IMF Working Papers

How Nations Become Fragile: An AI-Augmented Bird’s-Eye View (with a Case Study of South Sudan)

By Tohid Atashbar

August 11, 2023

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Format: Chicago

Tohid Atashbar. How Nations Become Fragile: An AI-Augmented Bird’s-Eye View (with a Case Study of South Sudan), (USA: International Monetary Fund, 2023) accessed November 23, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

In this study we introduce and apply a set of machine learning and artificial intelligence techniques to analyze multi-dimensional fragility-related data. Our analysis of the fragility data collected by the OECD for its States of Fragility index showed that the use of such techniques could provide further insights into the non-linear relationships and diverse drivers of state fragility, highlighting the importance of a nuanced and context-specific approach to understanding and addressing this multi-aspect issue. We also applied the methodology used in this paper to South Sudan, one of the most fragile countries in the world to analyze the dynamics behind the different aspects of fragility over time. The results could be used to improve the Fund’s country engagement strategy (CES) and efforts at the country.

Subject: Artificial intelligence, Artificial intelligence and machine learning, Machine learning, Population and demographics, Technology

Keywords: Aggregate fragility, AI technique, Artificial Intelligence, Artificial intelligence and machine learning, Artificial intelligence technique, Bird's-Eye View, Fragile and Conflict-Affected States, Fragility data, Fragility Syndrome, Fragility Trap, Global, Machine Learning, Middle East, North Africa, South Asia, Sub-Saharan Africa

Publication Details

  • Pages:

    36

  • Volume:

    ---

  • DOI:

    ---

  • Issue:

    ---

  • Series:

    Working Paper No. 2023/167

  • Stock No:

    WPIEA2023167

  • ISBN:

    9798400252242

  • ISSN:

    1018-5941

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