Commercial fusion energy requires frameworks to assess both the scientific and economic viability of a wide variety of fusion concepts. Inspired by the Lawson criterion's ability to universally describe fusion energy gain, a generalized framework is developed to determine the economic gain of fusion power plants. The model exploits temporal equilibrium, and engineering and cost parameters normalized to the energy capture surface. The derived criteria for economic gain are therefore independent of the power plant's absolute power, impartial to the particulars of its fusion technology, and can be applied to any fusion confinement concept. The derivation of the economic gain factor, $Q_{econ}$, results in nonlinear equations with ten controlling normalized design parameters ranging from fusion power density and surface component lifetime to energy fluence, price of energy, and component efficiency and cost. These ten controlling parameters are varied over a wide range to provide high-level insights in design, finance and operational tradeoffs that improve the prospects for economically viable fusion energy.
This paper analyzes the macroeconomic consequences of military spending and militarization within a dynamic growth framework. Building on a Keynesian goods-market model, we examine how the allocation of government expenditure between civilian and military sectors affects capital accumulation and technological progress. Military spending generates opposing effects: it stimulates aggregate demand and may support innovation through defense-related research, but it also crowds out civilian investment and creates structural rigidities. We formalize these mechanisms in a stylized endogenous-growth model in which productivity depends on the degree of militarization, producing a non-linear relationship between the military burden and long-run growth. Calibrated simulations show that moderate levels of military spending can temporarily support growth, whereas excessive militarization reduces long-run development. We further illustrate the asymmetric growth costs of conflict using a simple two-country war simulation between an advanced economy and a sanctioned middle-income economy.
As artificial intelligence (AI) becomes foundational to enterprise infrastructure, organizations face growing challenges in accurately assessing the full economic implications of AI deployment. Existing metrics such as API token costs, GPU-hour billing, or Total Cost of Ownership (TCO) fail to capture the complete lifecycle costs of AI systems and provide limited comparability across deployment models. This paper introduces the Levelized Cost of Artificial Intelligence (LCOAI), a standardized economic metric designed to quantify the total capital (CAPEX) and operational (OPEX) expenditures per unit of productive AI output, normalized by valid inference volume. Analogous to established metrics like LCOE (levelized cost of electricity) and LCOH (levelized cost of hydrogen) in the energy sector, LCOAI offers a rigorous, transparent framework to evaluate and compare the cost-efficiency of vendor API deployments versus self-hosted, fine-tuned models. We define the LCOAI methodology in detail and apply it to three representative scenarios, OpenAI GPT-4.1 API, Anthropic Claude Haiku API, and a self-hosted LLaMA-2-13B deployment demonstrating how LCOAI captures critical trade-offs in scalability, investment planning, and cost optimization. Extensive sensitivity analyses further explore the impact of inference volume, CAPEX, and OPEX variability on lifecycle economics. The results illustrate the practical utility of LCOAI in procurement, infrastructure planning, and automation strategy, and establish it as a foundational benchmark for AI economic analysis. Policy implications and areas for future refinement, including environmental and performance-adjusted cost metrics, are also discussed.
Kevin Bradley Dsouza, Graham Alexander Watt, Yuri Leonenko
et al.
Agricultural landscapes face the dual challenge of sustaining food production while reversing biodiversity loss. Agri-environmental policies often fall short of delivering ecological functions such as landscape connectivity, in part due to a persistent disconnect between farm-level economic decisions and landscape-scale spatial planning. We introduce a novel hierarchical optimization framework that bridges this gap. First, an Ecological Intensification (EI) model determines the economically optimal allocation of land to margin and habitat interventions at the individual farm level. These farm-specific intervention levels are then passed to an Ecological Connectivity (EC) model, which spatially arranges them across the landscape to maximize connectivity while preserving farm-level profitability. Finally, we introduce a Bayesian Optimization (BO) approach that translates these spatial outcomes into simple, cost effective, and scalable policy instruments, such as subsidies and eco-premiums, using non-spatial, farm-level policy parameters. Applying the framework to a Canadian agricultural landscape, we demonstrate how it enhances connectivity under real-world economic constraints. Our approach provides a globally relevant tool for aligning farm incentives with biodiversity goals, advancing the development of agri-environmental policies that are economically viable and ecologically effective.
