Hasil untuk "Economic history and conditions"

K. Deutsch

Elham Rafiei-Sardooi, A. Azareh, Bahram Choubin et al.

Abstract With the growth of cities, urban flooding has increasingly become an issue for regional and national governments. The destructive effects of floods are magnified in cities. Accurate models of urban flood susceptibility are required to mitigate this hazard mitigation and build resilience in cities. In this paper, we evaluate flood riskin Jiroft city, Iran, using a combination of machine learning and decision-making methods. Flood hazard maps were created using three state-of-the-art machine learning methods (support vector machine, random forest, and boosted regression tree). The metadata supporting our analysis comprises 218 flood inundation points and a variety of derived factors: slope aspect, elevation, slope angle, rainfall, distance to streets, distance to rivers, land use/land cover, distance to urban drainages, urban drainage density, and curve number. We then employed the TOPSIS decision-making tool for urban flood vulnerability analysis, which is based on socio-economic factors such as building density, population density, building history, and socio-economic conditions. Finally, we derived an urban flood risk map for Jiroft based on flood hazard and vulnerability maps. Of the three models tested, the random forest model yielded the most accurate map. The results indicate that urban drainage density and distance to urban drainages are the most important factors in urban flood hazard modeling. As might be expected, areas with a high or very high population density are most vulnerable to flooding. These results show that flood risk mapping provide insights for priority planning in flood risk management, especially in areas with limited hydrological data.

Venkata Rajesh Chundru, Shreshta Rajakumar Deshpande, Stanislav A Gankov

The existing literature on Battery Energy Storage Systems (BESS) predominantly focuses on two main areas: control system design aimed at achieving grid stability and the techno-economic analysis of BESS dispatch on power grid. However, with the increasing incorporation of ancillary services into power grids, a more comprehensive approach to energy management systems is required. Such an approach should not only optimize revenue generation from BESS but also ensure the safe, efficient, and reliable operation of lithium-ion batteries. This research seeks to bridge this gap by exploring literature that addresses both the economic and operational dimensions of BESS. Specifically, it examines how economic aspects of grid duty cycles can align with control schemes deployed in BESS systems. This alignment, or synergy, could be instrumental in creating robust digital twins virtual representations of BESS systems that enhance both grid stability and revenue potential. The literature review is organized into five key categories: (1) ancillary services for BESS, exploring support functions that BESS can provide to power grids; (2) control systems developed for real-time BESS power flow management, ensuring smooth operations under dynamic grid conditions; (3) optimization algorithms for BESS dispatch, focusing on efficient energy allocation strategies; (4) techno-economic analyses of BESS and battery systems to assess their financial viability; and (5) digital twin technologies for real-world BESS deployments, enabling advanced predictive maintenance and performance optimization. This review will identify potential synergies, research gaps, and emerging trends, paving the way for future innovations in BESS management and deployment strategies.

Rahul Baxi

AI agents are increasingly granted economic agency (executing trades, managing budgets, negotiating contracts, and spawning sub-agents), yet current frameworks gate this agency on capability benchmarks that are empirically uncorrelated with operational robustness. We introduce the Comprehension-Gated Agent Economy (CGAE), a formal architecture in which an agent's economic permissions are upper-bounded by a verified comprehension function derived from adversarial robustness audits. The gating mechanism operates over three orthogonal robustness dimensions: constraint compliance (measured by CDCT), epistemic integrity (measured by DDFT), and behavioral alignment (measured by AGT), with intrinsic hallucination rates serving as a cross-cutting diagnostic. We define a weakest-link gate function that maps robustness vectors to discrete economic tiers, and prove three properties of the resulting system: (1) bounded economic exposure, ensuring maximum financial liability is a function of verified robustness; (2) incentive-compatible robustness investment, showing rational agents maximize profit by improving robustness rather than scaling capability alone; and (3) monotonic safety scaling, demonstrating that aggregate system safety does not decrease as the economy grows. The architecture includes temporal decay and stochastic re-auditing mechanisms that prevent post-certification drift. CGAE provides the first formal bridge between empirical AI robustness evaluation and economic governance, transforming safety from a regulatory burden into a competitive advantage.

Ekaterina A. Ilyina, Igor E. Poverinov, Svetlana V. Ilyina et al.

