Hasil untuk "Balance of trade"

David K. Backus, Patrick J. Kehoe, F. Kydland

Sizhong Sun

This paper studies firms' optimal response to a trade liberalization shock in terms of export and product innovation both theoretically and empirically. We find that trade liberalization, namely China's WTO accession, reduces trade cost and promotes export, which in turn incentivizes firms to innovate as the marginal benefit of innovation for exporting firms is higher than that for non-exporting firms. In addition, as a firm starts to innovate, it predicts to have a higher probability of moving to a better productivity state and can save the entry cost of innovation in the future, resulting in additional dynamic benefits. Such an innovation-promotion effect is an unintended consequence of trade liberalization.

Yixuan Tang, Xintong Li, Yingwen Yu

Achieving sustainability in prefabricated construction necessitates a balance between operational efficiency and stringent environmental constraints. However, cascading rework chains triggered by assembly defects frequently disrupt this equilibrium. Existing literature predominantly addresses this dynamic through reactive rescheduling, thereby largely overlooking the potential of proactive topological interception. To bridge this gap, this study proposes a proactive bi-level scheduling framework that mathematically integrates strategic quality inspection planning with operational low-carbon project execution. Specifically, a Generalized Total Cost (GTC) model is formulated to internalize multi-objective trade-offs—including time, cost, and carbon emissions—into a unified financial metric through market-based shadow prices. This framework is operationalized through a novel bi-level Hybrid Evolutionary Algorithm (H-TS-CDBO). By combining the global exploration capabilities of Chaotic Dung Beetle Optimization with the local refinement mechanisms of Tabu Search, the proposed solver is specifically engineered to navigate the topological ruggedness induced by proactive inspection interventions. Empirical benchmarking validates the computational robustness of the solver, while an illustrative case study substantiates a critical managerial paradigm shift from “passive remediation” to “active prevention”: compared to traditional methods, a marginal preventive investment of 5.4% functions as an effective containment mechanism, yielding a 40.8% net reduction in the GTC. Furthermore, a sensitivity analysis regarding varying static carbon tax rates simulates algorithmic adaptation under diverse regulatory intensity thresholds, delineating an actionable pathway for project managers to achieve lean, low-carbon synergy amidst evolving regulatory pressures.

John S. McAlister, Jesse L. Brunner, Danielle J. Galvin et al.

Global trade of material goods involves the potential to create pathways for the spread of infectious pathogens. One trade sector in which this synergy is clearly critical is that of wildlife trade networks. This highly complex system involves important and understudied bidirectional coupling between the economic decision making of the stakeholders and the contagion dynamics on the emergent trade network. While each of these components are independently well studied, there is a meaningful gap in understanding the feedback dynamics that can arise between them. In the present study, we describe a general game theoretic model for trade networks of goods susceptible to contagion. The primary result relies on the acyclic nature of the trade network and shows that, through the course of trading with stochastic infections, the probability of infection converges to a directly computable fixed point. This allows us to compute best responses and thus identify equilibria in the game. We present ways to use this model to describe and evaluate trade networks in terms of global and individual risk of infection under a wide variety of structural or individual modifications to the trade network. In capturing the bidirectional coupling of the system, we provide critical insight into the global and individual drivers and consequences for risks of infection inherent in and arising from the global wildlife trade, and any economic trade network with associated contagion risks.

Anna Lunghi, Matteo Castiglioni, Alberto Marchesi

Bilateral trade is a central problem in algorithmic economics, and recent work has explored how to design trading mechanisms using no-regret learning algorithms. However, no-regret learning is impossible when budget balance has to be enforced at each time step. Bernasconi et al. [Ber+24] show how this impossibility can be circumvented by relaxing the budget balance constraint to hold only globally over all time steps. In particular, they design an algorithm achieving regret of the order of $\tilde O(T^{3/4})$ and provide a lower bound of $Ω(T^{5/7})$. In this work, we interpolate between these two extremes by studying how the optimal regret rate varies with the allowed violation of the global budget balance constraint. Specifically, we design an algorithm that, by violating the constraint by at most $T^β$ for any given $β\in [\frac{3}{4}, \frac{6}{7}]$, attains regret $\tilde O(T^{1 - β/3})$. We complement this result with a matching lower bound, thus fully characterizing the trade-off between regret and budget violation. Our results show that both the $\tilde O(T^{3/4})$ upper bound in the global budget balance case and the $Ω(T^{5/7})$ lower bound under unconstrained budget balance violation obtained by Bernasconi et al. [Ber+24] are tight.

