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Simulating the Linkages Between Economy and Armed Conflict in India With a Long Short-Term Memory Algorithm
Mengmeng Hao1,2, Jingying Fu1,2, Dong Jiang1,2,3
1State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Summary
This study uses machine learning to analyze India
Area of Science:
- Economics
- Political Science
- Computer Science
Background:
- Economic conditions significantly influence armed conflict.
- Predicting conflict trends is crucial for policy and security.
Purpose of the Study:
- To analyze the relationship between economic indicators and armed conflict in India.
- To apply machine learning for simulating and predicting armed conflict trends.
Main Methods:
- Utilized annual economic data from 1989-2016 for India.
- Employed Long Short-Term Memory (LSTM), a machine learning algorithm, for time series analysis.
- Implemented LSTM for both multiyear and yearly conflict prediction strategies.
Main Results:
- LSTM effectively simulated the economy-armed conflict relationship with over 90% accuracy.
- Yearly conflict predictions demonstrated higher accuracy than multiyear predictions.
- The model's predictive power relies on the availability of future economic data.
Conclusions:
- Machine learning, specifically LSTM, offers a robust tool for understanding and forecasting armed conflict.
- Accurate economic forecasting is key to improving future conflict predictions.
- This approach provides valuable insights for policymakers addressing socio-economic drivers of conflict.
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