## INTRODUCTION

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---

### Overview and purpose
- Fragility and conflict undermine macroeconomic stability and hinder resource development.
- The IMF assists Fragile and Conflict-Affected States (FCS) members through tailored policy advice, financial assistance, capacity building, and collaboration with international humanitarian, development, peace, and security organizations.
- Country Engagement Strategies (CES) are implemented to ensure targeted and harmonized efforts adapted to specific country needs.
- Central research question: “How can advanced methods such as AI and machine learning contribute to a more nuanced understanding of state fragility and consequently aid in the design of targeted interventions?”

### Conceptual framing of fragility
- Fragility is multifaceted (political, economic, social, physical-environment); relevant to LIC, EMDEs, and developed economies (including "Advanced-Fragile" countries).
- Fragility often manifests as a syndrome of interacting facets that create enduring challenges to long-term growth and governance.
- Collier’s six defining qualities of fragility:
  - Little to no broad shared identity
  - Large portion of population does not view the state as legitimate
  - State unable to perform fundamental duties (taxation, security, law, infrastructure)
  - Existential uncertainty shortening horizons for households and firms
  - Underdeveloped formal private sector causing low productivity and poverty
  - Economy and politics vulnerable to shocks

### Drivers of fragility (literature synthesis)
- Internal and external drivers include weak governance, corruption, poverty, inequality, conflict, international interventions, economic shocks, climate conditions, fragility of international economic systems, spread of extremist ideologies, and erosion of social cohesion.
- Selected empirical findings cited in the literature:
  - Poor institutions influence state fragility more than economic reasons (Vallings and Moreno-Torres, 2005).
  - Political identity fragmentation and inadequate national institutions reinforce each other (Kaplan, 2008).
  - Three factors create a slow-growth-poor-governance equilibrium: political instability and violence, insecure property rights and unenforceable contracts, and corruption (Andrimihaja et al., 2011).
  - Poorer countries are more fragile than affluent ones; trade-open countries are less unstable (Carment et al., 2008, 2011).
  - Country size and ethnic risk/diversity affect fragility (Feeny et al., 2015).
  - Socioeconomic correlates include poor HDI, increased infant mortality, and lower schooling.

### Intended contribution
- Proposes AI and machine learning to capture multicausal and nonlinear nature of state fragility and to provide context-specific insights on drivers of fragility.
- Reviews fragility measurement approaches and applies AI-assisted methodologies to classify fragility trends and inform targeted interventions.

---

### Measuring fragility — key indices and thresholds
- Fragile States Index (FSI)
  - 12 conflict risk indicators grouped into 4 categories: Cohesion, Economic, Political, Social.
  - Each indicator ranked between 0 (best) to 10 (worst); normalized and aggregated.
  - FSI covers 178 countries and territories.
- OECD States of Fragility
  - Multidimensional framework across six dimensions: economic, environmental, human, political, security, societal.
  - Uses mixed-methods: two-stage PCA and hierarchical clustering; described as covering forty-four indicators across all five dimensions while the platform has 57 indicators from third-party sources.
  - Context classified as fragile if score falls below -1.20; highly fragile if below -2.85.
  - Evaluates 176 contexts (defined as data available for at least 70 percent of indicators).
- Country Policy and Institutional Assessment (CPIA)
  - Fragility criteria: CPIA score for IDA countries below 3.0; or presence of a UN peacekeeping operation; or flight across borders of 2,000 or more per 100,000 population from the origin country.
  - CPIA has 16 criteria in four equally weighted clusters; scores range from 1 (low) to 6 (high).

---

### AI and machine learning methodology

### Rationale for ML/AI
- Advantages over traditional econometrics:
  - Capture complex, nonlinear relationships and high-dimensional interactions.
  - Use regularization, ensemble techniques (bagging and boosting), and architectures (RNN, LSTM) to reduce overfitting, handle outliers, and remember long-term sequential patterns.
  - Faster, potentially more cost-effective, and adaptable updates versus expert-judgement processes.
- Cautions:
  - Requires careful curation of training data to avoid perpetuating biases or overfitting noise.
  - Flexible models need disciplined implementation for meaningful, generalizable insights.

### Techniques highlighted and evaluation criteria
- Techniques used for classification, dimension reduction, topic modeling and feature selection:
  - Support Vector Machines (SVMs), Support Vector Clustering (SVC)
  - K-Nearest Neighbors (KNNs)
  - Kernel Principal Component Analysis (KPCA), Nonlinear PCA (NLPCA)
  - Artificial Neural Networks (ANNs): CNNs, RNNs, LSTMs
  - LDA, NMF, Deep Belief Networks (DBNs), Autoencoders
- Evaluation criteria:
  - Accuracy and precision
  - Generalizability (cross-validation)
  - Robustness to small input changes or errors
  - Model complexity and interpretability
  - Speed and efficiency (training and prediction times)