Craig Wright

This paper presents a comprehensive refutation of the so-called "blockchain trilemma," a widely cited but formally ungrounded claim asserting an inherent trade-off between decentralisation, security, and scalability in blockchain protocols. Through formal analysis, empirical evidence, and detailed critique of both methodology and terminology, we demonstrate that the trilemma rests on semantic equivocation, misuse of distributed systems theory, and a failure to define operational metrics. Particular focus is placed on the conflation of topological network analogies with protocol-level architecture, the mischaracterisation of Bitcoin's design--including the role of miners, SPV clients, and header-based verification--and the failure to ground claims in complexity-theoretic or adversarial models. By reconstructing Bitcoin as a deterministic, stateless distribution protocol governed by evidentiary trust, we show that scalability is not a trade-off but an engineering outcome. The paper concludes by identifying systemic issues in academic discourse and peer review that have allowed such fallacies to persist, and offers formal criteria for evaluating future claims in blockchain research.

Xiangyu Li, Yawen Zeng, Xiaofen Xing et al.

As automated trading gains traction in the financial market, algorithmic investment strategies are increasingly prominent. While Large Language Models (LLMs) and Agent-based models exhibit promising potential in real-time market analysis and trading decisions, they still experience a significant -20% loss when confronted with rapid declines or frequent fluctuations, impeding their practical application. Hence, there is an imperative to explore a more robust and resilient framework. This paper introduces an innovative multi-agent system, HedgeAgents, aimed at bolstering system robustness via ``hedging'' strategies. In this well-balanced system, an array of hedging agents has been tailored, where HedgeAgents consist of a central fund manager and multiple hedging experts specializing in various financial asset classes. These agents leverage LLMs' cognitive capabilities to make decisions and coordinate through three types of conferences. Benefiting from the powerful understanding of LLMs, our HedgeAgents attained a 70% annualized return and a 400% total return over a period of 3 years. Moreover, we have observed with delight that HedgeAgents can even formulate investment experience comparable to those of human experts (https://hedgeagents.github.io/).

Yuan Deng, Jieming Mao, Balasubramanian Sivan et al.

We study the social efficiency of bilateral trade between a seller and a buyer. In the classical Bayesian setting, the celebrated Myerson-Satterthwaite impossibility theorem states that no Bayesian incentive-compatible, individually rational, and budget-balanced mechanism can achieve full efficiency. As a counterpoint, Deng, Mao, Sivan, and Wang (STOC 2022) show that if pricing power is delegated to the right person (either the seller or the buyer), the resulting mechanism can guarantee at least a constant fraction of the ideal (yet unattainable) gains from trade. In practice, the agent with pricing power may not have perfect knowledge of the value distribution of the other party, and instead may rely on samples of that distribution to set a price. We show that for a broad class of sampling and pricing behaviors, the resulting market still guarantees a constant fraction of the ideal gains from trade in expectation. Our analysis hinges on the insight that social welfare under sample-based pricing approximates the seller's optimal revenue -- a result we establish via a reduction to a random walk.

Yunchen Zhang, Jianying Yang, Jianjun Zhang et al.

In the ecologically fragile western Shanxi Loess region, stand density regulation of artificial <i>Robinia pseudoacacia</i> L. forests plays a crucial role in sustaining the water regulation functions of the litter-soil system, yet multi-scale mechanistic analyses remain scarce. To address this gap, we established six stand density classes (ranging from 1200 to 3200 stems/ha) and quantified litter water-holding traits and soil physicochemical properties. We then applied principal component analysis (PCA) and structural equation modeling (SEM) to examine density-litter-soil relationships. Low-density stands (≤2000 stems/ha) exhibited significantly higher litter accumulation (6.08–6.37 t/ha) and greater litter water-holding capacity (maximum 20.58 t/ha) than the high-density stands (<i>p</i> < 0.05). Soil capillary water-holding capacity decreased with increasing density (4702.63–4863.28 t/ha overall), while non-capillary porosity (5.26–6.21%) and soil organic carbon (~12.5 g/kg) were higher in high-density stands (≥2800 stems/ha), reflecting a structural-carbon optimization trade-off. PCA revealed a primary hydrological function axis with low-density stands clustering in the positive quadrant, while high-density stands shifted toward nutrient-conservation traits. SEM confirmed that stand density affected soil capillary water-holding capacity indirectly through litter accumulation (significant indirect path; non-significant direct path), highlighting the central role of litter quantity. When density exceeded ~2400 stems/ha, litter decomposition rate decreased by ~56%, coinciding with capillary porosity falling below ~47%, a threshold linked to impaired balance between water storage and infiltration. These findings identify 1200–1600 stems/ha as the optimal density range; in this range, soil capillary water-holding capacity reached 4788–4863 t/ha, and available phosphorus remained ≥2.1 mg/kg, providing a density-centered, near-natural management paradigm for constructing “water-conservation vegetation” on the Loess Plateau.

