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Simulating the Linkages Between Economy and Armed Conflict in India With a Long Short‐Term Memory Algorithm

Author

Listed:
  • Mengmeng Hao
  • Jingying Fu
  • Dong Jiang
  • Fangyu Ding
  • Shuai Chen

Abstract

This article analyzes the linkages between the economy and armed conflict in India using annual frequency data for the period 1989–2016, the maximum time period for which consistent data are available for the country. An adequate set of economic indicators was established to fully reflect the economic condition. Long short‐term memory (LSTM), which is a machine‐learning algorithm for time series, was employed to simulate the relationship between the economy and armed conflict events. In addition, LSTM was applied to predict the trend of armed conflict with two strategies: multiyear predictions and yearly predictions. The results show that both strategies can adequately simulate the relationship between the economy and armed conflict, with all simulation accuracies above 90%. The accuracy of the yearly prediction is higher than that of the multiyear prediction. Theoretically, the future state and trend of armed conflict can be predicted with LSTM and future economic data if future economic data can be predicted.

Suggested Citation

  • Mengmeng Hao & Jingying Fu & Dong Jiang & Fangyu Ding & Shuai Chen, 2020. "Simulating the Linkages Between Economy and Armed Conflict in India With a Long Short‐Term Memory Algorithm," Risk Analysis, John Wiley & Sons, vol. 40(6), pages 1139-1150, June.
    • Handle: RePEc:wly:riskan:v:40:y:2020:i:6:p:1139-1150
    DOI: 10.1111/risa.13470
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    References listed on IDEAS

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    1. Shihai Wu & Yili Zhang & Jianzhong Yan, 2022. "Comprehensive Assessment of Geopolitical Risk in the Himalayan Region Based on the Grid Scale," Sustainability, MDPI, vol. 14(15), pages 1-20, August.

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