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 in the same prioritization decision. We study five quantitative operators used in the D-POAF decision practice and stress-test them under uncertainty, estimator dependence, drift, shared context error, dependencies, and limited capacity. Robustness extensions use 30 deterministic seeds. Moderate scoring changes preserve broad order while materially changing funded-set membership. Under independent estimator errors, inverse-MSE aggregation reaches MAE 0.615, a 42.2% reduction relative to the best individual estimator. The gain falls to 4.4% at error correlation rho = 0.6 and becomes negative at rho = 0.8, while calibration-set regression remains slightly better than the best individual and approaches a test-set oracle. Under estimator drift, four-Wave adaptive weighting reduces average RMSE by 4.64% (95% CI [4.50%, 4.78%]). Model-collective divergence achieves ROC-AUC 0.908 with independent channels and remains above 0.878 with shared noise up to 0.8. We separate budget-constrained portfolio selection from ODP's native dependency-aware sequencing role. When ODP distance is repurposed as a portfolio-selection heuristic, value-to-effort is stronger in the tested generator. For a fixed selected set, ascending ERS/BVS is optimal for an ERS-weighted completion objective without dependencies and reaches mean efficiency 0.982 versus the exact precedence-constrained optimum at dependency intensity lambda = 1.0. Scaling to 100 Feature Blocks, ODP has the highest mean early-value AUC among the transparent sequencing baselines, with a small margin over PVS ordering. These results characterize both the validity region of evidence-weighted human-AI prioritization and the boundary between selection and sequencing objectives.