Thomas Gaskin, Guven Demirel, Marie-Therese Wolfram et al.

Global trade is shaped by a complex mix of factors beyond supply and demand, including tangible variables like transport costs and tariffs, as well as less quantifiable influences such as political and economic relations. Traditionally, economists model trade using gravity models, which rely on explicit covariates that might struggle to capture these subtler drivers of trade. In this work, we employ optimal transport and a deep neural network to learn a time-dependent cost function from data, without imposing a specific functional form. This approach consistently outperforms traditional gravity models in accuracy and has similar performance to three-way gravity models, while providing natural uncertainty quantification. Applying our framework to global food and agricultural trade, we show that the Global South suffered disproportionately from the war in Ukraine's impact on wheat markets. We also analyse the effects of free-trade agreements and trade disputes with China, as well as Brexit's impact on British trade with Europe, uncovering hidden patterns that trade volumes alone cannot reveal.

Wanneng Wu, Ao Liu, Jianwen Hu et al.

Crafting an edge-based real-time object detector for unmanned aerial vehicle (UAV) aerial images is challenging because of the limited computational resources and the small size of detected objects. Existing lightweight object detectors often prioritize speed over detecting extremely small targets. To better balance this trade-off, this paper proposes an efficient and low-complexity object detector for edge computing platforms deployed on UAVs, termed EUAVDet (Edge-based UAV Object Detector). Specifically, an efficient feature downsampling module and a novel multi-kernel aggregation block are first introduced into the backbone network to retain more feature details and capture richer spatial information. Subsequently, an improved feature pyramid network with a faster ghost module is incorporated into the neck network to fuse multi-scale features with fewer parameters. Experimental evaluations on the VisDrone, SeaDronesSeeV2, and UAVDT datasets demonstrate the effectiveness and plug-and-play capability of our proposed modules. Compared with the state-of-the-art YOLOv8 detector, the proposed EUAVDet achieves better performance in nearly all the metrics, including parameters, FLOPs, mAP, and FPS. The smallest version of EUAVDet (EUAVDet-n) contains only 1.34 M parameters and achieves over 20 fps on the Jetson Nano. Our algorithm strikes a better balance between detection accuracy and inference speed, making it suitable for edge-based UAV applications.

Wisdom Richard Mgomezulu, Paul Thangata, Daniel Njiwa

The impact of trade liberalization on Malawi’s economy has been a hotly debated topic. To shed light on the subject, a study was conducted using the PEP-1–1 CGE model and the latest Malawi’s Social Accounting Matrix (SAM) from 2019. The results were eye-opening, revealing the potential effects of the African Continental Free Trade Area (AfCFTA) on various sectors of the economy. The removal of trade tariffs is predicted to have a significant impact on prices, with a decrease of 26.31% in the agricultural sector alone, services (−7.88%), public administration (−9.92%), and manufacturing and industry (−11.23%) imposing hopes of improving food affordability and food security. However, it is expected to have adverse impacts on wage rates in the agricultural sector (−18.78%), manufacturing and construction (−19.01%), services (−2.79%) and public administration (−15.81%). Additionally, while exports are expected to increase, the country’s balance of payments may suffer as imports are likely to outweigh foreign earnings. This could also lead to a decrease in government revenue from taxes. To mitigate these effects, the study suggests implementing export restructuring strategies, particularly in industries like manufacturing and construction, and promoting diversification of local production to boost competitiveness and improve wage rates. With these measures in place, the government will not only offset potential losses but also tap into new sources of taxable income.