This article proposes predictive economics as a distinct analytical perspective within economics, grounded in machine learning and centred on predictive accuracy rather than causal identification. Drawing on the instrumentalist tradition (Friedman), the explanation-prediction divide (Shmueli), and the contrast between modelling cultures (Breiman), we formalise prediction as a valid epistemological and methodological objective. Reviewing recent applications across economic subfields, we show how predictive models contribute to empirical analysis, particularly in complex or data-rich contexts. This perspective complements existing approaches and supports a more pluralistic methodology - one that values out-of-sample performance alongside interpretability and theoretical structure.
The aggregate Cobb-Douglas production function stands as a central element in the renowned Solow-Swan model in economics, providing a crucial theoretical framework for comprehending the determinants of economic growth. This model not only guides policymakers and economists but also influences their decisions, fostering sustainable and inclusive development. In this study, we utilize a one-input version of a new generalization of the Cobb-Douglas production function proposed recently, thereby extending the Solow-Swan model to incorporate energy production as a factor. We offer a rationale for this extension and conduct a comprehensive analysis employing advanced mathematical tools to explore solutions to this new model. This approach allows us to effectively integrate environmental considerations related to energy production into economic growth strategies, fostering long-term sustainability.
This study aims to analyze the reliability of Nepal’s industrial policies, focusing on the effects of macroeconomic variables on implementation and outcomes. This paper assesses Nepal’s industrial policies, emphasizing the need for improvements, export promotion, and human capital development while recognizing the importance of strategic planning and context-specific approaches for economic growth, stability, and development. The analytical and descriptive approaches have been applied to analyze the data by collecting secondary data sources that include official publications, which encompass 47 time series variables from 1974 to 2020. The findings provide mixed evidence for the economic impacts of liberalization, with exports and liberalization driving overall GDP growth. In contrast, other factors like economic openness, tourism, and their relationship with industrial GDP remain statistically insignificant. The paper indicates that remittances and investment have the most substantial impact on GDP, raising it by 1.86 and 1.21 units per unit increase, respectively. Exports have a moderate impact on industrial GDP (0.403 units). Export-oriented industries and tourism lack significant associations with either type of GDP. Liberalization significantly boosted both GDP and industrial GDP, with an increase of 179465.3 and 49595.62 units, respectively. Imports also jumped post-liberalization, driven by higher remittances as 1.215 units per unit increase. This study on industrial policies in developing economies, focusing on Nepal, adds valuable insights. The findings can ensure policymaking, boost economic growth, and strengthen Nepal’s industrial sector.
The Gulf Cooperation Council countries -- Oman, Bahrain, Kuwait, UAE, Qatar, and Saudi Arabia -- holds strategic significance due to its large oil reserves. However, these nations face considerable challenges in shifting from oil-dependent economies to more diversified, knowledge-based systems. This study examines the progress of Gulf Cooperation Council (GCC) countries in achieving economic diversification and social development, focusing on the Social Progress Index (SPI), which provides a broader measure of societal well-being beyond just economic growth. Using data from the World Bank, covering 2010 to 2023, the study employs the XGBoost machine learning model to forecast SPI values for the period of 2024 to 2026. Key components of the methodology include data preprocessing, feature selection, and the simulation of independent variables through ARIMA modeling. The results highlight significant improvements in education, healthcare, and women's rights, contributing to enhanced SPI performance across the GCC countries. However, notable challenges persist in areas like personal rights and inclusivity. The study further indicates that despite economic setbacks caused by global disruptions, including the COVID-19 pandemic and oil price volatility, GCC nations are expected to see steady improvements in their SPI scores through 2027. These findings underscore the critical importance of economic diversification, investment in human capital, and ongoing social reforms to reduce dependence on hydrocarbons and build knowledge-driven economies. This research offers valuable insights for policymakers aiming to strengthen both social and economic resilience in the region while advancing long-term sustainable development goals.
