Dynamic Adaptive Weighting of Effectiveness Assessment Indicators: Integrating G1, CRITIC and PIVW

Modern battlefields exhibit high dynamism, where traditional static weighting methods in combat effectiveness assessment fail to capture real-time changes in indicator values, leading to limited assessment accuracy—especially critical in scenarios like sudden electronic warfare or degraded command, where static weights cannot reflect the operational value decay or surge of key indicators. To address this issue, this study proposes a dynamic adaptive weighting method for evaluation indicators based on G1-CRITIC-PIVW. First, the G1 (Sequential Relationship Analysis Method) subjective weighting method—translates expert knowledge into indicator importance rankings—leverages expert knowledge to quantify the relative importance of indicators via sequential relationship ranking, while the CRITIC (Criteria Importance Through Intercriteria Correlation) objective weighting method—derives weights from data characteristics by integrating variability and inter-correlations—calculates weights by integrating indicator variability and inter-indicator correlations, ensuring data-driven objectivity. These two sets of weights are then fused using a deviation coefficient optimization model, minimizing the squared deviation from a reference weight and adjusting the fusion coefficient via Spearman’s rank correlation to resolve potential conflicts between subjective and objective judgments. Subsequently, the PIVW (Punishment-Incentive Variable Weight) theory—adapts weights to real-time indicator performance via penalty/incentive rules—is applied for dynamic adjustment. Scenario-specific penalty λ1 and incentive λ2 thresholds are set based on operational priorities and indicator volatility, penalizing indicators with values below λ1 and incentivizing those exceeding λ2 to reflect real-time indicator performance. Experimental validation was conducted using an Air Defense and Anti-Missile (ADAM) system effectiveness assessment framework, with data covering 7 indicators across 3 combat scenarios. Results show that compared to static weighting methods, the proposed method reduces MAE (Mean Absolute Error) by 15%–20% and weighted decision error rate by 84.2%, effectively reducing overestimation/underestimation of combat effectiveness in dynamic scenarios; compared to Entropy-TOPSIS, it lowers MAE by 12% while achieving a weighted Kendall’s τ consistency coefficient of 0.85, ensuring higher alignment with expert judgment. This method enhances the accuracy and scenario adaptability of effectiveness assessment, providing reliable decision support for dynamic battlefield environments.