### Implementation specifics in this study
- Data:
  - OECD multidimensional fragility framework: 57 indicators across 6 dimensions (economic, environmental, human, political, security, societal) for 176 countries; covers 99.5 percent of the world's population, including 100 percent of the population of sub-Saharan Africa, Middle East and North Africa, and South Asia.
- Primary methods:
  - KPCA performed for each year from 1979 to 2021 to determine aggregate fragility score (a number between 1 and 100; 1 as least fragile and 100 as the most fragile); results ranked based on KPCA eigenvalues.
  - SVC with a Polynomial kernel used to classify countries by fragility score; performance assessed with confusion matrix.
  - RNN implemented with Keras:
    - three fully connected layers with 20, 10, and 6 neurons
    - optimizer = Adam
    - loss = sparse categorical cross entropy
    - activation functions = ReLU for first two layers and SoftMax for last layer
  - LSTM implemented with Keras to estimate impact of each dimension on aggregate fragility and predict one year ahead:
    - sequential LSTM model with 50 units
    - relu activation
    - kernel regularizer = l2
    - compiled with mean squared error loss and adam optimizer
  - Visualization via plotly Dash interactive dashboard
- Methodological contrast:
  - OECD method: linear PCA + expert-knowledge-based hierarchical clustering
  - This study: nonlinear dimensionality reduction + automated unsupervised procedures (KPCA + SVC/RNN/LSTM)
  - Trade-offs: automated methods capture non-linearities and allow frequent updates; expert judgment may be more accurate in clustering stage.

---

### Empirical findings and diagnostic results

### Cross-country and dimensional patterns (selected findings)
- General distribution (2021):
  - Majority of countries exhibit low or limited fragility; a notable club of countries suffer high fragility.
  - Political, economic and human fragility affect larger numbers of countries (per box plot analysis).
- Environmental fragility (2021):
  - Most environmentally-fragile countries in 2021 included the Philippines, South Sudan and Haiti, with cited events: Super Typhoons Kiko and Odette (Philippines); historic flooding (South Sudan); M7.2 earthquake and Tropical Depression Grace (Haiti).
- Temporal trends (2010–2021):
  - Box plots suggest a decrease in economic fragility across nations, though "additional evidence is required to support this claim."
- Most fragile countries (2010–2021):
  - Between 2020 and 2021, South Sudan had the highest level of fragility for 4 years, followed by Somalia, Syria, and the Central African Republic for 2 years each.

### Country examples
- Syria:
  - Fragility jump in 2011–2012 linked to outbreak of civil war and rise of militant groups (e.g., ISIS).
  - Security fragility drove deterioration in aggregate fragility; relative improvements in economic and social fragility later reduced total fragility but levels remained high.
- Haiti vs Senegal:
  - Haiti experienced a fragility jump in 2010 (major earthquake); Senegal increased in 2009 (severe floods).
  - Environmental fragility spiked in those years for both countries.

---

### Case study — South Sudan

### Background and key metrics
- Declared independence in 2011; characterized as "born fragile."
- 2012 brief war with Sudan halted oil production temporarily, causing significant real GDP reduction.
- 2013 civil war between President Kiir and Vice President Machar: killed hundreds of thousands, displaced 1.5 million internally, and sent over 2 million refugees to neighboring countries.
- South Sudan ranked 164 out of 165 countries on progress toward United Nations Sustainable Development Goals.
- Since April 2021, reforms under an IMF Staff-Monitored Program (SMP) contributed to some improvements in macroeconomic stability.
- South Sudan graduated from SMP in March 2023 and began a SMP with the board involvement (PMB) with the IMF.

### Empirical diagnostics from ML/AI analysis
- Political fragility has remained consistently high since inception; political fragility is a significant contributor to overall fragility.
- Environmental shocks (flooding, drought) exacerbate fragility in certain years.
- Economic fragility showed improvement since 2016–2017.
- Strong correlations observed between security, societal, and political fragility and overall fragility; societal and political fragility strongly correlated with security fragility (heatmap for 2021).
- Sub-indicators of political fragility (risk and coping/resilience-related) have remained elevated with no clear improvements, except for women's political empowerment.

### Policy-relevant diagnostics and CES implications
- Prioritize political and environmental fragility within the IMF’s South Sudan CES to target root causes.
- Support establishment of a stable and inclusive political environment and promote climate-resilient strategies.
- Embed continuous monitoring using AI approaches in periodic program reviews to track correlations across fragility dimensions and target interventions.
- Enhance coordination and information sharing among IMF, development partners, authorities, and civil society to harmonize efforts and avoid redundancies.
- Economic recommendations: comprehensive economic and public finance reforms, investments in infrastructure, and improved agricultural techniques.
- Governance recommendations: sustainable governance strategies that prioritize inclusivity, dialogue, and addressing legacy political issues underpinning fragility.

---

### Summary and policy discussion — broader lessons
- Contributions of the study:
  - Reinforces need for a multidimensional, context-specific approach to state fragility.
  - Demonstrates utility of AI/ML techniques to capture complex, nonlinear relationships and improve reliability and accuracy of fragility assessments.
  - Shows benefits of unsupervised techniques (SVC, KPCA) in identifying dimensions and sub-indicators that may be less apparent via expert judgment alone.
  - Highlights speed, cost-effectiveness, and adaptability benefits of automated ML/AI approaches for more frequent updating of fragility measures.
- Policy takeaways:
  - AI/ML-informed fragility analysis can inform targeted interventions and resource allocation to dimensions with largest impact (e.g., political and environmental fragility in South Sudan).
  - Embedding AI-driven monitoring into regular engagement (e.g., periodic reviews) can make CES and similar strategies more adaptive and evidence-based.
  - Coordination and ongoing dialogue among international partners are critical to align interventions across multidimensional fragility drivers.

