Hasil untuk "Commercial geography. Economic geography"

Nahid Sultana, M M Rizvi, Indu Wadhawan

The management of irrigation water systems has become increasingly complex due to competing demands for agricultural production, groundwater sustainability, and environmental flow requirements, particularly under hydrologic variability and climate uncertainty. Addressing these challenges requires optimization frameworks that can jointly determine optimal crop allocation, groundwater pumping, and environmental flow releases while maintaining economic and hydrological feasibility. However, existing hydro-economic models, including the widely used Lewis and Randall formulation, may overestimate net benefits by allowing infeasible negative pumping and surface water allocations. We extend the Lewis and Randall framework by reformulating groundwater pumping and surface water use as non-negative, demand-driven decision variables and by explicitly incorporating environmental flow and canal capacity constraints. Three models are developed to maximize economic benefit, minimize environmental deficits, and a multiobjective model that evaluates the trade-offs between these two objectives. An illustrative test case examining optimal crop area allocation and environmental flow management across dry, average, and wet years, using data from the Rajshahi Barind Tract in northwestern Bangladesh, is presented. The results show that the proposed formulation produces economically and hydrologically consistent solutions, identifying optimal strategies when either net benefits or environmental protection is prioritized, as well as Pareto-optimal trade-offs when both objectives are considered together. These findings provide practical insights for balancing farm income, groundwater sustainability, and ecological protection, offering a robust decision-support tool for irrigation management in water-limited river basins.

Tianyu Fan

This paper introduces a new event-based measure of bilateral geopolitical alignment and provides evidence that it shapes economic growth. The measure is built from 373,020 geopolitical events across 193 countries over 1960--2024, compiled using large language models. With local projections exploiting within-country temporal variation, we find that a one-standard-deviation permanent improvement in geopolitical alignment increases GDP per capita by approximately 10 percent over 25 years. These effects are associated with improvements in domestic stability, investment, productivity, trade, and human capital. In accounting exercises, geopolitical factors account for GDP variations ranging from -30 to +30 percent across countries and time periods.

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.

Mahdi Goldani, Soraya Asadi Tirvan

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.

T. J. Davidge

Commercial flatbed scanners have the potential to deliver a quick and efficient means of capturing the scientific content of spectra recorded on photographic plates. We discuss the digitization of selected spectra in the Dominion Astrophysical Observatory (DAO) photographic plate collection with commercial scanners. In this pilot study, emphasis is placed on assessing if the information on the plates can be recovered using Epson V800 and 12000XL scanners; the more complicated issues associated with the shortcomings of photographic materials, such as correcting for nonlinearity, are deferred to a future study. Spectra of Vega that were recorded over ~4 decades with the DAO 1.8 meter telescope are examined. These spectra sample a range of photographic emulsions, plate preparation techniques, calibration information, observing techniques, and spectrograph configuration. A scanning density of 2400 elements per inch recovers information in the spectra. Differences in the modulation transfer function (MTF) of the two scanners are found, with the Epson 12000XL having a superior MTF. Comparisons with a CCD spectrum of Vega confirm that moderately weak features are faithfully recovered in photographic spectra that have been digitized with the 12000XL scanner. The importance of scanning the full plate to cover the light profile of the target and calibration information is emphasized. Lessons learned from these experiments are also presented.

Marcel Ausloos, Philippe Bronlet

We recall the historically admitted prerequisites of Economic Freedom (EF). We have examined 908 data points for the Economic Freedom of the World (EFW) index and 1884 points for the Index of Economic Freedom (IEF); the studied periods are 2000-2006 and 1997-2007, respectively, thereby following the Berlin wall collapse, and including Sept. 11, 2001. After discussing EFW index and IEF, in order to compare the indices, one needs to study their overlap in time and space. That leaves 138 countries to be examined over a period extending from 2000 to 2006, thus 2 sets of 862 data points. The data analysis pertains to the rank-size law technique. It is examined whether the distributions obey an exponential or a power law. A correlation with the country Gross Domestic Product (GDP), an admittedly major determinant of EF, follows, distinguishing regional aspects, i.e. defining 6 continents. Semi-log plots show that the EFW-rank relationship is exponential for countries of high rank ($\ge 20$); overall the log-log plots point to a behaviour close to a power law. In contrast, for the IEF, the overall ranking has an exponential behaviour; but the log-log plots point to the existence of a transitional point between two different power laws, i.e., near rank 10. Moreover, log-log plots of the EFW index relationship to country GDP is characterised by a power law, with a rather stable exponent ($γ\simeq 0.674$) as a function of time. In contrast, log-log plots of the IEF relationship with the country's gross domestic product point to a downward evolutive power law as a function of time. Markedly the two studied indices provide different aspects of EF.