Die Hu, Fengxiang Guo, Qingyan Meng et al.

Land surface temperature (LST) captures fundamental information on the spatiotemporal variation of energy balance at the surface. The trade-off between spatial and temporal resolutions of remote sensing images (retrieved LSTs), however, restricts fine-scale thermal environmental investigations. In this context, a novel dual-layer composite framework (DCF) for LST downscaling coupling spatial autocorrelation and spatial heterogeneity was developed based on the two fundamental laws of geography and used to improve existing kernel-driven methods. Besides, a new non-parametric kernel-driven LST downscaling method (N-DLST) was also proposed under the DCF, in which Bayesian non-parametric general regression (BNGR) was applied to predict the high-resolution LSTs with auto-selected kernels. In the experiment of downscaling Landsat 8 LST from 300 m to 30 m over the highly heterogeneous urban area, the N-DLST method significantly outperformed the original kernel-driven methods, with the highest coefficient of determination (R2 = 0.93) and lowest root mean square error (RMSE = 0.85). Moreover, the enhanced effects of DCF in downscaling LST were demonstrated by comparing the accuracy of the disaggregation of radiometric surface temperature (DisTrad), the geographically weighted regression-based method (GWR), and random forest (RF) method before and after their improvements. Visual interpretation and quantitative assessments revealed that the DCF could improve the accuracy of DisTrad, GWR, and RF methods with an increase in R2 by approximately 0.09 and a decrease in RMSE by more than 0.4 °C. In the cases of LST downscaling over highly heterogeneous contexts and water bodies, N-DLST effectively preserved the textures and large-scale variations, yielding the most consistent spatial pattern with the reference LST. Given the simplicity of the modelling process and absence of auxiliary data, the DCF could strengthen the performance of both linear and nonlinear LST downscaling methods, while the N-DLST method could serve as an effective tool for high-resolution LST prediction.

João A. M. Santos, Miguel S. E. Martins, Rui M. Pinto et al.

Within the framework of sustainable supply chain management and logistics, this work tackles the complex challenge of optimizing inventory levels across varied storage facilities. It introduces a comprehensive many-objective optimization model designed to minimize holding costs, energy consumption, and shortage risk concurrently, thereby integrating sustainability considerations into inventory management. The model incorporates the distinct energy consumption profiles associated with various storage types and evaluates the influence of stock levels on energy usage. Through an examination of a 60-day production schedule, the dynamic relationship between inventory levels and operational objectives is investigated, revealing a well-defined set of optimal solutions that highlight the trade-off between energy savings and shortage risk. Employing a 30-day rolling forward analysis with daily optimization provides insights into the evolving nature of inventory optimization. Additionally, the model is extended to encompass a five-objective optimization by decomposing shortage risk, offering a nuanced comprehension of inventory risks. The outcomes of this research provide a range of optimal solutions, empowering supply chain managers to make informed decisions that strike a balance among cost, energy efficiency, and supply chain resilience.

Sourabrata Mukherjee, Zdeněk Kasner, Ondřej Dušek

Text sentiment transfer aims to flip the sentiment polarity of a sentence (positive to negative or vice versa) while preserving its sentiment-independent content. Although current models show good results at changing the sentiment, content preservation in transferred sentences is insufficient. In this paper, we present a sentiment transfer model based on polarity-aware denoising, which accurately controls the sentiment attributes in generated text, preserving the content to a great extent and helping to balance the style-content trade-off. Our proposed model is structured around two key stages in the sentiment transfer process: better representation learning using a shared encoder and sentiment-controlled generation using separate sentiment-specific decoders. Empirical results show that our methods outperforms state-of-the-art baselines in terms of content preservation while staying competitive in terms of style transfer accuracy and fluency.

