The ability of large language models (LLMs) to manage and acquire economic resources remains unclear. In this paper, we introduce \textbf{Market-Bench}, a comprehensive benchmark that evaluates the capabilities of LLMs in economically-relevant tasks through economic and trade competition. Specifically, we construct a configurable multi-agent supply chain economic model where LLMs act as retailer agents responsible for procuring and retailing merchandise. In the \textbf{procurement} stage, LLMs bid for limited inventory in budget-constrained auctions. In the \textbf{retail} stage, LLMs set retail prices, generate marketing slogans, and provide them to buyers through a role-based attention mechanism for purchase. Market-Bench logs complete trajectories of bids, prices, slogans, sales, and balance-sheet states, enabling automatic evaluation with economic, operational, and semantic metrics. Benchmarking on 20 open- and closed-source LLM agents reveals significant performance disparities and winner-take-most phenomenon, \textit{i.e.}, only a small subset of LLM retailers can consistently achieve capital appreciation, while many hover around the break-even point despite similar semantic matching scores. Market-Bench provides a reproducible testbed for studying how LLMs interact in competitive markets.
The technological transition of MacBook charging solutions from MagSafe to USB-C, followed by a return to MagSafe 3, encapsulates the dynamic interplay between technological advancement, environmental considerations, and economic factors. This study delves into the broad implications of these charging technology shifts, particularly focusing on the environmental repercussions associated with electronic waste and the economic impacts felt by both manufacturers and consumers. By investigating the lifecycle of these technologies - from development and market introduction through to their eventual obsolescence - this paper underscores the importance of devising strategies that not only foster technological innovation but also prioritize environmental sustainability and economic feasibility. This comprehensive analysis illuminates the crucial factors influencing the evolution of charging technologies and their wider societal and environmental implications, advocating for a balanced approach that ensures technological progress does not compromise ecological health or economic stability.
This paper studies the macroeconomic impact of economic freedom on foreign direct investments inflows in both global and regional panel analyses involving 156 countries through the period of 1995-2016. Unlike to prior literature, it includes often neglected nations such as Fragile and Conflict-Affected states, Sub-Saharan, Oceanian, and Post-Soviet countries. The paper finds a positive impact of economic freedom on FDI under fixed-effects model in global case where a unit change in economic freedom scales FDI inflows up to 1.15 units. More specifically, all 9 regions also refer to positive and significant impact of economic freedom on FDI. The highest impact is recorded in European countries, whereas the lowest ones are documented in Fragile-Conflict affected states, Sub-Saharan zone, and Oceanian countries.
Napjainkra nyilvánvalóvá vált, hogy a területi különbségek kezelése sem uniós sem tagállami szinten, de különösen a vidéki térségek számára nem kínál az elvárásoknak megfelelő megoldásokat. A vidéki térségeket kiemelten érintik az olyan kérdések, mint a klímaváltozás negatív hatásainak kezelése és a reziliencia kérdése, a mesterséges intelligencia és a robotizáció térnyerésének veszélyei, a gazdasági növekedés egyik alapfeltételét jelentő innovációs ökoszisztéma hiánya vagy gyengesége. Az is nyilvánvaló, hogy e jövőt érintő kihívásokra eredményes válasz kizárólag komplex megközelítés mellett adható, amelyre – kormányzási oldalról – a rural proofing kínálhat egyfajta eszközt. A rural proofing egy olyan, egyelőre kevéssé kutatott eszköz, amelynek tudományági gyökerei és azok mélyebb összefüggései, továbbá a gyakorlati alkalmazás sikerességének feltételei nem teljesen tisztázottak. A tanulmány megvizsgálja azokat a jellemzőket, amelyek segítségével nem csupán a tudományági besorolás válhat világossá, de – ebből következően – a gyakorlati alkalmazás irányai is jóval egyértelműbbé válhatnak. Ezentúl sor kerül azon feltételek számbavételére is, amelyek nélkül e sajátos és újszerű kormányzási eszköz sikeressége elképzelhetetlennek tűnik. A megállapítások között szerepel az is, hogy a sikerhez elengedhetetlen tűnnek az olyan alapelvek érvényesülése (pl. partnerség, többszintű kormányzás), amelyek kifejezetten a fejlesztéspolitika és nem pedig a klasszikus közigazgatás világában honosak. A tanulmány rámutat arra is, hogy a rural proofing nem csupán a vidéki térségek esetén lehet sikeres, mivel lényegi elemi közé tartozik a területi sajátosságok figyelembevétele és a helyi igényekhez igazodó, differenciált beavatkozások kialakításának lehetősége. A megállapítások természetesen nem kérdésköröket kívánnak lezárni, hanem sokkal inkább olyan új megvilágításba szeretnék helyezni a rural proofing működési feltételeit, amelyek hozzájárulnak további tudományos vitákhoz, valamint egy sokkal eredményesebb központi és területi beavatkozási mechanizmus kialakításához.
