Simulator-generated counterfactual rollouts can effectively bootstrap forecasting models for new policies before real deployment data exists, and these models improve further with minimal real-world calibration.
When deploying a new decision policy, prediction models face a cold-start problem because historical data reflects old policies, not the new one. This paper uses simulation to generate counterfactual training data by rolling out the new policy in a simulator, then tests whether models trained on simulated data transfer to real-world inventory control.