ethnic minority and offered insider insight into participants' perspectives and thus allowing us to unpick the complexity of experiences and backgrounds.
Results
The majority of participants were willing to become involved or engaged in big data research. However, we found there were multiple barriers to involvement, these included: language (especially for those for whom English is the second language); use of jargon by researchers; time restrictions and unfamiliarity with big data or public involvement. Some participants questioned how much migrants could be involved when they were only in the United Kingdom on a temporary basis. The participants made recommendations for how researchers can mitigate these barriers. Awareness-raising activities would allow people to expand their understanding and build their confidence when speaking about big data research in a second language. Participants spoke of the need for researchers to work more closely with local communities, especially with local gatekeepers.
Conclusions
The results indicate that there is no ‘right’ way to involve and engage seldom heard communities around big data research. Researchers need to engage with communities, establish trust and develop a long-lasting relationships. These partnerships should move beyond single projects and aim to benefit both researchers and seldom heard communities.
The issues of developing the potential of museums in the context of international tourism have investigating in article. The purpose of writing the article is to determine the potential of museums in the development of international tourism and directions for its effective use in the light of modern trends. The subject of research in the article is the use of the informational, cultural, historical and scientific potential of museums for the development of the economy and the activation of international tourism. The dynamics and peculiarities of the development of cultural tourism in modern conditions are studied, the role of museums in these processes have outlined. It was established that the presence of museums and their effective functioning is an important competitive advantage of the country in the context of the development of international tourism, and the level of tourism development in the territory of the location of museums creates, among other things, economic prerequisites for the development of their resource potential. The specific features of the museum as an object of research of its potential are systematized. The interpretation of the concept of the potential of the museum in the context of its potential contribution to the development of tourism in the territory of its location due to the existing cultural, historical, scientific values, which depends on the level of integration into the environment of the tourist sphere of the region based on the establishment of close communication links between business, culture, science and power, is proposed.
The article deals theoretical approaches to the definition of the principles of financial strategy of the company, on the basis of their systematization of the author offers his version of the principles would guide the financial strategy of the entity.
EU post-socialist countries are nowadays the epicenter of urban shrinkage, despite economic growth trajectories reported during the last decades. However, systematic assessments of urban shrinkage patterns for this part of the continent are surprisingly insufficiently addressed in the literature, and the relationship between urban demographic decline/growth and economic decline/growth is still to be understood. This paper first delivers a state-of-the-art of the peculiarities of urban shrinkage in East-Central EU countries. Secondly, it employs an analysis grid to assess severity, prevalence, persistence, speed and regional incidence of urban decline in Romania—one of the most affected post-socialist countries within the European Union. Thirdly, it explores the statistical association between urban shrinkage severity and economic growth, on one hand, and between urban shrinkage severity and municipality revenues, on the other. Results show that urban shrinkage is currently increasing in prevalence and severity among Romanian cities, thus continuing an alarming trend that started in 1990. Secondly, the results pinpoint a statistically significant association between demographic shrinkage, local economic output and municipalities’ own-source revenues. However, the size effects are rather weak, suggesting a more nuanced relationship between economic and demographic urban growth than that predicted by some theories of urban change.
An aim of this study is to introduce a practitioner-friendly behavior model. Few theories of health behavior explicitly take the effect of social norms on behavior into account. Generally, theories that do take social norms into account assume that the effect of social norms on behavior operates through motivation. We use the Fogg Behavior Model (FBM), a behavior model that is new to public health, to explore whether social norms are associated with modern contraceptive use among Nigerian women, and whether they affect behavior through motivation or through ability. In other words, do social norms that discourage contraception lower women’s motivation to use contraception or do they lower women’s ability to use contraception. This study uses data from a cross-sectional household survey of Nigerian women, ages 14–24. The survey collected data on socio-economic and demographic characteristics of women, whether they were sexually experienced, and whether they used contraception. Modern contraceptive use was the outcome of interest for the study. The survey also collected data on social norms around premarital sex and contraceptive use. Multivariate logistic regression was used for the analysis. After adjusting for a range of socio-economic and demographic variables, we found that social norms that discourage contraception had a statistically significant negative association with contraceptive use (aOR = 0.90, p < 0.001). The analysis found that the negative association between social norms and contraceptive use remained statistically significant after controlling for motivation but did not remain statistically significant after controlling for ability. These findings suggest that social norms may affect contraceptive use in Nigeria through ability rather than motivation. In terms of programmatic implications, these finding suggest that public health interventions may be able to counter the negative effects of social norms that discourage contraceptive use by increasing women’s ability to practice contraception.