Introduction. Promotion of national traditions, cultures and languages is an important component of the foreign policy of countries in the modern conditions of globalization and dynamically developing international relations. The Russian language occupies an important place, being one of the world’s languages with a centuries-old history and rich cultural heritage. The Russian Federation is currently paying great attention to the preservation of the Russian language and the strengthening of its position in the world. In the Democratic Socialist Republic of Sri Lanka the Russian language is not widely spread, but with the development of economic, scientific, educational, cultural international cooperation the interest of Lankans to study the Russian language is growing. The relevance of the study is due to the fact that the expanding trade, economic, humanitarian ties between Russia and Sri Lanka cause the need to study the Russian language and coordinate efforts to interact all participants of the educational process. The development of a set of measures aimed at increasing the number of Russian-speaking citizens, establishing long-term ties with educational organizations of Sri Lanka is an important task in strengthening and developing coopera­tion between Russia and Sri Lanka. The aim of the article is to develop a set of institutional mechanisms and measures aimed at improving the effectiveness of targeted management of the Russian-speaking educational process carried out in Sri Lanka. Materials and Methods. During their research the authors adhered to the following sequence of work: during the preparatory stage data collection was carried out, then a sociological study among teachers and students and a qualitative study using the method of focus groups were conducted, after obtaining the necessary information the data were analyzed and specific recommendations on the functionality of the Russian language department of the linguistic direction of the National Institute of Education of Sri Lanka were developed. Results. The research allowed us to highlight the demand for the study of the Russian language in Sri Lanka and measures for its promotion. Regulations for the formation of new management mechanisms to strengthen the position for the study of the Russian language, taking into account local traditions and peculiarities of the worldview of the population have been identified. Realization of the proposed measures, in our opinion, will provide effective coordination of actions of parties concerned in the study of the Russian culture and language. Discussion and Conclusion. Practical significance lies in the use of the results obtained in the course of scientific research, expeditions to make up a list of systemic measures that will effectively manage Russian-language education in schools of Sri Lanka. The materials of the article can be useful to the authorities in the sphere of education and international cooperation, heads of educational organizations in Sri Lanka in order to improve the level of teaching Russian as a foreign language.

Jorge A. Rodríguez Soto

Los neoclásicos construyeron una síntesis para la economía; usurpando espacios de otras ciencias sociales así, la economía ganó el calificativo imperialista. La unificación implicó la reducción del fenómeno a unidades disipativas, sin perder integridad. Pero en la década de los treinta se rompió la linealidad del paradigma, con el surgimiento de la crítica Keynesiana, que abogaba por un entendimiento macroeconómico, en lugar de micro. En la actualidad se cuestiona la necesidad de microfundamentos para la macroeconomía. Este escrito revisa algunas críticas, cuestionando si el problema es la insistencia en los microfundamentos o la necesidad de microfundamentos distintos. Mostrando cómo la macroeconomía keynesiana es coherente con la psicología y marcando rutas hacia  una teoría con bases cognitivo-conductuales.

Yuzhi Hao, Danyang Xie

This paper pioneers a novel approach to economic and public policy analysis by leveraging multiple Large Language Models (LLMs) as heterogeneous artificial economic agents. We first evaluate five LLMs' economic decision-making capabilities in solving two-period consumption allocation problems under two distinct scenarios: with explicit utility functions and based on intuitive reasoning. While previous research has often simulated heterogeneity by solely varying prompts, our approach harnesses the inherent variations in analytical capabilities across different LLMs to model agents with diverse cognitive traits. Building on these findings, we construct a Multi-LLM-Agent-Based (MLAB) framework by mapping these LLMs to specific educational groups and corresponding income brackets. Using interest-income taxation as a case study, we demonstrate how the MLAB framework can simulate policy impacts across heterogeneous agents, offering a promising new direction for economic and public policy analysis by leveraging LLMs' human-like reasoning capabilities and computational power.

