Cognitive AI-Based Data Envelopment Analysis for Modeling Economic Collapse and Adaptive Responses in Modern Warfare

Modern warfare exerts profound and heterogeneous impacts on national and sectoral economies, often triggering complex patterns of collapse or adaptation that traditional efficiency analysis fails to capture. This study introduces a Cognitive AI-Based Data Envelopment Analysis (DEA) framework to evaluate economic resilience under extreme stress by integrating classical DEA, cognitive behavioral indicators, and artificial intelligence-based pattern recognition. The proposed methodology adjusts conventional efficiency scores using a cognitive coefficient derived from decision-making quality, risk perception, and adaptive policy responses, and applies AI-driven clustering and temporal trajectory analysis to identify latent structures of resilience and collapse. The framework was applied to a sample of simulated economic units, revealing that technically efficient economies may nonetheless be vulnerable if cognitive adaptability is low, whereas moderately efficient economies with high behavioral resilience can maintain stability under conflict. AI-based clustering effectively distinguished adaptive and collapse-prone units, while temporal and Markov chain analyses provided predictive insights into the dynamics of recovery and failure. These findings demonstrate that behavioral and cognitive factors are critical determinants of economic adaptation, extending the explanatory power of traditional DEA. The results offer practical guidance for policymakers aiming to enhance resilience, prioritize adaptive capacity, and design preemptive interventions in conflict-affected contexts. By integrating technical efficiency with cognitive adaptability and predictive modeling, this framework represents a novel, multidimensional tool for understanding and managing economic resilience under modern warfare.

کلیدواژه ها:

Adaptive capacity ، Cognitive DEA ، Economic resilience ، Machine learning ، Modern warfare