Adaptive Cost-Benefit Analysis: A Reinforcement Learning Framework for Sequential Policy Decisions Under Deep Uncertainty

Building on Castaldo's (2022) work on infinite-horizon decision processes in aircraft engine control, this research treats cost-benefit analysis as a sequential decision problem rather than a one-time calculation. The framework models policy decisions as a Markov Decision Process where each decision point allows for learning and adaptation. It combines reinforcement learning algorithms with Bayesian updating to create an "adaptive CBA" that continuously revises cost-benefit estimates as real-world data arrives, addressing the limitation of current methods that cannot effectively incorporate new information after initial analysis. For example, in climate adaptation policy, the framework would allow for gradual scaling and adaptation of interventions as climate patterns evolve and more precise impact data becomes available, improving public spending efficiency. The methodology includes developing novel reward functions that incorporate both economic metrics and non-monetary benefits, using inverse reinforcement learning to learn policy preferences from historical data. This approach could revolutionize long-term policy planning under uncertainty.

References:

1. Method of Analysis for Impact of Input Uncertainty Error Propagation in a Highly Nonlinear System: Applied to Modern Aircraft Engine. Timothy Castaldo (2022). Volume 2: Coal, Biomass, Hydrogen, and Alternative Fuels; Controls, Diagnostics, and Instrumentation; Steam Turbine.