*Source: wpiea2023167-print-pdf — How Nations Become Fragile: an AI-Augmented Bird’s-Eye View (With a Case Study of South Sudan) Working Paper No. WP/2023/167*

### INTRODUCTION ...........................................................................................................

### INTRODUCTION

### Overview and Structure
- The document is organized into major sections:
  - INTRODUCTION (page 5)
  - I. FRAGILITY: A DIVE INTO THE LITERATURE (page 6)
    - A. Definition(s) of Fragility (page 6)
    - B. Drivers of Fragility (page 8)
    - C. Mitigating Fragility (page 9)
  - II. MEASURING FRAGILITY (page 10)
    - A. Fragile States Index (page 10)
    - B. States of Fragility (page 10)
    - C. Country Policy and Institutional Assessment (page 12)
    - D. Other Measures (page 13)
  - III. AI TECHNIQUES TO EXPLORE FRAGILITY-RELATED DATA (page 14)
    - A. Why Use AI and Machine Learning When Traditional Approaches Are Available? (page 14)
    - B. AI Techniques for Classification and Dimension Reduction (page 15)
    - C. Application to OECD Data (page 18)
  - IV. THE CASE OF SOUTH SUDAN (page 26)
    - A. History and Background (page 26)
    - B. Pillars of the Fund’s South Sudan CES to Tackle Fragility (page 26)
    - C. Findings from this Analysis (page 27)
    - D. Implications from this Study to the Fund’s South Sudan CES (page 29)
  - V. SUMMARY AND POLICY DISCUSSION (page 30)
  - REFERENCES (page 31)

### Figures (inventory)
- Figure 1. Syndrome of Fragility (page unspecified)
- Figure 2. OECD’s States of Fragility (page unspecified)
- Figure 3. RNN vs LSTM Architecture (page unspecified)
- Figure 4. Fragility in Syria in the last decade (page unspecified)
- Figure 5. Different aspects of fragility in Syria in the Last Decade (page unspecified)
- Figure 6. Fluctuation of Aggregate Fragility in Haiti and Senegal (page unspecified)
- Figure 7. Fluctuation of Fragility Dimensions in Haiti and Senegal (page unspecified)
- Figure 8. Box Plot of Aggregate Fragility in 2021 (page unspecified)
- Figure 9. Box Plot of Dimensional Fragilities in 2021 (page unspecified)
- Figure 10. Most Environmentally-Fragile Countries in 2021 (page unspecified)
- Figure 11. Box Plot of Aggregate Fragility 2010–2021 (page unspecified)
- Figure 12. Box Plot of Economic Fragility 2010–2021 (page unspecified)
- Figure 13. Box Plot of Environmental Fragility 2010–2021 (page unspecified)
- Figure 14. Aggregate Fragility in South Sudan Over Time (page unspecified)
- Figure 15. Various Aspects of Fragility in South Sudan Over Time (page unspecified)
- Figure 16. Heatmap of Correlation of Dimensional Fragilities for South Sudan in 2021 (page unspecified)
- Figure 17. Risk and Coping Political Fragility Sub-Indicators for South Sudan (page unspecified)

### Tables (inventory)
- Table 1. Definitions of fragile states by various international organizations (page unspecified)
- Table 2. Indicators of the Fragile States Index (page unspecified)
- Table 3. CPIA Criteria (page unspecified)
- Table 4. A Sample Data from OECD’s Fragility Dataset (page unspecified)
- Table 5. Most Fragile Countries (2010–2021) (page unspecified)

### Glossary — Key acronyms (as listed)
- AI — Artificial Intelligence
- ANN — Artificial Neural Networks
- CAST — Conflict Assessment Framework
- CES — Country Engagement Strategy
- CNN — Convolutional Neural Networks
- CPIA — Country Policy and Institutional Assessment
- FCS — Fragile and Conflict-Affected States
- FSI — Failed States Index
- FSI — Fragile States Index
- IDA — International Development Association
- ISW — Index of State Weakness
- KNNs — K-Nearest Neighbors
- KPCA — Kernel Principal Component Analysis
- LDA — Latent Dirichlet Allocation
- LSTM — Long Short-Term Memory Networks
- ML — Machine Learning
- NLPCA — Non-Linear Principal Component Analysis
- NMF — Non-Negative Matrix Factorization
- OECD — Organization for Economic Co-operation and Development
- PCA — Principal Component Analysis
- RNN — Recurrent Neural Networks
- SVC — Support Vector Clustering
- SVM — Support Vector Machines

*Source: wpiea2023167-print-pdf — https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023167-print-pdf.pdf*

### Introduction

### wpiea2023167-print-pdf - Introduction

### Overview
- Fragility and conflict undermine macroeconomic stability and hinder resource development.
- The IMF assists Fragile and Conflict-Affected States (FCS) members through a tailored approach emphasizing customized policy advice, financial assistance, capacity building, and collaboration with international humanitarian, development, peace, and security organizations.
- IMF teams implement Country Engagement Strategies (CES) to ensure targeted and harmonized efforts adapted to specific country needs.

