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240607 ||| eng |
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|a 9798400252242
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| 100 |
1 |
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|a Atashbar, Tohid
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| 245 |
0 |
0 |
|a How Nations Become Fragile: An AI-Augmented Bird’s-Eye View (with a Case Study of South Sudan)
|c Tohid Atashbar
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| 260 |
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|a Washington, D.C.
|b International Monetary Fund
|c 2023
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| 300 |
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|a 36 pages
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| 653 |
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|a Population & demography
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| 653 |
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|a Environmental Economics: General
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| 653 |
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|a Economic & financial crises & disasters
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| 653 |
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|a Machine learning
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| 653 |
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|a Technological Change: Choices and Consequences
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| 653 |
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|a Socialist Institutions and Their Transitions: Consumer Economics
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| 653 |
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|a Environmental Economics
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| 653 |
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|a Demographic Economics: General
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| 653 |
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|a Environmental economics
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| 653 |
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|a Technology
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| 653 |
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|a Computational Techniques
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| 653 |
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|a Economics: General
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| 653 |
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|a Computer Programs: Other
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| 653 |
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|a Health, Education and Training, Welfare, and Poverty
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| 653 |
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|a Data Collection and Data Estimation Methodology
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| 653 |
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|a Intelligence (AI) & Semantics
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| 653 |
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|a Sanctions
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| 653 |
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|a Diffusion Processes
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| 653 |
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|a Informal sector; Economics
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| 653 |
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|a Negotiations
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| 653 |
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|a Environmental sciences
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| 653 |
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|a Welfare and Poverty: Other
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| 653 |
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|a Economics of specific sectors
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| 653 |
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|a Population and demographics
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| 653 |
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|a Currency crises
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| 653 |
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|a Demography
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| 653 |
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|a International Conflicts
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Artificial intelligence and machine learning
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| 653 |
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|a Macroeconomics
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| 653 |
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|a Population
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| 653 |
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|a Globalization: Economic Development
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b IMF
|a International Monetary Fund
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| 490 |
0 |
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|a IMF Working Papers
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| 028 |
5 |
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|a 10.5089/9798400252242.001
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| 856 |
4 |
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|u https://elibrary.imf.org/view/journals/001/2023/167/001.2023.issue-167-en.xml?cid=537693-com-dsp-marc
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 330
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| 520 |
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|a 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
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