History (General) and history of Europe, Economic history and conditions
This paper explores the potential of contextualized word embeddings (CWEs) as a new tool in the history, philosophy, and sociology of science (HPSS) for studying contextual and evolving meanings of scientific concepts. Using the term "Planck" as a test case, I evaluate five BERT-based models with varying degrees of domain-specific pretraining, including my custom model Astro-HEP-BERT, trained on the Astro-HEP Corpus, a dataset containing 21.84 million paragraphs from 600,000 articles in astrophysics and high-energy physics. For this analysis, I compiled two labeled datasets: (1) the Astro-HEP-Planck Corpus, consisting of 2,900 labeled occurrences of "Planck" sampled from 1,500 paragraphs in the Astro-HEP Corpus, and (2) a physics-related Wikipedia dataset comprising 1,186 labeled occurrences of "Planck" across 885 paragraphs. Results demonstrate that the domain-adapted models outperform the general-purpose ones in disambiguating the target term, predicting its known meanings, and generating high-quality sense clusters, as measured by a novel purity indicator I developed. Additionally, this approach reveals semantic shifts in the target term over three decades in the unlabeled Astro-HEP Corpus, highlighting the emergence of the Planck space mission as a dominant sense. The study underscores the importance of domain-specific pretraining for analyzing scientific language and demonstrates the cost-effectiveness of adapting pretrained models for HPSS research. By offering a scalable and transferable method for modeling the meanings of scientific concepts, CWEs open up new avenues for investigating the socio-historical dynamics of scientific discourses.
Minghao Han, Jingshi Yao, Adrian Wing-Keung Law
et al.
Used water treatment plays a pivotal role in advancing environmental sustainability. Economic model predictive control holds the promise of enhancing the overall operational performance of the water treatment facilities. In this study, we propose a data-driven economic predictive control approach within the Koopman modeling framework. First, we propose a deep learning-enabled input-output Koopman modeling approach, which predicts the overall economic operational cost of the wastewater treatment process based on input data and available output measurements that are directly linked to the operational costs. Subsequently, by leveraging this learned input-output Koopman model, a convex economic predictive control scheme is developed. The resulting predictive control problem can be efficiently solved by leveraging quadratic programming solvers, and complex non-convex optimization problems are bypassed. The proposed method is applied to a benchmark wastewater treatment process. The proposed method significantly improves the overall economic operational performance of the water treatment process. Additionally, the computational efficiency of the proposed method is significantly enhanced as compared to benchmark control solutions.
Perbankan syariah di Indonesia telah berkembang pesat, terbukti dari peningkatan jumlah bank syariah dan aset keuangan mereka. Meskipun ada kemajuan, kinerja keuangan bank syariah mengalami fluktuasi, terutama pada rasio Profitabilitas seperti Return on Assets (ROA) yang terpengaruh oleh pandemi COVID-19 dan kondisi ekonomi lainnya. Inflasi mempengaruhi biaya operasional dan pendapatan bank, namun dampaknya tidak selalu signifikan terhadap profitabilitas karena faktor lain juga memengaruhi. Penelitian ini bertujuan untuk menganalisis pengaruh dari Captal Adequacy Ratio (CAR), Non Performing Financing (NPF), Biaya Operasional Pendapatan Operasional (BOPO) terhadap Profitabilitas Perbankan Syariah di Indonesia dengan Inflasi sebagai variabel pemoderasi. Teknik pengambilan sample menggunakan Sampling Jenuh serta pendekatan Time Series untuk menganalisis data. Dengan model Vector Auto Regression (VAR) menggunakan Eviews versi 10 dan metode Moderate Regression Analysis (MRA) menggunakan SPSS Statistics versi 2023. Hasil penelitian menyimpulkan variabel CAR dan BOPO berpengaruh terhadap ROA, sebaliknya variabel NPF tidak berpengaruh terhadap ROA. Variabel Inflasi memoderasi CAR, BOPO terhadap ROA, sedangkan sebaliknya variabel Inflasi tidak memoderasi NPF terhadap ROA. Penelitian ini berkontribusi dalam memperluas hubungan langsung antara variabel independent terhadap variabel dependent yang dalam riset terdahulu tidak di elaborasi lebih detail.