### Fragility: concept and aim of the paper
- Fragility is multifaceted and can stem from political, economic, social, and physical-environment factors; it is relevant to LIC, EMDEs, and developed economies (including "Advanced-Fragile" countries with fragility in one or a few dimensions).
- The paper proposes the use of AI and machine learning to develop more sophisticated measures that capture the multicausal and nonlinear nature of state fragility and to provide context-specific insights on drivers of fragility.
- Central research question: “How can advanced methods such as AI and machine learning contribute to a more nuanced understanding of state fragility and consequently aid in the design of targeted interventions?”

### Key analytic approach and data
- The paper:
  - Reviews economic literature on fragility and its causes and effects, framing fragility as a syndrome of interacting facets.
  - Examines fragility measurement approaches including the World Bank classification, OECD's States of Fragility Index, and the Fragile States Index (FSI).
  - Applies AI-assisted methodologies to produce a big-picture classification of fragility trends using machine learning algorithms.
- Machine learning methods mentioned:
  - Recurrent Neural Networks for processing sequential data
  - Support Vector Machines for classification tasks
  - Kernel PCA for nonlinear dimensionality reduction
- Input data: a comprehensive dataset of fragility-related risk and coping indicators collected by OECD (States of Fragility 2022) for all nations.

### Literature and definitions (major points)
- No single consensus definition of "fragility"; common traits include limitations in institutional and policy implementation, turbulent political backdrop, severe domestic resource limits, and high vulnerability to shocks.
- Academic definitions emphasize limited institutional capability, political instability, and poor governance that impair security provision and basic public services.
- Collier’s characteristics of fragility include: little or no broad shared identity, lack of governmental legitimacy, lack of capacity, existential uncertainties, underdeveloped private sector, and significant exposure to political and economic shocks.
- Fragility often manifests as a syndrome of interconnected traits that create enduring challenges to long-term growth and governance.

### Drivers of fragility (findings from literature synthesis)
- Internal and external drivers include weak governance, corruption, poverty, inequality, conflict, international interventions, economic shocks, climate conditions, fragility of international economic systems, spread of extremist ideologies, and erosion of social cohesion.
- Specific research findings cited:
  - Poor institutions influence state fragility more than economic reasons (Vallings and Moreno-Torres, 2005).
  - Political identity fragmentation and inadequate national institutions reinforce each other (Kaplan, 2008).
  - Three factors create a slow-growth-poor-governance equilibrium: political instability and violence, insecure property rights and unenforceable contracts, and corruption (Andrimihaja et al., 2011).
  - Poorer countries are more fragile than affluent ones; trade-open countries are less unstable (Carment et al., 2008, 2011).
  - Country size and ethnic risk/diversity affect fragility (Feeny et al., 2015).
  - Socioeconomic correlates include poor HDI, increased infant mortality, and lower schooling.

### Fragility syndrome (Collier summary)
- Six defining qualities:
  - Little to no broad shared identity
  - Large portion of population does not view the state as legitimate
  - State unable to perform fundamental duties (taxation, security, law, infrastructure)
  - Existential uncertainty shortening horizons for households and firms
  - Underdeveloped formal private sector causing low productivity and poverty
  - Economy and politics vulnerable to shocks

### Mitigating fragility (policy implications and recommendations)
- Transition from fragility to resilience requires mutually reinforcing improvements in capacity and governance stressors and recognition of the link between macroeconomic stability and fragility.
- Prioritization is necessary because most FCS face multiple stressors (poor service delivery, low capacity, high-risk governance) and progress is unlikely to be linear.
- Effective interventions identified in case studies:
  - Enhance capacity to mobilize revenues efficiently
  - Strengthen budget and financial management controls
  - Build fiscal institutions and expand fiscal space to support resilience
  - Invest in capacity building with attention to absorptive capacity constraints (use of on-the-ground experts, realistic impact assessment tools, adequate financing)
  - Strengthen legitimate institutions and governance to provide security, justice, and jobs
- The IMF’s FCS Strategy (IMF, 2022) provides extended and tailored support to achieve macroeconomic stability, bolster resilience, and foster sustainable and equitable growth, emphasizing collaboration with international humanitarian, development, peace, and security organizations.