Abdul Haris, Muhammad Munawir Syarif, Hamed Narolla
et al.
This study aims to develop a framework for multicriteria analysis to evaluate alternatives for sustainable corn agricultural area planning, considering the integration of ecological, economic, and social aspects as pillars of sustainability. The research method uses qualitative and quantitative approaches to integrate ecological, economic, and social aspects in the multicriteria analysis. The analysis involves land evaluation, subcriteria identification, and data integration using Multidimensional Scaling and Analytical Hierarchy Process methods to prioritize developing sustainable corn agricultural areas. Based on the results of the RAP-Corn analysis, it indicates that the ecological dimension depicts less sustainability. The AHP results yield weight distribution and highly relevant scores that describe tangible preferences. Priority directions are grouped as strategic steps toward achieving the goals of sustainable corn agricultural area planning.
The digital innovation accompanied by explicit economic incentives have fundamentally changed the process of innovation diffusion. As a representative of digital innovation, NFTs provide a decentralized and secure way to authenticate and trade digital assets, offering the potential for new revenue streams in the digital space. However, current researches about NFTs mainly focus on their transaction networks and community culture, leaving the interplay among diffusion dynamics, economic dynamics, and social constraints on Twitter. By collecting and analyzing NFTs-related tweet dataset, the motivations of retweeters, the information mechanisms behind emojis, and the networked-based diffusion dynamics is systematically investigated. Results indicate that Retweeting is fueled by Freemint and trading information, with the higher economic incentives as a major motivation and some potential organizational tendencies. The diffusion of NFT is primarily driven by a 'Ringed-layered' information mechanism involving individual promoters and speculators. Both the frequency and presentation of content contribute positively to the growth of the retweet network. This study contributes to the innovation diffusion theory with economic incentives embedded.
Abstract Understanding the structure and properties of production networks is essential to identify the transmission channels from monetary shocks. While growingly studied, this literature keeps displaying critical caveats from which the investigation of G-7 economies is not spared. To fill this gap, this paper applies a version of Time-Varying Parameters Bayesian Vector-Autoregressions models (TVP-VAR) and investigates the responses of production networks (upstream and downstream dynamics) to endogeneous monetary shocks on key macro-level indicators (GDP, GDP deflator, exchange rate, short-term and long-term interest rates). Two distinct time-lengths are considered: a test (i.e., 2000–2014) and a treated period (i.e., 2007–2009,”the Great Recession”). Prior, key statistical conditions are checked using a stepwise stationary testing framework including the Kwiatkowski–Phillips–Schmidt–Shin (Kapetanios et al. in J Economet 112(2):359–379, 2003—KPSS) and panel Breitung (Nonstationary panels, panel cointegration, and dynamic panels. Emerald Group Publishing Limited, London, 2001) unit root tests; followed by the Pesaran (General diagnostic tests for cross section dependence in panels, 2004) Cross-sectional Dependence (CD) test; and the Im–Pesaran–Shin (Im et al. in J Economet 115(1):53–74, 2003—IPS) test for unit root in the presence of heterogenous slope coefficients. Panel Auto-Regressive Distributed Lag Mean Group estimates (PARDL-MG) offer interesting short- and long-run monetary shocks-production networks response functions, stratified by country and sector. Findings clearly indicate that upstreamness forces dominated downstremness dynamics during the period 2000–2014, whereas the financial sector ermeges as the clear transmission channel through which monetary shocks affected the productive economy during the Great Recession. In general, we conclude that the prioduction structure influences the transmission of monetary shocks in the G-7 economies. Adequate policy implications are supplied, along with a methodological note on the forecasting potential of TVP-VAR methodologies when dealing with series exhibiting structural breaks.