Abstract Households are responsible for a significant share of global greenhouse emissions. Hence, academic and policy discourses highlight behavioral changes among households as an essential strategy for combating climate change. However, formal models used to assess economic impacts of energy policies face limitations in tracing cumulative impacts of adaptive behavior of diverse households. The past decade has witnessed a proliferation of agent-based simulation models that quantify behavioral climate change mitigation relying on social science theories and micro-level survey data. Yet, these behaviorally-rich models usually operate on a small scale of neighborhoods, towns, and small regions, ignoring macro-scale social institutions such as international markets and rarely covering large areas relevant for climate change mitigation policy. This paper presents a methodology to scale up behavioral changes among heterogeneous individuals regarding energy choices while tracing their macroeconomic and cross-sectoral impacts. To achieve this goal, we combine the strengths of top-down computable general equilibrium models and bottom-up agent-based models. We illustrate the integration process of these two alien modeling approaches by linking data-rich macroeconomic with micro-behavioral models. Following a three-step approach, we investigate the dynamics of cumulative impacts of changes in individual energy use under three behavioral scenarios. Our findings demonstrate that the regional dimension is important in a low-carbon economy transition. Heterogeneity in individual socio-demographics (e.g. education and age), structural characteristics (e.g. type and size of dwellings), behavioral and social traits (e.g. awareness and personal norms), and social interactions amplify these differences, causing nonlinearities in diffusion of green investments among households and macro-economic dynamics.
Wearing a protective face covering can reduce the spread of COVID-19, but Americans’ compliance with wearing a mask is uneven. The purpose of this study is to examine the association between health determinants (Health Behaviors, Clinical Care, Social and Economic Conditions, and the Physical Environment) and mask wearing at the county level. Data were collected from publicly available sources, including the County Health Rankings and the New York Times. The dependent variable was the percent of county residents who reported frequently or always wearing a mask when in public. County demographics and voting patterns served as controls. Two-levels random effects regression models were used to examine the study hypotheses. Results indicate that, after considering the effects of the controls, Health Behaviors were positively associated with mask wearing, the Physical Environment held a negative association, and Clinical Care and Social and Behavioral Factors were unrelated. Results indicate that patterns of healthy behaviors can help predict compliance with public health mandates that can help reduce the spread of COVID-19. From an instutitional theory perspective, the data suggest counties develop collective values and norms around health. Thus, public health officials can seek to alter governance structures and normative behaviors to improve healthy behaviors.
Research background: The theoretical basis of the study derives from the assumptions of international entrepreneurship combining theories of entrepreneurship and theories of international business. The identification of entrepreneurship determinants and attributes was based on the economic, socio — cultural, as well as psychological approach to entrepreneurship. Purpose of the article: The aim of the article is to verify how demographic and basic traits of an entrepreneur affect the pace of the internationalization of firms from Poland. Methods: The study was based on CATI method. The article uses data collected on the basis of a study conducted on a sample of 355 companies from Poland. The research methods applied are a critical analysis of prior research, which allowed to identify the research gap and develop research hypotheses. In the empirical part, statistical methods were applied, including descriptive statistics and multidimensional regression. Findings & value added: The logistic regression estimation allows to confirm three hypotheses. With the age of the entrepreneur, the pace of internationalization of the firm increases. The pace of internationalization of the firm increases with the level of education of the entrepreneur. The fact that an entrepreneur belongs to a national minority increases the pace of the internationalizaOeconomia Copernicana, 12(2), 399–424 400 tion of the firm. Results of two-sample t-test confirm that firms whose entrepreneurs have higher entrepreneurial competences internationalize faster and earlier. The added value of the article is the combination of socio-demographic and psycho-cognitive characteristics of the entrepreneur with internationalisation. Applying this approach to a sample of firms from Poland (CEE market) contributes to research on international entrepreneurship in a thematic and geographical sense. The results of the study are of an applied nature. They can be addressed to many recipients: entrepreneurs, policymakers, educators, entities responsible for shaping and promoting entrepreneurship on both the micro and macro levels.