Katarzyna Maciejowska, Arkadiusz Lipiecki, Bartosz Uniejewski

Electricity price forecasts are typically evaluated using accuracy measures such as RMSE and MAE, although these metrics often fail to reflect their economic value in operational decisions. This paper investigates which statistical properties of electricity price forecasts are most relevant for economic performance, using battery energy storage system (BESS) arbitrage as an application. We assess prediction quality along four dimensions: forecast accuracy, intraday error dispersion, association between predicted and realized prices, and the ability to identify daily price extrema. We construct a comprehensive pool of 192 hourly day-ahead electricity price forecasts and use it to evaluate the relationship between proposed quality measures and profits generated for two representative BESS configurations. The results show that traditional accuracy metrics are only weakly correlated with BESS income. At the same time, dispersion- and association-based measures better capture a forecast's economic value by reflecting its ability to reproduce daily price patterns. These findings demonstrate that incorporating complementary evaluation criteria may improve forecast selection and enhance the economic performance of BESS.

Manuel G. Satué, Manuel R. Arahal, Luis F. Acedo et al.

Economic model predictive control has been proposed as a means for solving the unit loading and unit allocation problem in multi-chiller cooling plants. The adjective economic stems from the use of financial cost due to electricity consumption in a time horizon, such is the loss function minimized at each sampling period. The energetic approach is rarely encountered. This article presents for the first time a comparison between the energetic optimization objective and the economic one. The comparison is made on a cooling plant using air-cooled water chillers and a cold storage system. Models developed have been integrated into Simscape, and non-convex mixed optimization methods used to achieve optimal control trajectories for both energetic and economic goals considered separately. The results over several scenarios, and in different seasons, support the consideration of the energetic approach despite the current prevalence of the economic one. The results are dependent on the electric season and the available tariffs. In particular, for the high electric season and considering a representative tariff, the results show that an increment of about 2.15% in energy consumption takes place when using the economic approach instead of the energetic one. On the other hand, a reduction in cost of 2.94% is achieved.

Davit Gondauri

Today, the economy is greatly influenced by Artificial General Intelligence (AGI). The purpose of this paper is to determine the impact of the quantitative relations of AGI on the country's economic parameters. The authors use the analysis of historical data in the research, develop a new mathematical algorithm that refers to the level of AGI development, and conduct a regression analysis. The economic effect of AGI is deduced if it affects the growth of real GDP. As a result of the analysis, it is revealed that there is a positive Pearson correlation between the growth of AGI and real GDP; that is, to increase GDP by 1%, an average increase of 12.5% of AGI is required.

Francis Dennig, Bassel Tarbush

We characterize the outcomes of a canonical deterministic model for the intergenerational transmission of capital that features differential fertility. A fertility function determines the relationship between parental capital and the number of children, and a transmission function determines the relationship between the capital of a parent and that of their children. Together these functions generate an evolving cross-sectional distribution of capital. We establish easy-to-verify conditions on the fertility and transmission functions that guarantee (a) that the dynamical system has a steady state distribution that is either atomless (exhibiting inequality) or degenerate (not exhibiting inequality), and (b) that the system converges to such states from essentially any initial distribution. Our characterization provides new insights into the link between differential fertility and long-run cross-sectional inequality, and it gives rise to novel comparative statics relating the two. We apply our results to several parametric examples and to a model of economic growth that features endogenous differential fertility.

Yinzhu Quan, Zefang Liu

In this paper, we introduce EconLogicQA, a rigorous benchmark designed to assess the sequential reasoning capabilities of large language models (LLMs) within the intricate realms of economics, business, and supply chain management. Diverging from traditional benchmarks that predict subsequent events individually, EconLogicQA poses a more challenging task: it requires models to discern and sequence multiple interconnected events, capturing the complexity of economic logics. EconLogicQA comprises an array of multi-event scenarios derived from economic articles, which necessitate an insightful understanding of both temporal and logical event relationships. Through comprehensive evaluations, we exhibit that EconLogicQA effectively gauges a LLM's proficiency in navigating the sequential complexities inherent in economic contexts. We provide a detailed description of EconLogicQA dataset and shows the outcomes from evaluating the benchmark across various leading-edge LLMs, thereby offering a thorough perspective on their sequential reasoning potential in economic contexts. Our benchmark dataset is available at https://huggingface.co/datasets/yinzhu-quan/econ_logic_qa.