Sayra Cristancho, Graham Thompson

The resilience of a healthcare system hinges on the adaptability of its teams. Thus far, healthcare teams have relied on well-defined scopes of practice to fulfill their safety mandate. While this feature has proven effective when dealing with stable situations, when it comes to disruptive events, healthcare teams find themselves navigating a fine balance between safety and resilience. Therefore, a better understanding of how the safety vs resilience trade-off varies under different circumstances is necessary if we are to promote and better train for resilience in modern healthcare teams. In this paper, we aim to bring awareness to the sociobiology analogy that healthcare teams might find useful during moments when safety and adaptability have the potential to conflict. Three principles underpin the sociobiology analogy: communication, decentralization, and plasticity. Of particular interest in this paper is plasticity whereby swapping roles or tasks becomes an adaptive, rather than a maladaptive, response teams could embrace when facing disruptive situations. While plasticity has naturally evolved in social insects, infusing plasticity in healthcare teams requires intentional training. Inspired by the sociobiology analogy, such training must value the ability: a) to read each other’s cues and miscues, b) to step aside when others had the necessary skills, even if outside their scope, c) to deviate from protocols, and d) to foster cross-training. If the goal is to increase a team’s behavioural flexibility and boost their resilience, this training mindset should become second nature.

Célestin Coquidé, José Lages, Dima L. Shepelyansky

From the Bretton Woods agreement in 1944 till the present day, the US dollar has been the dominant currency in the world trade. However, the rise of the Chinese economy led recently to the emergence of trade transactions in Chinese yuan. Here, we analyze mathematically how the structure of the international trade flows would favor a country to trade whether in US dollar or in Chinese yuan. The computation of the trade currency preference is based on the world trade network built from the 2010-2020 UN Comtrade data. The preference of a country to trade in US dollar or Chinese yuan is determined by two multiplicative factors: the relative weight of trade volume exchanged by the country with its direct trade partners, and the relative weight of its trade partners in the global international trade. The performed analysis, based on Ising spin interactions on the world trade network, shows that, from 2010 to present, a transition took place, and the majority of the world countries would have now a preference to trade in Chinese yuan if one only consider the world trade network structure.

Wonguk Cho, Daekyung Lee, Beom Jun Kim

The international trade network is a complex system where multiple trade blocs with varying sizes coexist and overlap with each other. However, the resulting structures of community detection in trade networks are often inconsistent and fails to capture the complex landscape of international trade. To address these problems, we propose a multiresolution framework that aggregates all the configuration information from a range of resolutions. This allows us to consider trade communities of different sizes and illuminate the underlying hierarchical structure of trade networks and its constituting blocks. Furthermore, by measuring membership inconsistency (MeI) of each country and conducting multiple regression analysis with various economic and political indicators, we demonstrate that there exists a positive correlation between the external instability of countries and their structural inconsistency in terms of network topology.

Yutong Lu, Gesine Reinert, Mihai Cucuringu

The time proximity of high-frequency trades can contain a salient signal. In this paper, we propose a method to classify every trade, based on its proximity with other trades in the market within a short period of time, into five types. By means of a suitably defined normalized order imbalance associated to each type of trade, which we denote as conditional order imbalance (COI), we investigate the price impact of the decomposed trade flows. Our empirical findings indicate strong positive correlations between contemporaneous returns and COIs. In terms of predictability, we document that associations with future returns are positive for COIs of trades which are isolated from trades of stocks other than themselves, and negative otherwise. Furthermore, trading strategies which we develop using COIs achieve conspicuous returns and Sharpe ratios, in an extensive experimental setup on a universe of 457 stocks using daily data for a period of four years.

Emma M. Baxter, Vivi A. Moustsen, Sébastien Goumon et al.

There are animal welfare concerns about the continued use of permanent crating systems for farrowing and lactating sows, which is the most prevalent maternity system in global pig production. Greater societal attention in recent years has culminated in changes (or proposed changes) to regulations as well as market-driven initiatives to move away from crated systems. Transitioning from farrowing crates to systems that allow the sow greater freedom of movement and behavioral expression requires a number of key decisions, with various trade-offs apparent when trying to balance the needs of different stakeholders. This review discusses these decisions based on common questions asked by farmers, policy makers and other stakeholders when deciding on a new system to build/approve. Based on the latest scientific evidence and practical insight, decisions such as: whether to retrofit an existing barn or build a new one, what spatial dimensions are necessary per sow place, whether to adopt free farrowing or temporary crating, how to provide substrate/enrichment and be hygienic and environmentally friendly, and how to optimize the human inputs and transition between systems are considered. The aim of this paper is to provide a roadmap for those interested in uptake of higher welfare systems and practices, as well as to highlight areas requiring further optimization and research.