Error Propagation in Dynamic Programming: From Stochastic Control to American Option Pricing
The paper develops a rigorous framework for analyzing errors when using approximate dynamic programming to solve stochastic optimal control problems over discrete time. It decomposes total approximation error into regression, Monte Carlo sampling, and propagation components. This allows researchers to quantify how errors accumulate backward through the dynamic programming steps.