### Measuring fragility: indices and methodologies
- Fragile States Index (FSI)
  - Annual ranking by the Fund for Peace based on CAST framework.
  - Uses 12 conflict risk indicators grouped into 4 categories: Cohesion, Economic, Political, Social.
  - Each of the 12 indicators is ranked between 0 (best) to 10 (worst), normalized and aggregated into a composite index.
  - FSI covers 178 countries and territories.
- OECD States of Fragility
  - Multidimensional fragility framework across six dimensions: economic, environmental, human, political, security, and societal.
  - Uses a mixed-methods approach with a two-stage principal components analysis (PCA) and hierarchical clustering.
  - Described as covering forty-four indicators across all five dimensions (text also notes the States of Fragility platform has 57 indicators drawn from independent third-party sources).
  - A context is defined as fragile if its score falls below -1.20 or as highly fragile if it falls below -2.85.
  - The OECD analysis (States of Fragility in 2022 for the 2021 data cutoff) evaluates 176 contexts for which sufficient data are available (defined as data for at least 70 percent of indicators being accessible for a context).
  - PCA reduces indicators to two core components but entails information loss, particularly after the second-stage PCA.
- Country Policy and Institutional Assessment (CPIA)
  - Used by World Bank/IMF to categorize countries as fragile: a country is in fragility if it has CPIA score for IDA countries below 3.0 or presence of a UN peacekeeping operation or flight across borders of 2,000 or more per 100,000 population from the origin country (WB/IMF quantitative methodology).
  - CPIA assesses institutional and policy effectiveness and is used to determine aid and financing eligibility and to assess outcomes of policy and aid interventions.
  - The CPIA has 16 criteria divided into four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions.
  - Countries are scored 1 (low) to 6 (high) on each of the 16 categories; cluster and composite country ratings are averages of these ratings.

### AI and machine learning contribution (methodological promise)
- AI and machine learning can:
  - Produce a multidimensional view of state fragility and identify complex, nonlinear relationships among drivers.
  - Provide context-specific insights on drivers of fragility to design targeted interventions.
- The paper applies these methods to classify countries and analyze fragility trends using OECD fragility-related risk and coping indicators.

### South Sudan: case study preview
- South Sudan is presented as one of the most fragile nations in the world.
- The paper will provide:
  - A brief history and context of South Sudan
  - An analysis of causes and trends of fragility in South Sudan using results from the AI/ML analysis
  - Exploration of systematic ways to address the country’s fragility

### Conclusion and intended contribution
- The paper concludes with a summary of the analysis and ramifications for policymakers and stakeholders.
- Aim: to advance more effective strategies to address fragility and build resilience in fragile states, and to broaden understanding of the fragility landscape and its multiple causes.

*Source: wpiea2023167-print-pdf - Introduction*

### conclusions based on country expertise gained from within or outside the Bank as well as pertinent publicly

### wpiea2023167-print-pdf - conclusions based on country expertise gained from within or outside the Bank as well as pertinent publicly available metrics

### Overview
- The conclusions are based on country expertise gained from within or outside the Bank as well as pertinent publicly available metrics.

### Table 3. CPIA Criteria — Clusters and List of Criteria

- Economic Management
  - 1. Monetary and Exchange Rate Policies
  - 2. Fiscal Policy
  - 3. Debt Policy and Management

- Structural Policies
  - 1. Trade
  - 2. Financial Sector
  - 3. Business Regulatory Environment

- Policies for Social Inclusion/Equity
  - 1. Gender Equality
  - 2. Equity of Public Resource Use
  - 3. Building Human Resources
  - 4. Social Protection and Labor
  - 5. Policies and Institutions for Environmental Sustainability

- Public Sector Management and Institutions
  - 1. Property Rights and Rule-based Governance
  - 2. Quality of Budgetary and Financial Management
  - 3. Efficiency of Revenue Mobilization
  - 4. Quality of Public Administration

*Source: wpiea2023167-print-pdf - conclusions based on country expertise gained from within or outside the Bank as well as pertinent publicly available metrics*

### 5. Transparency,

### 5. Transparency, Accountability, and Corruption in the Public Sector

### Fragility and Conflict Classification (World Bank / OECD context)
- Fragility defined as "a systemic condition or situation characterized by an extremely low level of institutional and governance capacity which significantly impedes the state’s ability to function effectively, maintain peace and foster economic and social development."
- Countries/territories in a situation of Fragility are identified by the combination of:
  - 1) (a) the CPIA score for IDA countries (for which CPIA scores are disclosed) that is below 3.0; or (b) the presence of a UN peacekeeping operation; or (c) flight across borders of 2,000 or more per 100,000 population from the origin country or territory, who are internationally regarded as refugees in need of international protection; and
  - 2) Those that are not in conflict.
- Conflict defined as "a situation of acute insecurity driven by the use of deadly force by a group—including state forces, organized non-state groups, or other irregular entities—with a political purpose or motivation."
- Countries/territories in Conflict are identified by the combination of:
  - 1) Countries in ongoing conflict, as measured by (a) an absolute number of conflict deaths above 250 according to Armed Conflict Location & Event Data Project (ACLED) and 150 according to UCDP; and (b) above 2 per 100,000 population according to ACLED and above 1 according to UCDP; or
  - 2) Countries with a rapid deterioration of the security situation, as measured by (a) an absolute number of conflict deaths above 250 according to ACLED and 150 according to Uppsala Conflict Data Project (UCDP); (b) between 1 and 2 (ACLED) and 0.5 and 1 (UCDP) per 100,000 population; and (c) more than a doubling of the number of casualties in the last year.