Neural Language Models (LMs) offer an exciting solution for general-purpose embodied control. However, a key technical issue arises when using an LM-based controller: environment observations must be converted to text, which coupled with history, results in long and verbose textual prompts. As a result, prior work in LM agents is limited to restricted domains with small observation size as well as minimal needs for interaction history or instruction tuning. In this paper, we introduce diff history, a simple and highly effective solution to these issues. By applying the Unix diff command on consecutive text observations in the interaction histories used to prompt LM policies, we can both abstract away redundant information and focus the content of textual inputs on the salient changes in the environment. On NetHack, an unsolved video game that requires long-horizon reasoning for decision-making, LMs tuned with diff history match state-of-the-art performance for neural agents while needing 1800x fewer training examples compared to prior work. Even on the simpler BabyAI-Text environment with concise text observations, we find that although diff history increases the length of prompts, the representation it provides offers a 25% improvement in the efficiency of low-sample instruction tuning. Further, we show that diff history scales favorably across different tuning dataset sizes. We open-source our code and data to https://diffhistory.github.io.
An heuristic model of the society, as an assembly of weakly interacting individuals, is discussed. The model allows to connect macroscopic phenomena with features of relations between individuals. Addressing to the problem of inequality, a non-equilibrium situation is considered. The calculated income distribution function, which coincides with the Pareto distribution at large incomes, can be interpreted as a strongly deformed Gauss distribution function. The external, in relation to the system of interacting individuals, force is necessary to maintain the strong non-equilibrium in a stationary state. The model representation of the society allows us to explain the mechanism of the emergence and maintenance of economic inequality, the universal cause of which is asymmetry of elementary exchanges.
We demonstrate the effectiveness of the logistic function to model the evolution of two economic systems. The first is the GDP and trade growth of the USA, and the second is the revenue and human resource growth of IBM. Our modelling is based on the World Bank data in the case of the USA, and on the company data in the case of IBM. The coupled dynamics of the two relevant variables in both systems - GDP and trade for the USA, and revenue and human resource for IBM - follows a power-law behaviour.
Theodoros Chatzivasileiadis, Ignasi Cortes Arbues, Jochen Hinkel
et al.
This study investigates the long-term economic impact of sea-level rise (SLR) on coastal regions in Europe, focusing on Gross Domestic Product (GDP). Using a novel dataset covering regional SLR and economic growth from 1900 to 2020, we quantify the relationships between SLR and regional GDP per capita across 79 coastal EU & UK regions. Our results reveal that the current SLR has already negatively influenced GDP of coastal regions, leading to a cumulative 4.7% loss at 39 cm of SLR. Over the 120 year period studied, the actualised impact of SLR on the annual growth rate is between -0.02% and 0.04%. Extrapolating these findings to future climate and socio-economic scenarios, we show that in the absence of additional adaptation measures, GDP losses by 2100 could range between -6.3% and -20.8% under the most extreme SLR scenario (SSP5-RCP8.5 High-end Ice, or -4.0% to -14.1% in SSP5-RCP8.5 High Ice). This statistical analysis utilising a century-long dataset, provides an empirical foundation for designing region-specific climate adaptation strategies to mitigate economic damages caused by SLR. Our evidence supports the argument for strategically relocating assets and establishing coastal setback zones when it is economically preferable and socially agreeable, given that protection investments have an economic impact.
Due to developments in the field of fast transportation, increase in permitted speed and load capacity, moving loads can have significant effects on the dynamic forces of bridges. To consider the dynamic effect in the design of the structure, the dynamic impact factor is introduced as ratio of the dynamic response to the static response. Accurate evaluation of these coefficients helps in safe and economic designs for new bridges. However, the evaluation of the dynamic impact factor is difficult due to the vehicle-bridge interaction and the influence of many parameters that affect the dynamic impact factor, including the dynamic characteristics of the bridge and the vehicle, road surface conditions, vehicle speed, traffic conditions,. In this research, by applying live load of vehicles step by step and performing time history analysis, dynamic analysis under moving load has been done. Three different types of cable-stayed bridges with different spans and cable layouts have been investigated in the form of two-dimensional models. This study analyzes the impact coefficient of bending and shear forces of deck components and pylons, as well as the axial forces of cables, and the results are compared with the coefficients proposed in the design regulations. Also, the effect of changes in load passing speed on the dynamic impact factor in cable-stayed bridges has also been evaluated and studied. This research tries to improve and optimize the design and performance of cable-stayed bridges in order to deal with dynamic changes and increase the speed of loads.
