Machine Learning for Strategic Trade Analysis

Abstract

AbstractMachine learning techniques are supporting advances in more effectively gaining knowledge from data in many fields—strategic trade analysis should be no exception. This chapter introduces some potential uses of machine learning approaches to support identifying illicit strategic trade from within international trade data. First, an overview is provided of three case studies that utilized various machine learning algorithms for similar purposes, demonstrating the effectiveness of different approaches that could be tailored to strategic trade analysis. The majority of the chapter focuses on three types of machine learning techniques: supervised classification, unsupervised learning, and natural language processing. Within each of these, multiple methods are introduced with a focus of how analysts can specifically use international trade data and other information sources to investigate strategic trade. This discussion represents attempts to introduce machine learning into this field and serve as a starting point for future research and applications in this area.