Burin Gumjudpai

We consider demand-side economy. Using Caratheodory's approach, we define empirical existence of equation of state (EoS) and coordinates. We found new insights of thermodynamics EoS, the {\it effect structure}. Rules are proposed as criteria in promoting and classifying an empirical law to EoS status. Four laws of thermodynamics are given for economics. We proposed a method to model the EoS with econometrics. Consumer surplus in economics can not be considered as utility. Concepts such as total wealth, generalized utility and generalized surplus are introduced. EoS provides solid foundation in statistical mechanics modelling of economics and finance.

Antoine Lesage-Landry, Duncan S. Callaway

We extend the regret analysis of the online distributed weighted dual averaging (DWDA) algorithm [1] to the dynamic setting and provide the tightest dynamic regret bound known to date with respect to the time horizon for a distributed online convex optimization (OCO) algorithm. Our bound is linear in the cumulative difference between consecutive optima and does not depend explicitly on the time horizon. We use dynamic-online DWDA (D-ODWDA) and formulate a performance-guaranteed distributed online demand response approach for heating, ventilation, and air-conditioning (HVAC) systems of commercial buildings. We show the performance of our approach for fast timescale demand response in numerical simulations and obtain demand response decisions that closely reproduce the centralized optimal ones.

Sungho Shin, Victor M. Zavala

We consider the problem of computing equilibria (steady-states) for droop-controlled, islanded, AC microgrids that are both economic-optimal and dynamically stable. This work is motivated by the observation that classical optimal power flow (OPF) formulations used for economic optimization might provide equilibria that are not reachable by low-level controllers (i.e., closed-loop unstable). This arises because OPF problems only enforce steady-state conditions and do not capture the dynamics. We explain this behavior by using a port-Hamiltonian microgrid representation. To overcome the limitations of OPF, the port-Hamiltonian representation can be exploited to derive a bilevel OPF formulation that seeks to optimize economics while enforcing stability. Unfortunately, bilevel optimization with a nonconvex inner problem is difficult to solve in general. As such, we propose an alternative approach (that we call probing OPF), which identifies an economic-optimal and stable equilibrium by probing a neighborhood of equilibria using random perturbations. The probing OPF is advantageous in that it is formulated as a standard nonlinear program, in that it is compatible with existing OPF frameworks, and in that it is applicable to diverse microgrid models. Experiments with the IEEE 118-bus system reveal that few probing points are required to enforce stability.

Chaitanya Poolla, Abraham K. Ishihara, Dan Liddell et al.

Commercial buildings account for approximately 35% of total US electricity consumption, of which nearly two-thirds is met by fossil fuels resulting in an adverse impact on the environment. This adverse impact can be mitigated by lowering energy consumption via control of occupant plugload usage in a closed-loop building environment. In this work, we conducted multiple experiments to analyze changes in occupant plugload energy consumption due to incentives and/or visual feedback. The incentives entailed daily monetary values between $5 and $50 administered in a randomized order and the visual feedback consisted of a web-based dashboard aimed at increasing the energy awareness of participants. Experiments were performed in government office and university buildings at NASA Ames Research Park located in Moffett Field, CA. Autoregressive models were constructed to predict expected plugload savings in the presence of exogenous variables. Analysis of the data revealed modulation of plugload energy consumption can be achieved via visual feedback and incentive mechanisms suggesting that occupant-in-the-loop control architectures may be effective in the commercial building environment. Our findings indicate that the mean energy reduction due to visual feedback in office and university environments were ~9.52% and ~21.61%, respectively. By augmenting the visual feedback in the university environment with a monetary incentive, the mean energy reduction was found to be ~24.22%

Amit Chandak, Debojyoti Dey, Bhaskar Mukhoty et al.

Mass public quarantining, colloquially known as a lock-down, is a non-pharmaceutical intervention to check spread of disease. This paper presents ESOP (Epidemiologically and Socio-economically Optimal Policies), a novel application of active machine learning techniques using Bayesian optimization, that interacts with an epidemiological model to arrive at lock-down schedules that optimally balance public health benefits and socio-economic downsides of reduced economic activity during lock-down periods. The utility of ESOP is demonstrated using case studies with VIPER (Virus-Individual-Policy-EnviRonment), a stochastic agent-based simulator that this paper also proposes. However, ESOP is flexible enough to interact with arbitrary epidemiological simulators in a black-box manner, and produce schedules that involve multiple phases of lock-downs.