Studies have evaluated the economic feasibility of 100% renewable power systems using the optimization approach, but the mechanisms determining the results remain poorly understood. Based on a simple but essential model, this study found that the bottleneck formed by the largest mismatch between demand and power generation profiles determines the optimal capacities of generation and storage and their trade-off relationship. Applying microeconomic theory, particularly the duality of quantity and value, this study comprehensively quantified the relationships among the factor cost of technologies, their optimal capacities, and total system cost. Using actual profile data for multiple years/regions in Japan, this study demonstrated that hybrid systems comprising cost-competitive multiple renewable energy sources and different types of storage are critical for the economic feasibility of any profile.
Alvi Ataur Khalil, Alexander J Byrne, Mohammad Ashiqur Rahman
et al.
Advances in unmanned aerial vehicle (UAV) design have opened up applications as varied as surveillance, firefighting, cellular networks, and delivery applications. Additionally, due to decreases in cost, systems employing fleets of UAVs have become popular. The uniqueness of UAVs in systems creates a novel set of trajectory or path planning and coordination problems. Environments include many more points of interest (POIs) than UAVs, with obstacles and no-fly zones. We introduce REPlanner, a novel multi-agent reinforcement learning algorithm inspired by economic transactions to distribute tasks between UAVs. This system revolves around an economic theory, in particular an auction mechanism where UAVs trade assigned POIs. We formulate the path planning problem as a multi-agent economic game, where agents can cooperate and compete for resources. We then translate the problem into a Partially Observable Markov decision process (POMDP), which is solved using a reinforcement learning (RL) model deployed on each agent. As the system computes task distributions via UAV cooperation, it is highly resilient to any change in the swarm size. Our proposed network and economic game architecture can effectively coordinate the swarm as an emergent phenomenon while maintaining the swarm's operation. Evaluation results prove that REPlanner efficiently outperforms conventional RL-based trajectory search.
We deal with an infinite horizon, infinite dimensional stochastic optimal control problem arising in the study of economic growth in time-space. Such problem has been the object of various papers in deterministic cases when the possible presence of stochastic disturbances is ignored. Here we propose and solve a stochastic generalization of such models where the stochastic term, in line with the standard stochastic economic growth models, is a multiplicative one, driven by a cylindrical Wiener process. The problem is studied using the Dynamic Programming approach. We find an explicit solution of the associated HJB equation and, using a verification type result, we prove that such solution is the value function and we find the optimal feedback strategies. Finally we use this result to study the asymptotic behavior of the optimal trajectories.
Injection drug initiation usually requires assistance by someone who already injects drugs. To develop interventions that prevent people from starting to inject drugs, it is imperative to understand why people who inject drugs (PWID) assist with injection initiation. METHODS Injection initiation history and motives for initiating others were collected from 978 PWID in Los Angeles and San Francisco, CA, from 2016-17. This article documents motivations for providing injection initiation assistance and examines demographic, economic, and health-related factors associated with these motivations using multivariable logistic regression modeling. RESULTS Among the 405 PWID who ever facilitated injection initiation, motivations for initiating were: injury prevention (66%), skilled at injecting others (65%), to avoid being pestered (41%), in exchange for drugs/money (45%), and for food/shelter/transportation (15%). High frequency initiation (>5 lifetime injection initiations) was associated with all motivations except for being pestered. Initiation to prevent injury was associated with being female. Initiation due to pestering was associated with recycling income and sex work. Being skilled was associated with age and HIV status, while initiation for money or drugs was associated with age, race, education, social security income, and substance use treatment. Lastly, initiation for food, shelter, or transportation was associated with age, sexual orientation and education level. CONCLUSION Diverse factors were associated with reported motivations for assisting someone to initiate injection for the first time. Our analysis underscores the need for prevention strategies focused on improving economic and housing conditions along with implementing drug consumption rooms to disrupt the social processes of injection initiation.