Exploration and Experimentation with a Misspecified Simulator

The paper investigates how to collect data for recalibrating a misspecified simulator that models a partially observed real-world system as a fully observed Markovian process. It compares four data‑collection strategies: never recalibrating, passive on‑policy recalibration, continuous exploration across all units, and randomized experimentation on a sample of units. Theoretical analysis and a vending‑machine simulation show that experimentation can outperform passive adaptation when drift is invisible to on‑policy data or when recalibration lags behind ongoing drift, while exploration is preferable when trying worse actions is costly and dynamics may not have changed.

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