Multi-agent role-playing has recently shown promise for studying social behavior with language agents, but existing simulations are mostly monolingual and fail to model cross-lingual interaction, an essential property of real societies. We introduce MASim, the first multilingual agent-based simulation framework that supports multi-turn interaction among generative agents with diverse sociolinguistic profiles. MASim offers two key analyses: (i) global public opinion modeling, by simulating how attitudes toward open-domain hypotheses evolve across languages and cultures, and (ii) media influence and information diffusion, via autonomous news agents that dynamically generate content and shape user behavior. To instantiate simulations, we construct the MAPS benchmark, which combines survey questions and demographic personas drawn from global population distributions. Experiments on calibration, sensitivity, consistency, and cultural case studies show that MASim reproduces sociocultural phenomena and highlights the importance of multilingual simulation for scalable, controlled computational social science.

President Trump's tariff standoff with China has caused chaos, confusion, and major delays for companies of all shapes and sizes. As everyone waits to see what happens next, some businesses that depend on international trade are already feeling major impacts, saying that they might not meet their production deadlines. And one of those deadlines is pretty important: Christmas. Today on the show, we're joined by WIRED's senior business editor Louise Matsakis to talk through the latest on tariffs. Mentioned in this episode: Donald Trump Is Already Ruining Christmas by Zeyi Yang OpenAI Adds Shopping to ChatGPT in a Challenge to Google by Reece Rogers The Agonizing Task of Turning Europe's Power Back On by Natasha Bernal Write to us at uncannyvalley@wired.com.

The Harmonized Tariff System (HTS) classification industry, essential to e-commerce and international trade, currently lacks standardized benchmarks for evaluating the effectiveness of classification solutions. This study establishes and tests a benchmark framework for imports to the United States, inspired by the benchmarking approaches used in language model evaluation, to systematically compare prominent HTS classification tools. The framework assesses key metrics--such as speed, accuracy, rationality, and HTS code alignment--to provide a comprehensive performance comparison. The study evaluates several industry-leading solutions, including those provided by Zonos, Tarifflo, Avalara, and WCO BACUDA, identifying each tool's strengths and limitations. Results highlight areas for industry-wide improvement and innovation, paving the way for more effective and standardized HTS classification solutions across the international trade and e-commerce sectors.

The Environmental Extended Multi-Regional Input-Output analysis is the predominant framework in Ecological Economics for assessing the environmental impact of economic activities. This paper introduces ExioML, the first Machine Learning benchmark dataset designed for sustainability analysis, aimed at lowering barriers and fostering collaboration between Machine Learning and Ecological Economics research. A crucial greenhouse gas emission regression task was conducted to evaluate sectoral sustainability and demonstrate the usability of the dataset. We compared the performance of traditional shallow models with deep learning models, utilizing a diverse Factor Accounting table and incorporating various categorical and numerical features. Our findings reveal that ExioML, with its high usability, enables deep and ensemble models to achieve low mean square errors, establishing a baseline for future Machine Learning research. Through ExioML, we aim to build a foundational dataset supporting various Machine Learning applications and promote climate actions and sustainable investment decisions.

As a result, KR is critical to offering a simple strategy for defining relevant and contextual information within a finite number of facts from a specific domain of interest; these facts are referred to as a knowledge base (KB). In the past years, Knowledge Graph (KG), as a form of KR, has gained attention because it provides a contextual, natural, and human-like form of representing knowledge in specific domains and common sense. KG is formed in statements called triples on the T = (h, r, t) form, where h (head) and t (tail) represent objects in real life, and r, the relation is the connection between those entities. Internet companies like Google, Wikipedia, and Facebook have found a simple but powerful unified tool in the KG field to describe their multi-structured and multi-dimensional knowledge base, capturing user data to transform it into vast KBs [3]. The KG approach is particularly relevant to studying international trade, a significant cornerstone of economic and social development in the globalized economy [4, 5]. International trade is complex and interconnected, with multiple entities (commodities, companies, and countries) interacting in multiple ways [6]. This method helps to understand those complex interactions in a structured and intuitive way. In international economics, the gravity model, a fundamental part of the current method, is widely used to predict trade relations between entities based on factors like size (GDP, population) and distance or other factors [7, 8, 9].

The presence of a high number of zero flow trades continues to provide a challenge in identifying gravity parameters to explain international trade using the gravity model. Linear regression with a logarithmic linear equation encounters an indefinite value on the logarithmic trade. Although several approaches to solving this problem have been proposed, the majority of them are no longer based on linear regression, making the process of finding solutions more complex. In this work, we suggest a two-step technique for determining the gravity parameters: first, perform linear regression locally to establish a dummy value to substitute trade flow zero, and then estimating the gravity parameters. Iterative techniques are used to determine the optimum parameters. Machine learning is used to test the estimated parameters by analyzing their position in the cluster. We calculated international trade figures for 2004, 2009, 2014, and 2019. We just examine the classic gravity equation and discover that the powers of GDP and distance are in the same cluster and are both worth roughly one. The strategy presented here can be used to solve other problems involving log-linear regression.

Physicists may say otherwise, but it is trade that makes the world go round -- at least financially. From supply chain issues to volatility in prices across asset classes, from stocks to crude oil, trade defines much of the movement in the international economy. With trillions of dollars moving daily across the financial system, the temptation to indulge in surreptitious behaviour is great. Regulators, compliance officers and banking leaders have long sought effective tools to combat the increasing sophistication of bad actors, whose wrongdoing frequently leads to billions of dollars in financial losses. Compliance officers and regulators are looking to identify criminal actions such as insider trading, market manipulation, money laundering, violations of sanctions/export controls and trading in others' accounts more accurately and quickly.

Without maritime transportation, the global economy would cease to exist. Accounting for 80% of worldwide trade, the maritime transportation industry influences the economic sustainability of each and every country as it provides a safer, more viable method of international commerce. Maritime shipping is the more preferred method, but oceanic travel is an area that is greatly congested with a plethora of serious conflicts. Thus, Israel's startup ecosystem is using advanced intelligence to secure the knot with innovative technologies working towards solving the issues associated with maritime transportation. Each startup listed below is hyper-focused on a specific maritime transportation issue– creating a culmination of service towards such a widespread struggle.

The global impact of artificial intelligence is vast. To some extent, it has been completed, and more development is needed. International business growth, AI and global expansion are usually inseparable. The McKinsey Global Institute recently analyzed economic data from the United Nations, the World Economic Forum, and the World Bank. The report claims that by 2030, artificial intelligence can increase the global economy by 16% or about $13 trillion. It can also increase the global gross domestic product by as much as 26%.

London RegTech firm's cutting-edge AI enables financial institutions to validate tax forms in seconds. Success in the US has led to surging revenues and workforce growth of 46% this year despite the pandemic. The UK recent concluded its fourth round of trade negotiations with the US and in an ever-changing regulatory landscape, TAINA Technology's innovative software expedites compliance, enabling businesses to validate tax forms in seconds and lower costs. For many firms, RegTech: regulatory technology, has become key to ensuring compliance across jurisdictions at reduced costs. Using machine learning, a form of artificial intelligence in which computer algorithms improve through experience, TAINA's platform cuts costs by 84% by reducing time spent validating tax forms by over 75% and tax form rejection rates by over 85%.