### Use of OECD States of Fragility dataset
- Rationale for dataset choice:
  - Detailed multi-dimensional, multi-aspect approach to gauging state fragility.
  - Well-documented and exhaustive dataset with raw, unprocessed values from various indicators.
  - Captures political, economic, social and environmental aspects; includes environmental issues and coping capacities/resilience indicators in addition to risk factors.
- Note on alternative indices:
  - Other measures include State Fragility Index (SFI), Index of State Weakness (ISW) and Failed States Index (FSI) but these are no longer produced or not frequently used.
  - Comparing indices often yields similar rankings for the most fragile countries; deviations appear in intricate details.

### AI and Machine Learning (ML) advantages over traditional methods for fragility analysis
- AI aims to replicate human cognition; ML provides algorithms to uncover insights, make decisions, and refine performance.
- Key advantages of AI/ML over traditional econometric approaches:
  - Ability to process complex data structures and capture nonlinear relationships.
  - Better handling of high-dimensional datasets and variable interactions (mitigates curse of dimensionality relative to linear regression).
  - Use of regularization methods, ensemble techniques (bagging and boosting), and architectures (RNN, LSTM) to reduce overfitting, handle outliers, and remember long-term sequential patterns.
  - Faster, potentially more cost-effective, and adaptable updates to fragility measures versus expert-judgement driven processes.
- Limitations and cautions:
  - Requires thoughtful curation of training data to avoid perpetuating biases or overfitting noise.
  - Flexible models need disciplined implementation to deliver meaningful, generalizable insights.

### ML/AI Techniques Used and Evaluative Criteria
- Techniques highlighted for classification, dimension reduction, topic modeling and feature selection:
  - Support Vector Machines (SVMs) and Support Vector Clustering (SVC)
  - K-Nearest Neighbors (KNNs)
  - Non-linear versions of Principal Component Analysis (NLPCA, KPCA)
  - Artificial Neural Networks (ANNs): Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs)
  - Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), Deep Belief Networks (DBNs), Autoencoders
- Evaluation criteria for ML/AI methodologies:
  - Accuracy and precision
  - Generalizability (assessed via cross-validation)
  - Robustness to small input changes or errors
  - Model complexity and interpretability
  - Speed and efficiency (training and prediction times)

### Methodology Applied in This Study (implementation specifics)
- Primary techniques used in the case study:
  - 1) Support Vector Clustering (SVC)
  - 2) Kernel Principal Component Analysis (KPCA)
  - 3) Recurrent Neural Networks (RNNs)
  - 4) Long Short-Term Memory Networks (LSTMs)
- Data:
  - OECD multidimensional fragility framework: 57 indicators across 6 dimensions (economic, environmental, human, political, security and societal) for 176 countries; covers 99.5 percent of the world's population, including 100 percent of the population of sub-Saharan Africa, Middle East and North Africa, and South Asia.
- Implementation details:
  - KPCA performed for each year from 1979 to 2021 to determine aggregate fragility score (a number between 1 and 100; 1 as least fragile and 100 as the most fragile); results ranked based on KPCA eigenvalues.
  - SVC with a Polynomial kernel used to classify countries based on fragility score; performance assessed with confusion matrix.
  - RNN implemented with Keras: three fully connected layers with 20, 10, and 6 neurons; optimizer = Adam; loss = sparse categorical cross entropy; activation functions = ReLU for first two layers and SoftMax for last layer.
  - LSTM implemented with Keras to estimate impact of each dimension on aggregate fragility and predict one year ahead: sequential LSTM model with 50 units, relu activation, kernel regularizer = l2; compiled with mean squared error loss and adam optimizer.
  - Visualization via plotly Dash interactive dashboard.
- Methodological contrast:
  - OECD method: linear PCA + qualitative expert-knowledge-based hierarchical clustering.
  - This study: nonlinear dimensionality reduction + unsupervised automated procedures (KPCA + SVC/RNN/LSTM).
  - Anticipated trade-offs: automated methods may better capture non-linearities and update more frequently; expert judgment may be more accurate in clustering stage.

### Key Results and Empirical Findings (selected examples)
- Syria:
  - Fragility jump in 2011–2012 linked to outbreak of civil war and rise of militant groups (e.g., ISIS).
  - Security fragility drove deterioration in aggregate fragility; relative improvements in economic and social fragility later reduced total fragility but levels remained high.
- Haiti vs Senegal:
  - Haiti experienced a fragility jump in 2010 (major earthquake); Senegal increased in 2009 (severe floods).
  - Environmental fragility spiked in those years for both countries.
- Cross-country distribution (2021):
  - Majority of countries exhibit low or limited fragility, but a notable club of countries suffer high fragility.
  - Political, economic and human fragility appear to affect larger numbers of countries (per box plot analysis).
- Environmental fragility (2021):
  - Most environmentally-fragile countries in 2021 included the Philippines, South Sudan and Haiti, with cited events: Super Typhoons Kiko and Odette (Philippines); historic flooding (South Sudan); M7.2 earthquake and Tropical Depression Grace (Haiti).
- Most fragile countries (2010–2021):
  - Between 2020 and 2021, South Sudan had the highest level of fragility for 4 years, followed by Somalia, Syria, and the Central African Republic for 2 years each.
- Temporal trends (2010–2021):
  - Box plots suggest a decrease in economic fragility across nations, though "additional evidence is required to support this claim."
  - Environmental fragility trends examined via box plots for 2010–2021.

