Governed Human-AI Prioritization Under Uncertainty: Adaptive Estimation and Dependency-Constrained Portfolio Selection

Abstract

AI-native software engineering increasingly combines human judgment, historical analogy, parametric estimation, and AI-generated forecasts inside the same prioritization decision. The resulting problem is not merely how to rank candidate work, but how to govern heterogeneous estimates, uncertainty, strategic parameters, dependencies, and limited capacity in a way that remains inspectable and recalibratable. We study five quantitative operators used in the D-POAF decision practice: Business Value Score (BVS), Effort and Risk Score (ERS), Prioritization Value Score (PVS), Collective Calibration Score (CCS), and Optimal Development Path (ODP). Controlled synthetic experiments characterize their behavior under known latent variables and explicit error processes. Moderate BVS-weight perturbations preserved global rankings (median Spearman 0.986), while broader strategic changes reduced top-10% overlap to 0.788. Reliability-weighted effort aggregation achieved MAE 0.616, a 42.6% reduction relative to the best individual estimator (MAE 1.073), and outperformed a simple mean (MAE 0.638). Under estimator drift, adaptive reliability weighting reduced average RMSE by 4.5% relative to equal weighting. Model-collective divergence detected the highest-error priority estimates with ROC-AUC 0.906. In 800 dependency-constrained portfolio instances, value-to-effort achieved mean objective ratio 0.962 and ODP distance 0.953 against the exact optimum; bootstrap intervals confirm a small but systematic advantage for value-to-effort under the declared objective. These results establish a quantitative basis for evidence-weighted human-AI prioritization and define the optimization boundary between shortest-path ODP formulations and general release-portfolio selection.