Economic growth, development, planning, Economics as a science
Taxation and government spending are crucial tools for governments to promote economic growth and maintain social equity. However, the difficulty in accurately predicting the dynamic strategies of diverse self-interested households presents a challenge for governments to implement effective tax policies. Given its proficiency in modeling other agents in partially observable environments and adaptively learning to find optimal policies, Multi-Agent Reinforcement Learning (MARL) is highly suitable for solving dynamic games between the government and numerous households. Although MARL shows more potential than traditional methods such as the genetic algorithm and dynamic programming, there is a lack of large-scale multi-agent reinforcement learning economic simulators. Therefore, we propose a MARL environment, named \textbf{TaxAI}, for dynamic games involving $N$ households, government, firms, and financial intermediaries based on the Bewley-Aiyagari economic model. Our study benchmarks 2 traditional economic methods with 7 MARL methods on TaxAI, demonstrating the effectiveness and superiority of MARL algorithms. Moreover, TaxAI's scalability in simulating dynamic interactions between the government and 10,000 households, coupled with real-data calibration, grants it a substantial improvement in scale and reality over existing simulators. Therefore, TaxAI is the most realistic economic simulator for optimal tax policy, which aims to generate feasible recommendations for governments and individuals.
Christian Diem, András Borsos, Tobias Reisch
et al.
To estimate the reaction of economies to political interventions or external disturbances, input-output (IO) tables -- constructed by aggregating data into industrial sectors -- are extensively used. However, economic growth, robustness, and resilience crucially depend on the detailed structure of non-aggregated firm-level production networks (FPNs). Due to non-availability of data little is known about how much aggregated sector-based and detailed firm-level-based model-predictions differ. Using a nearly complete nationwide FPN, containing 243,399 Hungarian firms with 1,104,141 supplier-buyer-relations we self-consistently compare production losses on the aggregated industry-level production network (IPN) and the granular FPN. For this we model the propagation of shocks of the same size on both, the IPN and FPN, where the latter captures relevant heterogeneities within industries. In a COVID-19 inspired scenario we model the shock based on detailed firm-level data during the early pandemic. We find that using IPNs instead of FPNs leads to errors up to 37% in the estimation of economic losses, demonstrating a natural limitation of industry-level IO-models in predicting economic outcomes. We ascribe the large discrepancy to the significant heterogeneity of firms within industries: we find that firms within one sector only sell 23.5% to and buy 19.3% from the same industries on average, emphasizing the strong limitations of industrial sectors for representing the firms they include. Similar error-levels are expected when estimating economic growth, CO2 emissions, and the impact of policy interventions with industry-level IO models. Granular data is key for reasonable predictions of dynamical economic systems.
احمد فتاحی اردکانی, محمد رضوانی, یدالله بستان
et al.
تحریمهای غرب نسبت به فدراسیون روسیه و اقدام متقابل فدراسیون روسیه در سال 2014 و تحریم واردات مواد غذایی از غرب شامل اتحادیه اروپا، امریکا، استرالیا، کانادا و نروژ این فرضیه را تداعی میکند که ممکن است تغییراتی در ترجیحات مصرفکنندگان کشور روسیه نسبت به برخی محصولات از جمله محصول خیار وارداتی ایران رخ داده باشد. در مطالعه حاضر، با استفاده از روش ناپارامتری بر مبنای اصل تعمیمیافته ترجیحات آشکارشده (GARP)، بررسی و آزمون این فرضیه انجام گرفت. در این راستا، دادههای قیمت و مقدار مربوط به واردات خیار فدراسیون روسیه از ایران و کشورهای ترکیه و چین با بیشترین صادرات این محصول به روسیه برای دوره زمانی 2017-2000 بررسی شد. نتایج مطالعه نشان داد که یک تغییر ساختاری معنیدار در ترجیحات مصرفکنندگان کشور روسیه در سال 2003 به نفع محصول خیار ایران رخ داده، که نشاندهندة پایداری و وفاداری مصرفکنندگان کشور روسیه نسبت به خیار وارداتی از سوی ایران است؛ در نهایت، مناقشات سیاسی فدراسیون روسیه و ترکیه فرصتی مناسب برای توسعه صادرات محصولات ایرانی در بازار فدراسیون روسیه فراهم کرده است. اطمینان از تولید خیار باکیفیت در تمام ایام سال و عدم تغییر سیاستهای دولت در صادرات میتواند کمکی به افزایش سهم ایران در بازار فدراسیون روسیه باشد.