Theophilus G. Baidoo, Ashley Obeng

Inflation remains a persistent challenge for many African countries. This research investigates the critical role of machine learning (ML) in understanding and managing inflation in Ghana, emphasizing its significance for the country's economic stability and growth. Utilizing a comprehensive dataset spanning from 2010 to 2022, the study aims to employ advanced ML models, particularly those adept in time series forecasting, to predict future inflation trends. The methodology is designed to provide accurate and reliable inflation forecasts, offering valuable insights for policymakers and advocating for a shift towards data-driven approaches in economic decision-making. This study aims to significantly advance the academic field of economic analysis by applying machine learning (ML) and offering practical guidance for integrating advanced technological tools into economic governance, ultimately demonstrating ML's potential to enhance Ghana's economic resilience and support sustainable development through effective inflation management.

Anna Josephson, Rodrigo Guerra Su, Greg Collins et al.

The cost of the impacts of climate change have already proven to be larger than previously believed. Understanding the costs and benefits of adapting to the changing climate is necessary to make targeted and appropriate investment decisions. In this paper, we use a narrative review to synthesize the current literature on the economic case for climate adaptation, with the objective of assessing the value (economic and otherwise) of climate change adaptation, as well as the strength of the methods and evidence that have been used to date. We find that skepticism is warranted about many of the estimates about costs and benefits of climate adaptation and their underlying assumptions, due to a range of complexities associated with (1) uncertainty in distinguishing the economic impacts of climate change from seasonal variability; (2) difficulties in non-market valuation; (3) lack of consistent data collection over time at multiple scales; and (4) distributional inequities in access to proactive adaptation and recovery funding. While useful for broad stroke advocacy purposes, these estimates fall short of the refinement and rigor needed to inform investment decision-making, particularly at micro and local scales. Most estimates rely on cost benefit analysis and do not effectively address these issues. An emergent and promising literature tackles alternative estimation strategies and attempts to address some of them, including the complexities of uncertainty and non-market valuation.

Pooja Kansra, Sumit Oberoi

Background Diabetes mellitus is an obtrusive universal health emergency in developed and developing countries, including India. With the exponential rise of epidemiological conditions, the costs of treating and managing diabetes are on an upsurge. This study aimed to estimate the cost of diabetes and determine the determinants of the total cost among diabetic patients. Methods This cross-sectional study was executed in the northern state of Punjab, India. It involves the multi-stage area sampling technique and data was collected through a self-structured questionnaire adapted following the “ WHO STEPS Surveillance ” manual. Mann–Whitney U and Kruskal–Wallis tests were performed to compare the cost differences in socio-demographic variables. Lastly, multiple linear regression was conducted to determine and evaluate the association of the dependent variable with numerous influential determinants. Results The urban respondents' average direct and indirect costs are higher than rural respondents. Age manifests very eccentric results; the highest mean direct outpatient care expenditure of ₹52,104 was incurred by the respondents below 20 years of age. Gender, complications, income, history of diabetes and work status were statistically significant determinants of the total cost. Study reports a rapid increase in the median annual direct and indirect cost from ₹15,460 and ₹3572 in 1999 to ₹34,100 and ₹4200 in 2021. Conclusions The present study highlights that the economic jeopardy of diabetes can be managed by educating people about diabetes and its associated risk factors. The economic burden of diabetes could be restrained by formulating new health policies and promoting the use of generic medicines. The result of the study directs that expenditure on outpatient care is to be reimbursed under the ‘ Ayushman Bharat-Sarbat Sehat Bima Yojana’ .

S. Edwards

J. Teugels

A. Rowland, Catherine A Lesesne, A. Abramowitz

Serena Ng, Susannah Scanlan

Monthly and weekly economic indicators are often taken to be the largest common factor estimated from high and low frequency data, either separately or jointly. To incorporate mixed frequency information without directly modeling them, we target a low frequency diffusion index that is already available, and treat high frequency values as missing. We impute these values using multiple factors estimated from the high frequency data. In the empirical examples considered, static matrix completion that does not account for serial correlation in the idiosyncratic errors yields imprecise estimates of the missing values irrespective of how the factors are estimated. Single equation and systems-based dynamic procedures that account for serial correlation yield imputed values that are closer to the observed low frequency ones. This is the case in the counterfactual exercise that imputes the monthly values of consumer sentiment series before 1978 when the data was released only on a quarterly basis. This is also the case for a weekly version of the CFNAI index of economic activity that is imputed using seasonally unadjusted data. The imputed series reveals episodes of increased variability of weekly economic information that are masked by the monthly data, notably around the 2014-15 collapse in oil prices.

Kenji Itao, Kunihiko Kaneko

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.