Inverse Behavioral Optimization of QALY-Based Incentive Systems: Quantifying the System Impact of Adaptive Health Programs

Battle Count: 59

The paper is primarily focused on health economics and policy design rather than financial markets or trading. However, it shares methodological connections with quantitative finance: (1) inverse optimization and behavioral parameter recovery parallels factor model estimation; (2) the fairness-efficiency trade-off conceptually mirrors risk-return trade-offs; (3) temporal responsiveness and adaptive learning relate to regime-switching and online learning in trading; (4) the FOSSIL regret-minimizing framework has connections to portfolio optimization under uncertainty; (5) Monte Carlo sensitivity analysis methodology is transferable. The paper is categorized under q-fin.MF but its direct applicability to trading strategies is minimal. The behavioral optimization framework could theoretically be adapted for incentive design in market microstructure or algorithmic trading governance, but this is not explored.

The theoretical framework involves multi-layer optimization (forward utility maximization, inverse parameter recovery with Bayesian regularization, system impact propagation) with convexity and stability proofs. However, the empirical implementation is simplified to reduced-form OLS and AR(1) calibration. The simulation component (N interacting regional units with behavioral propagation rules) is moderate in complexity. Full structural inverse optimization with FOSSIL weighting would be significantly more complex. The paper provides reproducible Python code, reducing practical implementation barriers. Key challenges include: proper Bayesian prior specification, ensuring identifiability conditions (A1-A3), handling heterogeneous policy environments, and scaling to realistic multi-agent systems.

The paper provides a Zenodo repository (https://zenodo.org/records/17439497) with all preprocessed data, configuration scripts, and simulation codes. All simulations implemented in Python 3.10 using NumPy, Pandas, and Matplotlib. Random seeds and hyperparameter schedules are fixed. Monte Carlo experiments use 20 replications. However, the FOSSIL framework itself is referenced as a separate preprint (arXiv:2509.13218) under review, and some theoretical proofs are deferred to Appendix A. The empirical calibration uses reduced-form OLS rather than the full structural inverse optimization described theoretically.