Enhancing intelligence source performance management through two-stage stochastic programming and machine learning techniques

The effectiveness of intelligence operations depends heavily on the reliability and performance of human intelligence (HUMINT) sources. Yet, source behavior is often unpredictable, deceptive or shaped by operational context, complicating resource allocation and tasking decisions.

This study developed a hybrid framework combining Machine Learning (ML) techniques and Two-Stage Stochastic Programming (TSSP) for HUMINT source performance management under uncertainty. A synthetic dataset reflecting HUMINT operational patterns was generated and used to train classification and regression models. The extreme Gradient Boosting (XGBoost) and Support Vector Machines (SVM) were applied for behavioral classification and prediction of reliability and deception scores. The predictive outputs were then transformed into scenario probabilities and integrated into the TSSP model to optimize task allocation under varying behavioral uncertainties.

The classifiers achieved 98% overall accuracy, with XGBoost exhibiting higher precision and SVM demonstrating superior recall for rare but operationally significant categories. The regression models achieved R-squared scores of 93% for reliability and 81% for deception. These predictive outputs were transformed into scenario probabilities for integration into the TSSP model, optimizing task allocation under varying behavioral risks. When compared to a deterministic optimization baseline, the hybrid framework delivered a 16.8% reduction in expected tasking costs and a 19.3% improvement in mission success rates.

The findings demonstrated that scenario-based probabilistic planning offers significant advantages over static heuristics in managing uncertainty in HUMINT operations. While the simulation results are promising, validation through field data is required before operational deployment.