An information entropy statistical methodology was used to evaluate the growth of the UK economy over the period 2000 to 2019, with an emphasis on the impact of labour productivity on gross domestic product (GDP) per capita and the average growth in real wages, during this time period. The growth of the UK economy over the period 2000 to 2019 can be described in terms of three distinct phases: 1) 2000 to 2007 - strong sustained economic growth 2) 2008 to 2013 - the impact of the international financial crisis, its immediate aftermath, and period of recovery 3) 2014 to 2019 - weak sustained economic growth The key determinant of the UK economic performance over this period would appear to the annual rate of growth in labour productivity. It was closely related to the annual rate of growth in GDP per capita, and it was significantly weaker in the period 2014 to 2019 compared to the period 2000 to 2007. This also corresponded with a weaker rate of growth in annual average real wages over the period 2014 to 2019 compared to the period 2000 to 2007. Throughout the period 2000 to 2019, UK CPI was maintained, on average, at approximately 2.1% per annum. More rapid UK economic growth would be expected to be achieved by sustained investment in measures that enhance labour productivity, with the further expectation that a sustained improvement in labour productivity would increase the annual rate of growth of UK GDP per capita and average real wages. While the results given in this paper are specific to the UK over the time period 2000 to 2019, the expectation is that the methodology and approach adopted can be applied to quantifying the dynamics of any developed economy over any time period.
Umut Demir, A. Sadik Satir, Gulay Goktas Sever
et al.
Development of guidance, navigation and control frameworks/algorithms for swarms attracted significant attention in recent years. That being said, algorithms for planning swarm allocations/trajectories for engaging with enemy swarms is largely an understudied problem. Although small-scale scenarios can be addressed with tools from differential game theory, existing approaches fail to scale for large-scale multi-agent pursuit evasion (PE) scenarios. In this work, we propose a reinforcement learning (RL) based framework to decompose to large-scale swarm engagement problems into a number of independent multi-agent pursuit-evasion games. We simulate a variety of multi-agent PE scenarios, where finite time capture is guaranteed under certain conditions. The calculated PE statistics are provided as a reward signal to the high level allocation layer, which uses an RL algorithm to allocate controlled swarm units to eliminate enemy swarm units with maximum efficiency. We verify our approach in large-scale swarm-to-swarm engagement simulations.
In this paper, we provide a macro-comparative assessment of welfare state convergence. Using the welfare state regime approach, the paper analyses the development of main welfare state indicators within in the enlarged European Union. In this study we capitalize on descriptive statistics and a single convergence analysis based on standard deviation in order to capture alterations in national welfare models of 26 European countries and among acknowledged welfare regimes. Our fundamental aim is to seize on long-term processes (convergence, divergence, or persistence), so we cover almost a two-decade period starting at 2000. Our results, in general, suggest that convergence among welfare states (different indicator of social spending) of European countries is particularly weak, however convergence inside welfare regimes is significantly stronger apart from the Anglo-Saxon group. The pre-crisis period was characterized by a stronger convergence among European countries as a consequence of economic prosperity and intense EU intervention.