### Case Study: South Sudan — Findings and Policy-Relevant Diagnostics
- Background and key facts:
  - Declared independence in 2011; characterized as "born fragile."
  - 2012 brief war with Sudan halted oil production temporarily, causing significant real GDP reduction.
  - 2013 civil war between President Kiir and Vice President Machar: killed hundreds of thousands, displaced 1.5 million internally, and sent over 2 million refugees to neighboring countries.
  - South Sudan ranked 164 out of 165 countries on progress toward United Nations Sustainable Development Goals.
- Macroeconomic engagement:
  - Since April 2021, reforms under an IMF Staff-Monitored Program (SMP) contributed to some improvements in macroeconomic stability.
  - South Sudan graduated from SMP in March 2023 and began a SMP with the board involvement (PMB) with the IMF.
- Identified drivers of fragility:
  - Overly centralized state with limited legitimacy and checks on the executive branch.
  - Rent extraction by political elite and vested interests.
  - Conflict over resources; large security sector/incomplete demobilization.
  - Weak public financial management; economy over-reliant on oil; large displaced population; increasing exposure to climate shocks.
- Empirical diagnostics from ML/AI analysis:
  - Political fragility has remained consistently high since inception; political fragility is a significant contributor to overall fragility.
  - Environmental shocks (flooding, drought) exacerbate fragility in certain years.
  - Economic fragility showed improvement since 2016–2017.
  - Strong correlations observed between security, societal, and political fragility and overall fragility; societal and political fragility strongly correlated with security fragility (heatmap for 2021).
  - Sub-indicators of political fragility (risk and coping/resilience-related) have remained elevated with no clear improvements, except for women's political empowerment.
- Policy implications for IMF’s South Sudan Country Engagement Strategy (CES):
  - Prioritize political and environmental fragility within CES to target root causes.
  - Support establishment of a stable and inclusive political environment and promote climate-resilient strategies.
  - Embed continuous monitoring using AI approaches in periodic program reviews to track correlations across fragility dimensions and target interventions.
  - Enhance coordination and information sharing among IMF, development partners, authorities, and civil society to harmonize efforts and avoid redundancies.
  - Economic recommendations: comprehensive economic and public finance reforms, investments in infrastructure, and improved agricultural techniques.
  - Governance recommendations: sustainable governance strategies that prioritize inclusivity, dialogue, and addressing the legacy political issues underpinning fragility.

### Summary and Policy Discussion (broader lessons)
- Contributions of the study:
  - Reinforces the necessity of a multidimensional, context-specific approach to state fragility.
  - Demonstrates utility of AI/ML techniques to capture complex, nonlinear relationships in fragility data and to improve reliability and accuracy of fragility assessments.
  - Shows benefits of unsupervised techniques (SVC, KPCA) in identifying dimensions and sub-indicators contributing to fragility that may be less apparent via expert judgment alone.
  - Highlights speed, cost-effectiveness, and adaptability benefits of automated ML/AI approaches for more frequent updating of fragility measures.
- Policy takeaways:
  - AI/ML-informed fragility analysis can inform targeted interventions and allocation of resources to dimensions with largest impact (e.g., political and environmental fragility in South Sudan).
  - Embedding AI-driven monitoring into regular engagement (e.g., periodic reviews) can make CES and similar strategies more adaptive and evidence-based.
  - Coordination and ongoing dialogue among international partners are critical to align interventions across multidimensional fragility drivers.

*Source: IMF Working Papers — How Nations Become Fragile: an AI-Augmented Bird’s-Eye View (excerpt).*

