The Value of Mechanistic Priors in Sequential Decision Making
The hybrid prior method improves sequential decision-making by incorporating mechanistic information from a model's physical structure. It quantifies this as mutual information between the model's recommendation and the true optimal policy. This approach reduces cumulative regret significantly compared to standard dosing methods and uninformed learners, offering theoretical guarantees for sample complexity reduction.