Renhao Cui, Gagan Agrawal, Rajiv Ramnath

Businesses communicate using Twitter for a variety of reasons -- to raise awareness of their brands, to market new products, to respond to community comments, and to connect with their customers and potential customers in a targeted manner. For businesses to do this effectively, they need to understand which content and structural elements about a tweet make it influential, that is, widely liked, followed, and retweeted. This paper presents a systematic methodology for analyzing commercial tweets, and predicting the influence on their readers. Our model, which use a combination of decoration and meta features, outperforms the prediction ability of the baseline model as well as the tweet embedding model. Further, in order to demonstrate a practical use of this work, we show how an unsuccessful tweet may be engineered (for example, reworded) to increase its potential for success.

Baisravan HomChaudhuri, Pierluigi Pisu

In this paper, we focus on developing driver-in-the loop fuel economic control strategy for multiple connected vehicles. The control strategy is considered to work in a driver assistance framework where the controller gives command to a driver to follow while considering the ability of the driver in following control commands. Our proposed method uses vehicle-to-vehicle (V2V) communication, exploits traffic lights' Signal Phase and Timing (SPAT) information, models driver error injection with Markov chain, and employs scenario tree based stochastic model predictive control to improve vehicle fuel economy and traffic mobility. The proposed strategy is decentralized in nature as every vehicle evaluates its own strategy using only local information. Simulation results show the effect of consideration of driver error injection when synthesizing fuel economic controllers in a driver assistance fashion.

Raghvendra Kannao, Prithwijit Guha

Commercial detection in news broadcast videos involves judicious selection of meaningful audio-visual feature combinations and efficient classifiers. And, this problem becomes much simpler if these combinations can be learned from the data. To this end, we propose an Multiple Kernel Learning based method for boosting successful kernel functions while ignoring the irrelevant ones. We adopt a intermediate fusion approach where, a SVM is trained with a weighted linear combination of different kernel functions instead of single kernel function. Each kernel function is characterized by a feature set and kernel type. We identify the feature sub-space locations of the prediction success of a particular classifier trained only with particular kernel function. We propose to estimate a weighing function using support vector regression (with RBF kernel) for each kernel function which has high values (near 1.0) where the classifier learned on kernel function succeeded and lower values (nearly 0.0) otherwise. Second contribution of this work is TV News Commercials Dataset of 150 Hours of News videos. Classifier trained with our proposed scheme has outperformed the baseline methods on 6 of 8 benchmark dataset and our own TV commercials dataset.

Benjamin Munro, Julia McLachlan

We propose a high level network architecture for an economic system that integrates money, governance and reputation. We introduce a method for issuing, and redeeming a digital coin using a mechanism to create a sustainable global economy and a free market. To maintain a currency's value over time, and therefore be money proper, we claim it must be issued by the buyer and backed for value by the seller, exchanging the products of labour, in a free market. We also claim that a free market and sustainable economy cannot be maintained using economically arbitrary creation and allocation of money. Nakamoto, with Bitcoin, introduced a new technology called the cryptographic blockchain to operate a decentralised and distributed accounts ledger without the need for an untrusted third party. This blockchain technology creates and allocates new digital currency as a reward for "proof-of-work", to secure the network. However, no currency, digital or otherwise, has solved how to create and allocate money in an economically non-arbitrary way, or how to govern and trust a world-scale free enterprise money system. We propose an "Ontologically Networked Exchange" (ONE), with purpose as its highest order domain. Each purpose is defined in a contract, and the entire economy of contracts is structured in a unified ontology. We claim to secure the ONE network using economically non-arbitrary methodologies and economically incented human behaviour. Decisions influenced by reputation help to secure the network without an untrusted third party. The stack of contracts, organised in a unified ontology, functions as a super recursive algorithm, with individual use programming the algorithm, acting as the "oracle". The state of the algorithm becomes the "memory" of a scalable and trustable artificial intelligence (AI). This AI offers a new platform for what we call the "Autonomy-of-Things" (AoT).

Andreas Joseph, Irena Vodenska, Eugene Stanley et al.

The combination of the network theoretic approach with recently available abundant economic data leads to the development of novel analytic and computational tools for modelling and forecasting key economic indicators. The main idea is to introduce a topological component into the analysis, taking into account consistently all higher-order interactions. We present three basic methodologies to demonstrate different approaches to harness the resulting network gain. First, a multiple linear regression optimisation algorithm is used to generate a relational network between individual components of national balance of payment accounts. This model describes annual statistics with a high accuracy and delivers good forecasts for the majority of indicators. Second, an early-warning mechanism for global financial crises is presented, which combines network measures with standard economic indicators. From the analysis of the cross-border portfolio investment network of long-term debt securities, the proliferation of a wide range of over-the-counter-traded financial derivative products, such as credit default swaps, can be described in terms of gross-market values and notional outstanding amounts, which are associated with increased levels of market interdependence and systemic risk. Third, considering the flow-network of goods traded between G-20 economies, network statistics provide better proxies for key economic measures than conventional indicators. For example, it is shown that a country's gate-keeping potential, as a measure for local power, projects its annual change of GDP generally far better than the volume of its imports or exports.