### References

### References

### State fragility, governance, and development
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- Acemoglu, D. and Robinson, J. A. (2021). Culture, Institutions and Social Equilibria: A Framework. (No. w28832). National Bureau of Economic Research.
- Akanbi, O. A., Gueorguiev, N., Honda, M. J., Mehta, P., Moriyama, M. K., Primus, K., and Sy, M. (2021). Avoid a Fall or Fly Again: Turning Points of State Fragility. Washington, D.C.: International Monetary Fund.
- Andrimihaja, N. A., Cinyabuguma, M. M., and Devarajan, S. (2011). Avoiding the Fragility Trap in Africa. World Bank Policy Research Working Paper, (5884).
- Besley, T. and Mueller, H. (2020). Revenue Mobilization and State Fragility. Macroeconomic Policy in Fragile States.
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- Carment, D., Samy, Y., and Prest, S. (2008). State Fragility and Implications for Aid Allocation: An Empirical Analysis. Conflict Management and Peace Science, 25(4), 349–373.
- Caselli, F. G., Presbitero, A. F., Chami, R., Espinoza, R., and Montiel, P. J. (2021). Aid Effectiveness in Fragile States. Macroeconomic Policy in Fragile States, 493–520.
- Collier, P. (2018). The Struggle for South Sudan: Challenges of Security and State Formation. Bloomsbury Publishing.
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- Collier, Paul. (2020). Governance. Operating in a Fragile Environment. IMF Internal Training.
- Collier, Paul. Transition Programs: A Theory of the Scaffolding Needed to Build out of Fragility', in Ralph Chami, Raphael Espinoza, and Peter J. Montiel (eds), Macroeconomic Policy in Fragile States (2021; online edition, Oxford Academic, 18 Feb. 2021), Oxford Academic.
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- International Monetary Fund, 2008, “The Fund’s Engagement in Fragile States and Post-Conflict Countries—A Review of Experience—Issues and Options”, Washington, D.C.: International Monetary Fund.
- International Monetary Fund, 2012, “Staff Guidance Note on the Fund’s Engagement with Countries in Fragile Situations,” Washington, D.C.: International Monetary Fund.
- International Monetary Fund, 2015, “IMF Engagement with Countries in Post-Conflict and Fragile Situations—Stocktaking,” Washington, D.C.: International Monetary Fund.
- International Monetary Fund, 2022, “The IMF Strategy for Fragile and Conflict-Affected States,” Washington, D.C.: International Monetary Fund.
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- International Growth Centre (IGC), (2018). Escaping the Fragility Trap. The LSE-Oxford Commission on State Fragility, Growth and Development.

### Machine learning, AI methods, and evaluation
- Athey, S. (2018). The Impact of Machine Learning on Economics. In the Economics of Artificial Intelligence: An Agenda (pp. 507–547). University of Chicago Press.
- Borg, M., Bronson, J., Christensson, L., Olsson, F., Lennartsson, O., Sonnsjö, E., ... and Karsberg, M. (2021, June). Exploring the Assessment List for Trustworthy AI in the Context of Advanced Driver-Assistance Systems. In 2021 IEEE/ACM 2nd International Workshop on Ethics in Software Engineering Research and Practice (SEthics) (pp. 5-12). IEEE.
- Boyer, Jerome. (2021), Evaluating and Selecting a Machine Learning Algorithm, IBM Gragae Methodology, www.ibm.com/garage/method/practices/reason/evaluate-and-select-machine-learning-algorithm. Accessed 16 July 2023.
- Chauhan, Nagesh Singh. (2020). Model Evaluation Metrics in Machine Learning––KDnuggets. KDnuggets, www.kdnuggets.com/model-evaluation-metrics-in-machine-learning.html.
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- Ghosh, Samadrita. “The Ultimate Guide to Evaluation and Selection of Models in Machine Learning”. Neptune.ai, 21 July 2022, neptune.ai/blog/ml-model-evaluation-and-selection.
- Iskhakov, F., Rust, J., and Schjerning, B. (2020). Machine Learning and Structural Econometrics: Contrasts and Synergies. The Econometrics Journal, 23(3), S81–S124.
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- Mankad, Sahil. “A Tour of Evaluation Metrics for Machine Learning.” Analytics Vidhya, 24 Nov. 2020, www.analyticsvidhya.com/blog/2020/11/a-tour-of-evaluation-metrics-for-machine-learning.
- Molina, M. and Garip, F. (2019). Machine Learning for Sociology. Annual Review of Sociology, 45, 27–45.
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### IMF publications, training, and macroeconomic policy in fragile states
- Chami, R. and Espinoza, R. (2021) Macroeconomic Policy in Fragile and Conflict-Affected States, IMF Internal Training.
- Chami, R., Espinoza, R., and Montiel, P. J. (Eds.). (2021). Macroeconomic Policy in Fragile States. Oxford University Press.
- Caselli, F. G., Presbitero, A. F., Chami, R., Espinoza, R., and Montiel, P. J. (2021). Aid Effectiveness in Fragile States. Macroeconomic Policy in Fragile States, 493–520.
- Diallo, O., Loris Gui-Diby, S., and Imam, P. A. (2022). Do Monetary Policy Outcomes Promote Stability in Fragile Settings?

### Other methodological and topical contributions
- Borg, M., Bronson, J., Christensson, L., Olsson, F., Lennartsson, O., Sonnsjö, E., ... and Karsberg, M. (2021, June). Exploring the Assessment List for Trustworthy AI in the Context of Advanced Driver-Assistance Systems. In 2021 IEEE/ACM 2nd International Workshop on Ethics in Software Engineering Research and Practice (SEthics) (pp. 5-12). IEEE.
- Ghosh, Samadrita. “The Ultimate Guide to Evaluation and Selection of Models in Machine Learning”. Neptune.ai, 21 July 2022, neptune.ai/blog/ml-model-evaluation-and-selection.
- Mankad, Sahil. “A Tour of Evaluation Metrics for Machine Learning.” Analytics Vidhya, 24 Nov. 2020, www.analyticsvidhya.com/blog/2020/11/a-tour-of-evaluation-metrics-for-machine-learning.

*How Nations Become Fragile: an AI-Augmented Bird’s-Eye View (With a Case Study of South Sudan) Working Paper No. WP/2023/167*

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_Source: https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023167-print-pdf.pdf_