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Dynamics of global trade diplomacy: an artificial intelligence multi-dimensional analysis of preferential trade agreements

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  • Seyed-Ali Sadegh-Zadeh

    (University of Staffordshire)

Abstract

This study leverages advanced artificial intelligence (AI) techniques, including clustering and network analysis, to examine 381 Preferential Trade Agreements (PTAs) from 1958 to 2021, providing new insights into global trade diplomacy. By utilizing predictive modelling, the research identifies key patterns and strategic alignments within PTAs based on the similarity of provisions, enabling the optimization of trade policy decisions. The network analysis highlights the central role of Europe in shaping global trade, revealing the interconnectedness of trade agreements and their influence on regional integration and competition policies. Additionally, the study offers prescriptive insights by assessing the impact of specific provisions, such as competition policy and state aid, on national and international trade outcomes. This machine learning-assisted framework provides a structured and reproducible approach to uncovering hidden patterns in PTA provisions and trade relationships. While not designed for causal inference or direct simulation, it offers valuable empirical insights that can support exploratory policy analysis and inform future trade diplomacy. The findings demonstrate the potential of AI in optimizing trade strategies and support the development of more informed, data-driven decisions in the global trade landscape.

Suggested Citation

  • Seyed-Ali Sadegh-Zadeh, 2025. "Dynamics of global trade diplomacy: an artificial intelligence multi-dimensional analysis of preferential trade agreements," Journal of Computational Social Science, Springer, vol. 8(3), pages 1-33, August.
    • Handle: RePEc:spr:jcsosc:v:8:y:2025:i:3:d:10.1007_s42001-025-00393-9
    DOI: 10.1007/s42001-025-00393-9
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