AI-Driven Shock-Oriented Data Envelopment Analysis f or Assessing Economic Resilience in Short-Term Wars

Authors

  • Maryam Ghandehari * Department of Industrial Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran.
  • Mohsen Imeni Department of Accounting, Ayandegan University, Tonekabon, Iran. https://orcid.org/0000-0003-1863-4995

https://doi.org/10.48314/tsc.v1i3.53

Abstract

This study develops an AI-driven, shock-oriented Data Envelopment Analysis (DEA) framework to evaluate the economic resilience of Decision-Making Units (DMUs) under short-term wars. The research addresses the challenge of assessing real-time efficiency losses caused by abrupt, nonlinear shocks, which conventional methods often fail to capture. A comprehensive dataset encompassing key economic indicators, including labor, capital, energy, fiscal resources, logistical capacity, and sectoral outputs, was analyzed across pre-war, wartime, and immediate post-war phases. Artificial intelligence models, comprising autoencoders and Long Short-Term Memory (LSTM) networks, were applied to detect the magnitude, direction, and timing of instantaneous shocks, which were then integrated into a shock-adjusted DEA model. Efficiency scores revealed that output-constrained units suffered the most substantial performance declines, whereas input-constrained units were able to mitigate losses through adaptive resource reallocation. High baseline efficiency was associated with faster post-war recovery, emphasizing the importance of pre-conflict operational robustness. Sensitivity analysis demonstrated that even minor variations in shock intensity significantly affect resilience outcomes, underscoring the necessity for precise, high-frequency data monitoring. The proposed framework provides a quantitative, real-time tool for identifying vulnerabilities, guiding resource allocation, and monitoring recovery trajectories, offering actionable insights for policymakers and planners tasked with maintaining economic stability during short-term conflicts.

Keywords:

Adaptive capacity, Economic resilience, Shock-oriented data envelopment analysis, Short-term war, AI-based shock detection

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Published

2025-08-07

Issue

Vol. 1 No. 3 (2025): Transactions on Soft Computing

Section

Articles

How to Cite

Ghandehari, M., & Imeni, M. (2025). AI-Driven Shock-Oriented Data Envelopment Analysis f or Assessing Economic Resilience in Short-Term Wars. Transactions on Soft Computing , 1(3), 192-203. https://doi.org/10.48314/tsc.v1i3.53

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