Stelios M. Potirakis, Pavlos I. Zitis, Konstantinos Eftaxias

The field of study of complex systems considers that the dynamics of complex systems are founded on universal principles that may be used to describe a great variety of scientific and technological approaches of different types of natural, artificial, and social systems. Several authors have suggested that earthquake dynamics and the dynamics of economic (financial) systems can be analyzed within similar mathematical frameworks. We apply concepts of the nonextensive statistical physics, on time-series data of observable manifestations of the underlying complex processes ending up to these different extreme events, in order to support the suggestion that a dynamical analogy exists between a financial crisis (in the form of share or index price collapse) and a single earthquake. We also investigate the existence of such an analogy by means of scale-free statistics (the Gutenberg-Richter distribution of event sizes). We show that the populations of: (i) fracto-electromagnetic events rooted in the activation of a single fault, emerging prior to a significant earthquake, (ii) the trade volume events of different shares / economic indices, prior to a collapse, and (iii) the price fluctuation (considered as the difference of maximum minus minimum price within a day) events of different shares / economic indices, prior to a collapse, follow both the traditional Gutenberg-Richter law as well as a nonextensive model for earthquake dynamics, with similar parameter values. The obtained results imply the existence of a dynamic analogy between earthquakes and economic crises, which moreover follow the dynamics of seizures, magnetic storms and solar flares.

Adom Giffin

Econophysics, is based on the premise that some ideas and methods from physics can be applied to economic situations. We intend to show in this paper how a physics concept such as entropy can be applied to an economic problem. In so doing, we demonstrate how information in the form of observable data and moment constraints are introduced into the method of Maximum relative Entropy (MrE). A general example of updating with data and moments is shown. Two specific econometric examples are solved in detail which can then be used as templates for real world problems. A numerical example is compared to a large deviation solution which illustrates some of the advantages of the MrE method.

Samuel E. Vazquez

We study a class of heterogeneous agent-based models which are based on a basic set of principles, and the most fundamental operations of an economic system: trade and product transformations. A basic guiding principle is scale invariance, which means that the dynamics of the economy should not depend on the units used to measure the different products. We develop the idea of a "near-equilibrium" expansion which allow us to study the dynamics of fluctuations around economic equilibrium. This is similar to the familiar "perturbation theory" studied in many areas of physics. We study some simple models of both centralized and decentralized markets. We show the relaxation to equilibrium when appropriate. More interestingly, we study a simple model of a decentralized market that shows a spontaneous transition into a monetary phase. We use mean field theory analysis to provide a statistical interpretation of the monetary phase. Furthermore, we show that such phase can be dynamically unstable. Finally, we study some simple centralized financial markets, one of which shows a speculative bubble and a crash.

Martin Hohnisch, Dietrich Stauffer, Sabine Pittnauer

We investigate the impact of an exogenous environment on the emergence of social herding of economic sentiment. An interactions-driven dynamics of economic sentiment is modeled by an Ising model on a large (two-dimensional) square lattice. The individual states are called optimism and pessimism. The exogenous environment is modeled as a sequence of random events, which might have a positive or negative influence on economic sentiment. These exogenous events can be frequent or rare, have a lasting impact or a non-lasting impact. Impact of events is inhomogeneous over the lattice, as individuals might fail to perceive particular events. We introduce two notions of social herding: permanent herding refers to the situation where an ordered state (i.e. a state with an overwhelming majority of optimists or pessimists) persists over an infinite time horizon, while temporary herding refers to the situation where ordered states appear, persist for some time and decay. The parameter of the inter-agent interaction strength is such as to engender permanent herding without the influence of the environment. To investigate the impact of an environment we determine whether an initially ordered state decays. We consider two cases: in the first case positive and negative events have both the same empirical frequencies and strengths, while in the second case events have the same empirical frequencies but different strengths. (In the first case the environment is ``neutral''in the long term), In the neutral case we find temporary herding if events are sufficiently ``strong'' and/or perceived by a sufficiently large proportion of agents, and our results suggest that permanent herding occurs for small values of the parameters. In the ``non-neutral'' case we find only temporary herding.