{
  "title": "How Nations Become Fragile: An AI-Augmented Bird’s-Eye View (with a Case Study of South Sudan)",
  "publication": "IMF Working Papers, August 11, 2023",
  "sourceUrl": "https://www.imf.org/en/publications/wp/issues/2023/08/11/how-nations-become-fragile-an-ai-augmented-birds-eye-view-with-a-case-study-of-south-sudan-537693",
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  "summary": "In this study we introduce and apply a set of machine learning and artificial intelligence techniques to analyze multi-dimensional fragility-related data.",
  "sections": [
    {
      "heading": "Study objective and scope",
      "content": "- Introduce and apply a set of machine learning and artificial intelligence techniques to analyze multi-dimensional fragility-related data.\n- Analyze fragility data collected by the OECD for its States of Fragility index.\n- Apply the methodology to South Sudan to analyze the dynamics behind different aspects of fragility over time.\n- Aim to inform and potentially improve the Fund’s country engagement strategy (CES) and efforts at the country."
    },
    {
      "heading": "Methodology",
      "content": "- Use of machine learning and artificial intelligence techniques to explore multi-dimensional fragility data.\n- Focus on uncovering non-linear relationships and diverse drivers of state fragility.\n- Emphasis on deriving insights that support a nuanced and context-specific understanding of fragility."
    },
    {
      "heading": "Key findings",
      "content": "- ML/AI techniques can provide further insights into the non-linear relationships in fragility data.\n- These techniques reveal diverse drivers of state fragility that may not be apparent from linear analysis.\n- The analysis highlights the importance of a nuanced and context-specific approach to understanding and addressing fragility as a multi-aspect issue.\n- Application to South Sudan demonstrates how the methodology can analyze the dynamics of different fragility aspects over time."
    },
    {
      "heading": "Case study — South Sudan",
      "content": "- South Sudan identified as one of the most fragile countries in the world.\n- The methodology was applied to analyze dynamics behind different aspects of fragility in South Sudan over time.\n- Results from the South Sudan application are presented as actionable analysis that could inform engagement and policy design."
    },
    {
      "heading": "Policy implications and recommended uses",
      "content": "- Insights from ML/AI analysis of fragility data could be used to improve the Fund’s country engagement strategy (CES).\n- Results can support more targeted, context-specific interventions to address multiple, interacting dimensions of fragility.\n- Encourage integrating AI-augmented analysis into country-level diagnostics to better capture non-linearities and heterogeneous drivers."
    },
    {
      "heading": "Thematic tags (as presented in source)",
      "content": "- Artificial intelligence\n- Artificial intelligence and machine learning\n- Machine learning\n- Population and demographics\n- Technology\n- 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\n\nHow Nations Become Fragile: An AI-Augmented Bird’s-Eye View (with a Case Study of South Sudan), IMF Working Paper summary.\n\n---\n\n Content in this bundle\n\n- Working Paper\n  - Working Paper (Markdown version){rel=\"alternate\" type=\"text/markdown\"}\n  - Working Paper (PDF){rel=\"external\" type=\"application/pdf\"}\n\n---\n\nSource: https://www.imf.org/en/publications/wp/issues/2023/08/11/how-nations-become-fragile-an-ai-augmented-birds-eye-view-with-a-case-study-of-south-sudan-537693"
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    "Authors: Tohid Atashbar",
    "Published: August 11, 2023",
    "Series: IMF Working Papers",
    "DOI: https://doi.org/10.5089/9798400252242.001",
    "Introduce and apply a set of machine learning and artificial intelligence techniques to analyze multi-dimensional fragility-related data.",
    "Analyze fragility data collected by the OECD for its States of Fragility index.",
    "Apply the methodology to South Sudan to analyze the dynamics behind different aspects of fragility over time.",
    "Aim to inform and potentially improve the Fund’s country engagement strategy (CES) and efforts at the country.",
    "Use of machine learning and artificial intelligence techniques to explore multi-dimensional fragility data.",
    "Focus on uncovering non-linear relationships and diverse drivers of state fragility.",
    "Emphasis on deriving insights that support a nuanced and context-specific understanding of fragility.",
    "ML/AI techniques can provide further insights into the non-linear relationships in fragility data.",
    "These techniques reveal diverse drivers of state fragility that may not be apparent from linear analysis.",
    "The analysis highlights the importance of a nuanced and context-specific approach to understanding and addressing fragility as a multi-aspect issue.",
    "Application to South Sudan demonstrates how the methodology can analyze the dynamics of different fragility aspects over time.",
    "South Sudan identified as one of the most fragile countries in the world.",
    "The methodology was applied to analyze dynamics behind different aspects of fragility in South Sudan over time.",
    "Results from the South Sudan application are presented as actionable analysis that could inform engagement and policy design.",
    "Insights from ML/AI analysis of fragility data could be used to improve the Fund’s country engagement strategy (CES).",
    "Results can support more targeted, context-specific interventions to address multiple, interacting dimensions of fragility.",
    "Encourage integrating AI-augmented analysis into country-level diagnostics to better capture non-linearities and heterogeneous drivers.",
    "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",
    "**Working Paper**"
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