Single Window for International Trade: Intelligent Optimization and Computational Social Science Methodological Exploration

Abstract

The rapid evolution of international trade necessitates the adoption of intelligent digital solutions to enhance trade facilitation. The Single Window System (SWS) has emerged as a key mechanism for streamlining trade documentation, customs clearance, and regulatory compliance. However, traditional SWS implementations face challenges such as data fragmentation, inefficient processing, and limited real-time intelligence. This study proposes a computational social science framework that integrates artificial intelligence (AI), machine learning, network analytics, and blockchain to optimize SWS operations. By employing predictive modeling, agent-based simulations, and algorithmic governance, this research demonstrates how computational methodologies improve trade efficiency, enhance regulatory compliance, and reduce transaction costs. Empirical case studies on AI-driven customs clearance, blockchain-enabled trade transparency, and network-based trade policy simulation illustrate the practical applications of these techniques. The study concludes that interdisciplinary collaboration and algorithmic governance are essential for advancing digital trade facilitation, ensuring resilience, transparency, and adaptability in global trade ecosystems.